Student assignments (bachelor and master)

Educational program: Biomedical Engineering, Electrical Engineering, Computer Science, Mathematics or other relevant direction.

Daily supervisor: ir. MSc. Juan Delgado Teran (j.d.delgadoteran@utwente.nl)

Principal investigator: Ciska Heida (t.heida@utwente.nl)

Project background:

In the INTENSE project, we aim to better understand and diagnose one of the most disabling symptoms of Parkinson’s disease – freezing-of-gait (FOG) https://youtu.be/3-wrNhyVTNE, in order to provide personalized care using cueing. External cueing consists of the application of (often rhythmic) spatial or temporal stimuli, for example, the beat of a metronome or a series of stripes on the floor, to improve the performance of movements such as gait, or to help initiate movements.

In this part of project, participants with Parkinson's disease were equipped with wearable sensors for a 7-day home data collection. We aim to address the specific challenges posed by unsynchronized wearable sensor data collected from the homes of individuals with Parkinson's disease. The diverse nature of the sensors introduces complexities in aligning and interpreting the data accurately. This project represents a unique opportunity to contribute to the advancement of technology for Parkinson's disease management by developing methods for data synchronization and leveraging unsupervised or semi-supervised learning techniques for Freezing of Gait detection.

What you can expect from us:

In this project, you can expect to obtain the knowledge and experience about:

·        Developing and implementing a synchronization mechanism. This includes developing strategies for timestamping data from the pressure sensors (Moticon), and smartwatch (Empatica) to guarantee alignment across devices.

·        Exposure to real-world applications of data synchronization and analysis for healthcare purposes.

·        Opportunities to enhance skills in unsupervised and semi-supervised learning techniques

·        on synchronized data to detect and/or predict freezing-of-gait episodes, providing a more comprehensive understanding of FOG in a real-world context.

·        Gain insights into Parkinson's Disease and its specific features related to freezing-of-gait through hands-on experience with synchronized, real-world data.

What we expect from you:

·        Ability to independently address challenges related to data synchronization and unsupervised learning techniques.

·        Proficiency in programming languages, particularly Python, for the implementation of synchronization algorithms and data analysis.

·        Analytical Skills:

·        Strong analytical skills to conduct exploratory data analysis and interpret results effectively.

·        Clear and concise communication of findings and progress in regular updates and presentations.

·        Willingness to collaborate with other team members and adapt to evolving project requirements.

Information and application:

Please send your application to: ir. MSc. Juan Delgado Teran (j.d.delgadoteran@utwente.nl) and include:

·        A curriculum vitae including your name and contact (max 2 A4 pages).

·        A personal motivational letter (max 1 A4 page)

·        List of courses and grades of your BSc and MSc degrees.

Assignment: Master

Educational program: BME, EE or other relevant program

Start: 1^st or 2^nd quartile 2023-2024

Contact person: Robbert van Middelaar r.p.vanmiddelaar@utwente.nl, Frank Wouda f.j.wouda@utwente.nl

Topics

Running, sports science, biomechanics, modelling, machine learning, sensors

Project description

Running is related to a lot of injuries, where most of them occur at the knee and ankle. Our running research focuses on the prevention of running injuries, but also trying to enhance running performance. Measuring the movement of the runner (kinematics) or forces (kinetics) can give us insight into these potential injuries or performance enhancement. Normally, this can be done with an extensive measurement setup which is restricted to the lab, consisting of marker data and force plates. In our research, we try to estimate the kinematics and kinetics with inertial sensors, which are easy-to-use sensors which can also be used outdoors. We try to model the running kinematics and kinetics with as less inertial sensors as possible, as well indoors as outdoors.

The foot is a complex segment, with the multidimensional ankle joint but also multiple segments of the foot (the heel, toes). The goal of this assignment is create a (3D) model of the foot, measuring kinematics and kinetics as accurately as possible with inertial sensors. Think about the orientations of the multiple segments in the foot, but also, for example, an estimate of the Centre of Pressure. Do we need multiple sensors or is one sufficient?

Advised background knowledge

Biomechanics of human movement, biomechatronics, machine learning or related topics
MATLAB/Python experience

Topic: Estimating the kinematics of a simplified body model using a minimal set of IMUs

Contact person: Junhao Zhang (j.zhang-7@utwente.nl)

Education program: Master BME, EE, or other relevant programs

Start: 1^st or 2^nd quartile 2023-2024

Project supervisors: Prof. Dr. Ir. P.H. Veltink (BSS-EEMCS, UTwente), MEng. Junhao Zhang (BSS-EEMCS, UTwente), Dr. E.H.F van Asseldonk (BE-ET, UTwente)

Keywords: Biomechanics, sensor fusion, inertial sensors, modelling, balance assessment

Project description

Assessing the state of balance during gait is an important aspect of understanding the functional abilities and physical limitations of an individual. The development of inertial measurement unit (IMU) enables IMU-based motion capture systems to provide an economical, efficient, and easy-to-use method for collecting kinematics data that is necessary for assessing the state of balance. However, most commercial IMU-based motion capture systems usually include more than 10 IMUs that must be affixed to the person, which makes them impractical and inconvenient for daily life use. One possible solution is reducing the set of IMUs. However, doing so implies that not all body segments can be tracked, thus a simplified body model with a reduced number of segments should be used. According to our previous findings, a seven-seg model could be sufficient, including an HT (head&torso) segment and lower limb segments.

In our previous work, Irfan et al. [1] proposed a method to estimate the relative positions of feet and CoM using only three IMUs, comprising one IMU around the sacrum and two IMUs on each foot. In this assignment, students may consider the inclusion of additional IMUs, such as one at the trunk, to facilitate the estimation of the kinematic quantities of the trunk. The remaining question is whether the kinematics of all lower limbs can adequately be estimated from IMUs on the sacrum and on the feet or shanks, taking into account the kinematic chain between both the sensor locations formed by the leg and foot segments. Therefore, the goal of this project is to estimate the kinematics of all segments of this simplified body model using a minimal set of IMU and determine the optimal number of IMUs, by extending Irfan’s previous work.

Advised background knowledge

Biomechanics of human movement, biomechatronics, sensor fusion, Kalman filtering, or related topics, MATLAB/Python experience

Information and application: Interested students are encouraged to send an e-mail to Junhao Zhang, (j.zhang-7@utwente.nl) with their motivation, interests, and course list.

Reference:

[1] M. I. M. Refai, B. J. F. van Beijnum, J. H. Buurke, and P. H. Veltink, "Portable Gait Lab: Tracking Relative Distances of Feet and CoM Using Three IMUs," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 10, pp. 2255-2264, 20

Educational programs: Biomedical engineering, electrical engineering, technical medicine, or other relevant directions.

Supervision team: Carlijn Braem (BSS-EEMCS, c.i.r.braem@utwente.nl), Utku Yavuz (BSS-EEMCS, s.u.yavuz@utwente.nl)

Background

Chronic diseases are the leading cause of mortality and morbidity worldwide, accounting for 70% of deaths and 54.6% of disability-adjusted life-years. Unhealthy lifestyle behaviours are the major risk factors of chronic diseases, like cardiovascular disease, diabetes, and obesity. A healthy lifestyle on the other hand is a way of living that lowers the risk of being seriously ill or dying early.

Changes to a healthy lifestyle will improve life expectancy and quality of life in people with chronic disease and it even prevents the onset of chronic diseases. However, changing lifestyle habits can be challenging. Daily monitoring of lifestyle behaviour with wearable sensors could help people gain insight in their lifestyle and with that, help them improve their lifestyle. New wearable sensors for daily lifestyle monitoring can measure more parameters than current consumer wearables, but we do not know how these parameters are related to lifestyle.

That is why we started a study where we collect lifestyle in healthy people data with a wearable sensor, questionnaires, and daily questionnaires (EMA). In this study we use the IMEC Chill+ wristband, a research device that measures galvanic skin response (GSR), heart rate (HR) and acceleration (ACC). The sensors result in 80+ parameters which are related to stress, sleep, and physical activity. The (daily) questionnaires provide us with contextual data and will help to determine which parameters are important.

What you can expect from us:

In this project you will investigate the relationship between parameters from the IMEC Chill+ wristband and lifestyle. For this you will develop algorithms and learn to preprocess different signals. A potential scientific publication can be expected based on the project outcome. In this project you will also be involved in the inclusion of research subjects and practical implementation of the research protocol.

What we expect from you:

We are looking for a master student who is interested in data analysis and health monitoring with wearable sensors. It is therefore recommended that you have followed ‘Advanced Techniques for Signal Analysis’. You can immediately start with data analysis, as subject inclusion has already started.

Information and application:

If you are interested or want to know more about this research project you can send an email to Carlijn Braem: c.i.r.braem@utwente.nl.

Master graduation project (BSS-UTwente +Nokia Bell Labs Cambridge-UK):

Educational program: Embedded System, Electrical Engineering, or other relevant direction

Supervision team:

Dr. Ying Wang (BSS-EEMC, UTwente) and Dr. Hongwei Li (Nokia Bell Labs Cambridge-UK)

Project background:

Noncommunicable diseases, including cardiovascular diseases (CVDs), diabetes, and mental disorders, are the number one cause of death and disability in the world. The prevalent cases of noncommunicable disease have continued its decades-long rise, and the global burden of the diseases, e.g. premature mortality and the loss of quality of life, will dramatically increase given a boost in aging population with unhealthy lifestyle. The daily monitoring of noncommunicable diseases can help clinicians provide timely interventions; hence, this can mitigate socioeconomic burden and increase patients’ quality of life. However, the performance of current wearable sensing techniques and monitoring techniques is still lacking behind despite the growing understanding of disease pathophysiology and treatment in the advanced stage of diseases.

Collaborating between BSS-UTwente and NokiaBell Labs, we aim at developing advanced wearable sensing technology and health-condition monitoring technology to tackle the challenges mentioned above. A wearable health device “Patchkeeper” developed by NokiaBell Labs contains an electronic stethoscope, PPG, ECG and IMU sensors to measure multimodal signals from human or animal subjects. The acquired multimodal signals are analysed for the disease monitoring in the daily life.

What you can expect from us:

In this project, you can expect to obtain the knowledge and experience about:

• Apply embedded computer architecture, electronics, embedded software optimization techniques in the development of Patchkeeper.
• Develop algorithms for noncommunicable disease monitoring using signal analysis and machine learning techniques:
• Preprocess different modal signals. Extract features from the multimodal signals. Apply machine learning techniques to identify the health condition of human subjects.
• Design and implement a polit experiment with healthy subjects to collect relevant data.
• Opportunities for visiting Nokia Bell Labs in Cambridge, UK for the Patchkeeper development.
• A potential scientific publication can be expected based on the project outcomes.

 What we expect from you:

• We are looking for open-minded students who like to challenge themselves.
• Students should have strong skills in embedded system design, advanced signal analysis, and machine learning techniques.
• Students should have strong programming skills in Python and C language.
• Interests in physiological signal analysis and relevant research.

Information and application:

Please send your application to dr. Ying Wang (ying.wang@utwente.nl), and include:

• A curriculum vitae including your name and contact (max 2 A4 pages).
• A personal motivation letter (max 1 A4 page).
• Grade list of your BSc and MSc courses.

Master graduation project or internship (exceptions possible for Bachelor students with necessary skills)

Educational programs: Biomedical Engineering, Electrical Engineering, or other relevant studies.

Supervision team: Dr. Bettina Schwab (BSS-EEMC, UTwente), Msc. Silvana Huertas Penen (BSS-EEMC, UTwente)

Topics: Non-invasive brain stimulation, Computer simulations using the Finite element method (FEM)

Introduction: We are interested in optimizing stimulation montages for a non-invasive brain stimulation technique, transcranial alternating current stimulation (tACS).

•  For this optimization, Electric field distributions across different conditions have to be compared.
•  You will run multiple electric field simulations of tACS using the software SimNIBS in Matlab (See example below) and analyze the results.
•  If you finish earlier than attempted, you can aim for additional, advanced analyses.

Required pre-knowledge: We are looking for open-minded students who like to challenge themselves and have a background in electrical engineering, biomedical engineering, or related fields.

• Students should have a strong interest in performing FEM computer simulations
• Students should have strong programming skills in Matlab.
• Experience in the physiological signal analysis is optimal but not mandatory. 

Information and application: Interested students are encouraged to send an e-mail to Silvana Huertas Penen, (s.huertaspenen@utwente.nl)with their interests and course list.

Student assignments: BSc and MSc project assignments

Educational programme: BMT/BME, TG/TM, or similar

Contact person: Sigert Mevissen

Email contact: s.j.mevissen-1@utwente.nl

Project supervisors: Bert-Jan van Beijnum & Sigert Mevissen

Topic: Motion analysis for predicting graft detachment in DMEK cornea transplantation patients.

This research project of BSS and the department of ophthalmology at Deventer Hospital uses an IMU to investigate the relationship between graft detachment and the extent to which patients can adopt a calm flat posture after Descemet Membrane Endothelial Keratoplasty (DMEK) corneal transplantation.

1500 corneal transplants are performed annually in the Netherlands, of which more than 100 take place at Deventer Hospital. In a DMEK operation, the endothelial and descemet layers of the patient's cornea are replaced with the same layers of functioning donor tissue. In a DMEK, the graft is not sutured but pushed into place by an air or gas bubble. Because air rises, patients are instructed to lie flat as much as possible for the first 24 hours after surgery, so that the largest area of the bubble can push the graft against the patient's own cornea. A major postoperative complication of this surgery is graft detachment, after which a new air bubble is added or a new graft is put in place. Patients undergoing DMEK are mostly older than 65 years, and strict flat bed rest is quite a challenge for them. It is therefore important to investigate to what extent graft detachment can be predicted by looking at the patient's movements and orientation. The patient will have an IMU attached to their head for the first 24 hours after surgery to capture movements.

Assignment:

Initially, the aim is to find a general relationship between the degree of movement and graft detachment. This will then be to investigate which specific postures and movements affect the release and to what extent, so that calm flat posture instruction can be adjusted to alleviate the burden on the patient. For this, the location of the graft detachment will be taken into account.

Elements to expect in this assignment:

• Signal processing to analyze accelerometer and gyroscope data and convert it into useful information. 
• Motion analysis to construct parameters representing degree of motion. 
• Develop activity recognition (possibly machine learning) methods to determine how often specific movements and postures are adopted. 
• Perform measurements on DMEK patients in the ophthalmology department of Deventer hospital 

Assignment:                  Bachelor, Master, Internship

Educational program:   BME, TM, HS, PSY, CREATE

Daily supervisor:           Kim Wijlens, MSc or ir. Lian Beenhakker

Project description

Due to improved treatment and early diagnosis of breast cancer, there are more survivors. However, this also means that more patients are struggling from the late effects of treatment. One of these late effects is cancer-related fatigue (CRF), which is defined by the American National Comprehensive Cancer Network as “a distressing, persistent, subjective sense of physical, emotional and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning.”

In the Personalized cAnceR TreatmeNt and caRe (PARTNR) project, we aim to help breast cancer patients suffering from CRF. We will holistically assess patients, considering all aspects that can cause/influence the fatigue and predict who might be at risk of developing CRF. Using multimodal data, we will advise an intervention to reduce the fatigue which is based on the holistically assessed patient information about CRF and personal preferences patients might have for types of intervention. In the end, we will combine all information from patients into an intelligent self-learning platform that patients can use to keep track of and improve their fatigue.

Open assignment
Dysregulation of activity and sleep are perpetuating factors of cancer-related fatigue. The relation between physical activity behaviour and fatigue suggests that cancer survivors might be performing too much activity in the morning. This results in increased fatigue levels, which in turn result in a relapse in activity from the afternoon going into the evening. Patients’ activity can be monitored with a wearable, mobile phone apps or questionnaires. Currently, we assess activity via subjective questionnaires. Wearables or mobile phone data could provide continuous information about the patients’ activities. Wearables like Garmin or Fitbit have a protected algorithm, via the dashboard the variable outcomes are available and via the developer site the raw data can be obtained. However, the variable outcomes are subject to unknown changes due to the algorithm updates. This is a challenge in research since updates can occur during the studies and within patients. An open source platform as (https://www.beiwe.org/technology/#beiwe) can be used to retrieve raw phone data. So the question is from what source (questionnaires, wearables or raw phone data) should activity data be used to for a personalised treatment advice based on activity level. In addition, the frequency of available data differs between assessment via questionnaires and continues information via wearables or phone data. So, how can the variables of continues sources be adapted into output that are comparable to questionnaire information without content loss, to use as information to provide the patient with relevant personalized treatment advice?

The PARTNR project is a collaboration between the University of Twente, Ziekenhuis groep Twente (ZGT), Helen Dowling Institute (HDI), Roessingh Rehabilitation Centre, University Medical Center Groningen (UMCG), Dutch breast cancer association (BVN), Netherlands comprehensive cancer organisation (IKNL), Ivido and Evidencio.

If you would like to work on the PARTNR project during your internship or thesis, please contact either Kim Wijlens (k.a.e.wijlens@utwente.nl) or Lian Beenhakker (l.beenhakker@utwente.nl). Supervisors will depend on the chosen assignment.

PARTNR Team:

dr. Annemieke Witteveen (BSS), prof. dr. Sabine Siesling (HTSR), dr. Christina Bode (PGT), 

Supervisors: 

Bert-Jan van Beijnum (UT) (b.j.f.vanbeijnum@utwente.nl)

Jaap Buurke (RRD, UT) (j.buurke@rrd.nl) / (j.h.buurke@utwente.nl)

Type of assignments:

BSc thesis, MSc thesis, internship

Background student:

Biomedical Engineering, Movement Science, Technical Medicine, Biomechanics, or other relevant direction.

Project description:

The validation of an ambulatory three IMU setup for measuring balance parameters in adults with an asymmetric gait.

People with an asymmetric gait, for example as a result of hemiparesis caused by a stroke, or due to an amputation, experience trouble in keeping their balance. As a result of this, they need help with daily life activities and they often fall. Clinical therapy is therefore focused on improving mobility and functional capacity. However, there is a lack of objective information about the rehabilitation progress when the patient is back home. [1] In order to compare the gait and balance parameters during clinical training and performance in the home setting, a wearable and unobtrusive system is needed.

Balance is related to the position of the CoM (centre of mass) with respect to the position of the feet (i.e. base of support) [2]. Tracking foot and CoM positions is feasible with an IMU only setup, where the IMUs are placed on the segments of interest. However, as no information about relative distances are known, and due to strapdown integration, the positions measured by the IMUs drift away from each other. Current state of the art systems such as the ForceShoes™ use an ultrasound system to reduce this drift. [3] Other alternatives include measuring every segment using the Xsens suit and applying biomechanical constraints. [4,5] However, as the ForceShoes™ are bulky to be used in daily life (weight: 1 kg each), and the Xsens suit is extensive, alternatives are required.

In a previous study a minimal IMU setup for ambulatory sensing of foot and CoM positions in overground variable gait is validated in healthy (symmetric gait) adults, with the ForceShoes™ and the VICON^© motion capture system as a reference. [6] In order to reduce the drift between the IMU positions, this setup makes use of the Centroidal Moment Pivot (CMP)-theory.

Aim:

The goal of the current assignment is to validate this method  for patients with an asymmetric gait. This is important, since the effect of the assumptions made in the CMP-theory could be different if one has an asymmetric gait. For this, ~7 CVA-patients need to be measured, according to a pre-defined protocol. The measurements will be performed in the gait lab at Roessingh Research and Development, Enschede, after which you will post-process and analyze the data. There is already ethical approval for the study, so you can almost directly start measuring the patients!

Start time:

Whenever you want, preferably as soon as possible.

Note: it is also possible (but not mandatory) to continue during the summer holiday.

References

[1] B. Klaassen et al., “A Fully Body Sensing System for Monitoring Stroke Patients in a Home Environment”, Communications in Computer and Information Science, vol. 511, pp. 378-393, 2016.

[2] A.L. Hof, M.G.J. Gezendam, W.E. Sinke, “The condition for dynamic stability”, Journal of Biomechanics, vol. 38, no. 1, pp. 1-8, 2005.

[3] D. Weenk, D. Roetenberg, B.-J. van Beijnum, H.J. Hermens, P.H. Veltink, “Ambulatory Estimation of Relative Foot Positions by Fusing Ultrasound and Inertial Sensor Data”, IEEE Transactions on neural systems and rehabilitation engineering, vol. 23, no. 5, pp. 817-826, 2015.

[4] D. Roetenberg, H. Luinge, P. Slycke, “Xsens MVN: Full 6DOF Human Motion Tracking Using Miniature Inertial Sensors”, Xsens Technologies, Enschede, The Netherlands, version April 3, 2013.

[5] H. Zhoa et al., “Heading Drift Reduction for Foot-Mounted Inertial Navigation System via Multi-Sensor fustion and Dual-Gait Analysis”, IEEE Sensors Journal, vol. 19, no. 19, pp. 8514-8521, 2019.

[6] M.I.M.Refai, B.-J. F. van Beijnum, J.H. Buurke, P.H. Veltink, “Portable Gait Lab: Tracking Relative Distances of Feet and CoM Using Three IMUs”, IEEE Transactions on neural systems and rehabilitation engineering, vol. 28, no. 10, 2020.

Student assignment: Master/Bachelor BMT, EE, related

Supervisor: Juan Delgado Teran (j.d.delgadoteran@utwente.nl)

Principal investigator: Ciska Heida (t.heida@utwente.nl)

Topics: signal analysis, Parkinson’s disease, movement sensing, physiological sensing, daily monitoring, e-health, machine/deep learning

Introduction: In the PROMPT and INTENSE projects, we aim to better understand and diagnose one of the most disabling symptoms of Parkinson’s disease – freezing-of-gait (FOG) https://youtu.be/3-wrNhyVTNE, in order to provide personalized care using cueing. External cueing consists of the application of (often rhythmic) spatial or temporal stimuli, for example, the beat of a metronome or a series of stripes on the floor, to improve the performance of movements such as gait, or to help initiate movements

To observe patients in a semi-daily living environment we use multiple sensors to record the patient’s behaviour during half a day in the eHealth House (TechMed Centre). During data collection, various wearable sensors are attached to the person with Parkinson’s Disease including motion sensors (Xsens suit), pressure sensors (Moticon) and a smartwatch (Empatica). The subject is asked to perform everyday tasks (e.g. cooking, cleaning, etc.) in the eHealth House, as well as take a 10-minute walk outside with and without medication. In the eHealth house. Videos are taken of the subject moving around in the eHealth House, in order to label freezing episodes. The labelled video data is then utilized to train classification algorithms to detect freezing-of-gait based on sensor data.

In this project, you can expect to obtain the knowledge and experience about:

•  Applying machine learning techniques on different types of data to detect and/or predict FOG   episodes.
•  Use of signal analysis techniques to pre-process and clean the data from artefacts.
•  Learning about Parkinson's Disease and features about FOG.
•  Possibly becoming a co-author of a publication depending on the results.

What we expect from you:

• Proactive and an open-minded attitude to new challenges
• Strong interest in advanced signal analysis and/or Machine Learning/Deep learning
• Experience with movement sensors would be ideal but is not required.

For more information contact Juan Delgado Terán (j.d.delgadoteran@utwente.nl)

Master graduation project (exceptions possible for internships and Bachelor students)

Educational programs: Biomedical Engineering, Electrical Engineering, (M3 for Technical Medicine), or other relevant studies.

Supervision team: Dr. Ciska Heida (BSS-EEMC, UTwente), Dr. Bettina Schwab (BSS-EEMC, UTwente), Msc. Silvana Huertas Penen (BSS-EEMC, UTwente)

Topics: Transcranial alternating current stimulation (tACS), neuromodulation, EEG data analysis.

Introduction: We are working on researching how transcranial alternating current stimulation (tACS) modulates brain activity/connectivity. The project will include: acquisition of EEG data in healthy participants during tACS and analysis of EEG data. Projects can be adapted to personal interests and skills.

Example assignments:

1. Acquisition of EEG data with tACS + analysis of brain activity using EEG data

 2. Acquisition of EEG data with tACS+ analysis of functional connectivity (EEG data)

Required pre-knowledge:

1. We are looking for open-minded students who like to challenge themselves and have a background in electrical engineering, biomedical engineering, or related fields.

2. Students should have strong interest in advanced signal analysis techniques.

 3. Students should have strong programming skills in Matlab.

4. Experience in physiological signal analysis andclinical research is optimal but not mandatory.

Information and application:

Motivated students are encouraged to send an e-mail to Silvana Huertas Penen (s.huertaspenen@utwente.nl) with their interests and course list.

Master graduation project (BSS+CRPH) 

Educational program: Embedded Systems, Electrical Engineering, Biomedical Engineering, or other relevant direction

Supervision team:

Dr. Ying Wang (BSS-EEMC, UTwente) and Dr. Eline Mos-Oppersma  (CRPH-TNW, UTwente)

Project background:

The diaphragm represents the major muscle of the so called ‘human respiratory pump’, which allows us to breath several times every minute of every day. As all skeletal muscles, the diaphragm and accessory respiratory muscles are composed of multiple groups of muscle fibers and innervated by motor neurons. Activation of the motor neurons results in the propagation of action potentials and the activation of the muscle fibers. This diaphragmatic wave of electrical activation can be recorded using dedicated electrodes to obtain electromyograms (EMG). When the respiratory pump fails and a patient needs mechanical support of breathing at the Intensive Care, it is essential to monitor diaphragmatic activity, both to prevent further failure and to optimize treatment.

A novel easy-to-use and noninvasive approach is to measure the electromyogram (EMG) via electrodes attached to the skin. Yet, analysis of these data is complex, in part based on the inherent crosstalk of other (thoracic or abdominal) muscles. More important, the main disturber of the surface EMG of the diaphragm is the heart. The geometry of the heart relative to the diaphragm determines the amplitude and timing of the electrical activity of the heart to be represented in the measured signal. Separation of these sources of electrical activity would improve interpretation and clinical application of EMG measurements, and thereby improve individual patient care.

What you can expect from us:

We aim to investigate Signal Separation methods, e.g., Independent Component Analysis and deep neural network, for the monitoring of respiratory muscle activity of ICU patients. Through Signal Separation methods, different physiological source signals are expected to be well separated, and hence, the quality of respiratory muscle activity will be satisfied for accurate ICU patients’ monitoring.

In this project, you can expect to obtain the knowledge and experience about:

• Design and implement a polit experiment with healthy subjects to collect relevant signals as reference.
• Compare and evaluate different signal separation methods on the reference signals.
• Apply and customize the outperformed signal separation method on ICU patients’ dataset.
• A potential scientific publication can be expected based on the project outcomes.

What we expect from you:

• We are looking for open-minded students who like to challenge themselves and have a background in electrical engineering, biomedical engineering, or other relevant direction.
• Students should have strong interest in advanced signal analysis techniques.
• Students should have strong programming skills in Matlab\Python.
• Experience in physiological signal analysis and\or clinical research is optimal but not mandatory.

Information and application:

Please send your application to Dr. Ying Wang (ying.wang@utwente.nl), and include:

• A curriculum vitae including your name and contact (max 2 A4 pages).
• A personal motivation letter (max 1 A4 page).
• Lists of courses of your BSc and MSc programs.

Master graduation project (BSS+CRPH)

Educational program: Robotics, Systems&Control, Electrical Engineering, Biomedical Engineering, or other relevant direction

Supervision team:

Dr. Ying Wang (BSS-EEMC, UTwente) and Dr. Libera Fresiello  (CRPH-TNW, UTwente)

Project background:

The human cardiovascular system is a highly dynamic and complex system that has intrigued scientists for several centuries. The dynamicity of the cardiovascular system is particularly important during daily physical activity, when heart and vessels have to adapt to deliver more oxygen to muscular tissues.

Nowadays, with the advancement of computer powers, new and sophisticated techniques of investigations are emerging as machine learning. This technique  can accelerated the building of physiological models with data-driven strategies and can potentially serve the purpose of studying the human cardiovascular system during daily physical activity. However, these data-driven models lack in explaining the underlying physiological mechanism. This could lead to unreliable models used in real healthcare applications.

On the other hand, in-silico physiological simulators (e.g., the cardiorespiratory simulator described below) offer a detailed description of cause-effect mechanisms occurring in the human body, but are relatively complicated with unknown parameters which need to be manually tuned; and hence, the physiological simulator is not ideal to be directly used in the daily life applications.

Given this complementarity, physiological simulators and data-driven models derived by machine learning techniques can benefit from each other. Building such synergy can provide a reliable model for the monitoring of cardiorespiratory responses during daily physical activity.

 Brief description of the physiological model:

The in silico cardiorespiratory simulator is a lumped parameter model of heart, vessels, ventilation and respiration. It reproduces pressure and flows profiles over a heart cycle in the main circulatory districts as well as O2 and CO2 content in the arterial and venous vessels. The simulator includes also negative feedback loops (e.g. autonomic and metabolic controls) that allow to reproduce human homeostasis and the adaptation of the human body to stimuli and change of status. As for example, if exercise is input, the simulator automatically reproduces the response of the human body in terms of increase of heart rate, ventilation, cardiac output etc. The simulator was also adapted to reproduce exercise in heart failure patients, with related physical impairments and limitations. As such, it is a detailed and sophisticated deterministic model of human cardiorespiratory (patho)physiology.

Main reference:

Fresiello, L., Meyns, B., Di Molfetta, A., & Ferrari, G. (2016). A Model of the Cardiorespiratory Response to Aerobic Exercise in Healthy and Heart Failure Conditions. Frontiers in physiology, 7, 189. https://doi.org/10.3389/fphys.2016.00189

Alber, M., Buganza Tepole, A., Cannon, W.R. et al. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digit. Med. 2, 115 (2019). https://doi.org/10.1038/s41746-019-0193-y

What you can expect from us:

In this project, you can expect to obtain the knowledge and experience about:

• Apply machine learning techniques and dynamic system techniques in building up the synergy between cardiorespiratory simulators and data-driven models.
• Use signal analysis techniques to get physiological parameters.
• Design and implement a polit experiment with healthy subjects to collect relevant data.
• Learning basic cardiovascular physiological concepts and modelling techniques
• A potential scientific publication can be expected based on the project outcomes.

 What we expect from you:

• We are looking for open-minded students who like to challenge themselves.
• Students should have strong interest in advanced signal analysis, machine learning, and dynamic system techniques.
• Students should have strong programming skills in Matlab\Python.
• Experience in physiological signal analysis and\or clinical research is optimal but not mandatory.

Information and application:

Please send your application to Dr. Ying Wang (ying.wang@utwente.nl), and include:

• A curriculum vitae including your name and contact (max 2 A4 pages).
• A personal motivation letter (max 1 A4 page).
• Lists of courses of your BSc and MSc programs.

Educational program: Biomedical Engineering, Electrical Engineering or other relevant direction

Daily supervisor: Dr. Ying Wang <ying.wang@utwente.nl>

Project background:

Noncommunicable diseases, including cardiovascular diseases (CVDs) and diabetes, are the number one cause of death and disability in the world. For example, the number of people with CVDs nearly doubled to 523 million, and the one with diabetes almost quadrupled

Student assignment: Bachelor/Master project

to 463 million over the past three decades. The prevalent cases of noncommunicable disease have continued its decades-long rise, and the global burden of the diseases, e.g. premature mortality and the loss of quality of life, will dramatically increase given a boost in aging population with unhealthy lifestyle.

The early detection of noncommunicable diseases can help clinicians provide timely interventions; hence, this can mitigate socioeconomic burden and increase patients’ quality of life. However, the effectiveness of current early detection techniques is still lacking far behind despite the growing understanding of disease pathophysiology and treatment for in the advanced stage of diseases.

What you can expect from us:

We aim to develop algorithms to track the health condition of individuals at the risk of noncommunicable diseases using different modal physiological signals, such as accelerometer, electrocardiography (ECG), and photoplethysmogram (PPG) signals.

In this project, you can expect to obtain several or all knowledge and research experience listed below:

·        Design a polit experiment for healthy subjects as the control group of the targeted population.

·        Collect physiological signals from human subjects using wearable sensors.

·        Develop algorithms for the early detection of noncommunicable diseases:

o   Preprocess different modal signals.

o   Extract features from the signals.

o   Apply machine learning and/or physiological system techniques to identify the health condition of human subjects.

·        Test the developed algorithms on data collected from the targeted population: you will have chances to collaborate with hospitals and improve your algorithms accordingly.

·        A potential scientific publication can be expected based on the project outcomes.

What we expect from you:

·        We are looking for talented and open-minded students who have a background in biomedical engineering, electrical engineering, or other relevant direction.

·        Students should have strong interest in signal and system analysis and machine learning techniques.

·        Students should have strong programming skills in Matlab/Python.

·        Experience in physiological signal analysis and/or clinical research is optimal but not mandatory.

Information and application:

Please send your application to Ying Wang (ying.wang@utwente.nl), and include:

·        A curriculum vitae including your name and contact (max 2 A4 pages).

·        A personal motivation letter (max 1 A4 page).

·        Lists of courses of your BSc and MSc degrees.

Student assignment: Master graduation project (40-45 credits, 28-32 weeks)

Educational program: Electrical Engineering, Biomedical Engineering, or other relevant direction

Daily supervisor: Dr. Ying Wang <ying.wang@utwente.nl>

Project background:

Continuous wearable sensing technologies have been widely applied in the vital sign monitoring of in-hospital patients for timely and personalized intervention. These advanced techniques play an essential role in the monitoring of patients’ clinical adverse events to decrease patients’ mortality rate and release the burden of national health care systems. However, the performance of current remote monitoring systems is still not satisfied given several interference factors, such as, daily body movement artefacts, missing data, and patients’ heterogeneity.

What you can expect from us:

We aim to further improve the performance of our current monitoring algorithms using different modal vital sign signals and patients’ medical records, such as electrocardiography (ECG), photoplethysmogram (PPG) signals and body temperature. In this project, you can expect to obtain the knowledge and research experience about:

·        Develop an algorithm to detect or even predict clinical adverse events:

o   Preprocess different modal signals.

o   Extract features from the time-series signals, such as, using long-termed trend analysis, time-frequency and morphological signal analysis.

o   Apply machine learning techniques for the detection and/or prediction.

·        Test the developed algorithm on data collected from in-hospital patients: you can visit the hospital to observe the patients’ daily activities to inspire and improve your algorithms.

·        A potential scientific publication can be expected based on the project outcomes.

What we expect from you:

·        We are looking for talented and open-minded students who have a background in electrical engineering, biomedical engineering, or other relevant direction.

·        Students should have strong interest in signal processing, analysis, and machine learning.

·        Students should have strong programming skills in Matlab/Python.

·        Experience in physiological signal analysis and/or clinical research is optimal but not mandatory.

Information and application:

Please send your application to Ying Wang (ying.wang@utwente.nl), and include:

·        A curriculum vitae including your name and contact (max 2 A4 pages).

·        A personal motivation letter (max 1 A4 page).

·        Lists of courses of your BSc and MSc degrees.

Supervisors:                      Dr. ir. J.R. Buitenweg (j.r.buitenweg@utwente.nl)

Our research group recently developed a method to observe altered sensory processing in chronic pain patients. This method stimulates small nerve fibers in the skin, responsible for the sensation of pain, and measures detection probability and brain activity in response to these stimuli. In order to specifically stimulate nociceptive nerve fibers in the skin, the current should remain below twice the detection threshold (Mouraux, 2010). The current stimulation paradigm stimulates close to this detection threshold to remain nociceptive-specific and to accurately estimate the detection threshold. However, the signal-to-noise ratio of brain potentials evoked in this range of stimulus amplitudes is poor, and does not allow for observation of all evoked potential components that are typically observed during nociceptive stimulation, nor observation of event-related synchronization and desynchronization in the brain. Recently, a first pilot showed that we can enhance the signal-to-noise ratio by pairing each near-threshold stimulus with one stimulus of twice the number of pulses, also referred to as ‘paired-probing’. The next step is to use this paradigm in a larger number of participants and check whether this paradigm allows for observation of all typical evoked potential components as well as event-related (de)synchronization. As such, this master assignment includes the following objectives:

1)     Use the paired-probing paradigm to assess nociceptive detection thresholds, and evoked EEG responses (evoked potential, event-related (de)synchronization) on the hands and feet in 30 healthy participants.

2)     Assess whether stimulation using this paradigm remains limited to nociceptive nerve fibers, or co-activates tactile nerve fibers in the skin.

3)     Evaluate whether we can improve the signal-to-noise ratio of evoked potential components and event-related (de)synchronization using this paradigm.

References

Mouraux, A., Iannetti, G. D., & Plaghki, L. (2010). Low intensity intra-epidermal electrical stimulation can activate Aδ-nociceptors selectively. Pain, 150(1), 199-207. doi:10.1016/j.pain.2010.04.026

Supervisors:                   Dr. ir. J.R. Buitenweg  (j.r.buitenweg@utwente.nl)

Starting date:                   as soon as possible

Required background:   Biomedical engineering or electrical engineering, with a strong emphasis on signal processing, pattern recognition and machine learning. Experience with programming in Python and Matlab. Knowledge about electroencephalography and experience with experiments on human subjects are a pre.

Additional information: This master assignment can potentially be extended into a PhD project provided that funding is available.

Description:

About 50% of patients with diabetes eventually develop diabetic polyneuropathy during their lifetime, resulting in the loss of function of peripheral sensory nerve fibers (Hicks & Selvin, 2019). This loss of function is often followed by the development chronic neuropathic pain. Currently, there are no non-invasive methods available to monitor the development of chronic neuropathic pain and guide medical treatment.

Our research group recently developed a method to observe altered sensory processing in chronic pain patients. This method stimulates small nerve fibers in the skin, responsible for the sensation of pain, and measures detection probability and brain activity in response to these stimuli. Based on this evoked brain activity, we might be able to observe emerging neuropathic pain. However, the signal-to-noise ratio of single preselected features is too low accurately identify neuropathic pain in patients. Brain activity based observation of neuropathic pain in these patients might be improved by extracting a large set of features in the time, frequency, and/or time-frequency domain and combining these features using machine learning methods such as SVM, random forest, or Riemannian-geometry-based decoding (Gemein et al., 2020). During this master assignment, you will develop methods to automatically extract features from EEG data, to train and to evaluate classification performance of several feature-based machine learning methods. Objectives:

1)     Develop a pipeline for data preprocessing and augmentation.

2)     Develop machine learning pipelines for a collection of advanced feature-based machine learning methods for EEG classification.

3)     Evaluate the performance of data augmentation and machine learning methods.

References

Gemein, L. A. W., Schirrmeister, R. T., Chrabąszcz, P., Wilson, D., Boedecker, J., Schulze-Bonhage, A., . . . Ball, T. (2020). Machine-learning-based diagnostics of EEG pathology. NeuroImage, 220, 117021. doi:https://doi.org/10.1016/j.neuroimage.2020.117021

Hicks, C. W., & Selvin, E. (2019). Epidemiology of Peripheral Neuropathy and Lower Extremity Disease in Diabetes. Current Diabetes Reports, 19(10), 86-86. doi:10.1007/s11892-019-1212-8

Teachers:

Bettina Schwab

Student assignments:  

MSc project assignment (exceptions possible for BSc projects and internships)

Educational program:  

Biomedical Engineering, Electrical Engineering, (M3 for Technical Medicine)

Topics:                           

Transcranial alternating current stimulation (tACS), deep brain stimulation (DBS), brain-computer interfaces, neuromodulation, neural network dynamics, EEG data analysis

Required pre-knowledge:

Programming in matlab, signal analysis

Introduction:

We are working on a range of different topics related to brain stimulation and modulation of networks in the brain. Using computational (neural network and neural mass) modeling and field simulations, we aim to predict and further enhance the effects of this stimulation, and to use it specifically to steer functional connectivity. Thus, projects range from a technical level via quantitative neurophysiology to clinical applications. Dependent on the project, collaborations with the University Medical Center Hamburg-Eppendorf (Germany) and the University of Oxford (UK) are possible.

Motivated students are encouraged to send me an e-mail (b.c.schwab@utwente.nl) with their interests, course list and CV. Projects can then be adapted to personal interests and skills.

Example assignments:

-        Development of a software phase-locked loop to couple DBS and tACS

-        Measurement & analysis of tACS effects on EEG dynamics in healthy control participants

-        Computational modeling to predict and enhance the effects of tACS

-        Electric field simulations for tACS and/or DBS

-        Closed-loop stimulation dependent on EEG or kinematic signals

-        Clinical optimization of brain stimulation for Parkinson’s disease or stroke

Email contact: b.c.schwab@utwente.nl

Educational programme:  BMT, TG, HS.

a)     Optimizing health outcome in (neo)adjuvant treatment for breast cancer patients.

Background information:

In the Netherlands 1, out of 7 women will develop breast cancer, in 2017 17.423 new cases were diagnosed. A substantial part of these women are treated with (neo) adjuvant systemic therapy, hormonal- and/or chemotherapy. In case of (neo)adjuvant chemotherapy, this is accompanied by a significant loss in physical condition and by a substantial weight gain. In part these women experience chronic fatigue afterwards, loss of concentration and difficulty in regaining normal activity at work and at home. Our understanding is that if preventive measures are taken the loss in physical condition and weight gain can be (partially) prevented.

Previous exercise and nutritional habits combined with lifestyle are important factors in maintaining, as good as possible, weight and physical fitness. The majority of women with breast cancer have some overweight and they do not engage in enough physical activity.

Tailoring for which patient and at what moment intervention is needed is difficult at this moment by lack of tools and practical guidelines. Recently a personalized and technology-supported coaching system to support the patient’ self-management is developed for diabetes patients. A comparable system can be used for oncology patients treated with chemotherapy.

Supervisors: Dr. Laverman – g.d.laverman@utwente.nl, Dr. Oving.

b)     Impact of (Neo)Adjuvant Chemotherapy on Long-Term Performance and Employment of Early-Stage Breast Cancer Survivors in our region.

Background information:

Many women with early-stage breast cancer are working at the time of diagnosis and survive without recurrence. The short-term impact of chemotherapy on employment (<1 year) has been demonstrated, but the long-term impact merits further research. Even less is known about long-term impact of cancer treatments on social functioning, physical activity or sports, managing family live and maintaining relationships. Recently a database was made of all our breast cancer patients. This database can be used for further research.

Supervisors:  Dr. G. Laverman, Dr. Irma Oving, Dr. Ester.Siemerink

Student assignment: Master graduation project (40-45 credits, 28-32 weeks)

Educational program: Embedded Systems, Electrical Engineering, Biomedical Engineering, or other relevant direction

Supervision team: Dr. Ying Wang (BSS, UTwente) and Dr. Yang Miao (RS, UTwente)

Project background:

Continuous health monitoring plays an essential role in timely and personalized disease prevention. Daily remote monitoring with advanced sensing techniques can help increase individuals’ quality of life and release the burden of national health care systems. Currently, two types of sensing techniques—wearable and contactless sensing—have been used for health monitoring. Compared with wearable sensing techniques, contactless sensing can provide users comfort and natural feeling meanwhile reducing the device-management burden of healthcare professionals. However, the technical usage of contactless sensing systems, e.g., radio-based techniques, is still not sufficiently validated in daily health monitoring.

What you can expect from us:

We aim to investigate the technical usage of contactless sensing system in daily health monitoring. Through a radio-based contactless system, vital signs (e.g., heart rate and breath rate) and movement signals will be collected from experiment subjects. Daily health monitoring system will be developed in this project, and the project results will be transferred to clinical applications in future.  

In this project, you can expect to obtain the knowledge about:

• Design a pilot experiment for healthy subjects to simulate the activities of in-\out-hospital patients.
• Collect multimodal physiological signals from healthy subjects to learn about the potential effects of real-life factors on the quality of signals.
• Develop an algorithm to track the physiological states of individuals:
• Preprocess different modal signals. Extract clinical-relevant features from the signals. Apply machine learning techniques to classify different physiological states.
• A potential scientific publication can be expected based on the project outcomes.

What we expect from you:

• We are looking for talented and open-minded students who have a background in embedded systems, electrical engineering, biomedical engineering, or other relevant direction.
• Students should have a strong interest in signal processing, analysis, and machine learning.
• Students should have strong programming skills in Matlab\Python.
• Experience in physiological signal analysis and\or clinical research is optimal.

Information and application:

Please send your application to Dr. Ying Wang (ying.wang@utwente.nl), and include:

• A curriculum vitae including your name and contact (max 2 A4 pages).
• A personal motivation letter (max 1 A4 page).
• Lists of courses and grades of your BSc and MSc degrees.

Supervisors:

Roswita Vaseur, Wendy Oude Nijeweme - d’Hollosy, Monique Tabak

Student assignments:

BSc and MSc project assignment

Educational programs:

Interaction Technology, Industrial Design Engineering, Communication Science  

RE-SAMPLE Project description:

Chronic Obstructive Pulmonary Disease (COPD) is a common, progressive lung condition with a high impact on quality of life and life expectancy. Many patients with COPD have multiple chronic conditions, like diabetes, cardiovascular diseases or mental health issues. The care of patients with COPD and co-morbid chronic conditions is very complex. There are overlapping risk factors and symptoms which can delay the selection of appropriate treatment. Furthermore, multiple healthcare professionals are involved in the treatment of people with COPD. The challenge of the increasing number of patients with COPD and multi-morbid chronic conditions requires an integrated, personalized, approach to support and manage care for these patients. RE-SAMPLE will work to transform the healthcare journey of patients with other chronic conditions by using real world data (RWD) to monitor symptoms beyond scheduled medical check-ups, to provide doctors, caregivers, and patients a unique insight into common, day-today triggers that can lead to health complications and to support personalized treatment and develop a virtual companionship program.

Student assignment description

The objective of this project is to develop an interactive tool that will be used during consultation with a healthcare professional. This tool will visualize the progression and burden of disease to set goals and decide on a treatment plan together. To develop our interactive tool, students will work on:

Focus groups

-        Developing and conducting a protocol for focus groups with patients and healthcare professionals to explore presentation of information (communication strategies) for shared decision making. For example; identifying what input we need for the interactive tool or what kind of information doctors and patients with COPD would like to see in this tool.

Co-designing and prototyping

-        Based on the information elicited during focus groups, the interactive tool will be developed based on co-designing and prototyping which include visualizing shared decision making processes.

Contact:

Roswita Vaseur (r.m.e.vaseur@utwente.nl)

Wendy Oude Nijeweme - d’Hollosy (w.dhollosy@utwente.nl)

Monique Tabak (m.tabak@utwente.nl)

Supervisors:

Roswita Vaseur, Wendy Oude Nijeweme - d’Hollosy, Monique Tabak

Student assignments:

BSc and MSc project assignment

Educational programs:

Health Sciences, Biomedical Engineering

RE-SAMPLE Project description:

Chronic Obstructive Pulmonary Disease (COPD) is a common, progressive lung condition with a high impact on quality of life and life expectancy. Many patients with COPD have multiple chronic conditions, like diabetes, cardiovascular diseases or mental health issues. The care of patients with COPD and co-morbid chronic conditions is very complex. There are overlapping risk factors and symptoms which can delay the selection of appropriate treatment. Furthermore, multiple healthcare professionals are involved in the treatment of people with COPD. The challenge of the increasing number of patients with COPD and multi-morbid chronic conditions requires an integrated, personalized, approach to support and manage care for these patients. RE-SAMPLE will work to transform the healthcare journey of patients with other chronic conditions by using real world data (RWD) to monitor symptoms beyond scheduled medical check-ups, to provide doctors, caregivers, and patients a unique insight into common, day-today triggers that can lead to health complications and to support personalized treatment and develop a virtual companionship program.

Student assignment description

The objective of this project is to develop a high-level overview of a health coaching strategy for patients with COPD and co-morbid chronic conditions. Personalized coaching actions and lifestyle coaching will be specified to counteract or delay changes in disease progression. To develop our coaching strategy, students will:

1.      Identify what coaching domains are relevant for patients

Examples of coaching domains are physical activity, mental health promotion, sleep behavior, coping, nutrition and smoking cessation. The student will possibly help with analyzing and processing data of interviews with patients to decide which domains should be offered to patients.

2.      Provide content to the identified coaching domains.

After relevant coaching domains are identified, the content for these domains must be designed. Relevant aspects which may be considered for the content are:

a.      Purpose/goals

b.      Requirements

c.      Treatment

d.      Behavior Change Techniques

e.      Measurable parameters  

f.       Delivery of information

g.      Etc.

3.      Personalize the coaching domains

After the relevant coaching  domains are identified and the content is designed, the coaching domains will be personalized based on patient characteristics and preferences (applying elicitation methods). Thus, specific patient profiles will be designed and adjusted based on patient characteristics and preferences.

Contact:

Roswita Vaseur (r.m.e.vaseur@utwente.nl)

Wendy Oude Nijeweme - d’Hollosy (w.dhollosy@utwente.nl)

Monique Tabak (m.tabak@utwente.nl)

Teachers:                           Bert-Jan van Beijnum

Student assignments:      MSc project assignment

Educational programme:  EE, BME

Topics: sleep apnea, detection algorithms, signal analysis

Project Summary:

Sleep apnea is a common sleep-related breathing disorder which leads to a temporary obstruction of the upper airways during sleep. Despite its temporary nature, sleep apnea has a dramatic impact on people’s lives. Early symptoms include daytime sleepiness and reduced concentration and learning abilities. If it remains untreated, sleep apnea can lead to health-threatening diseases, such as cardiomyopathies, stroke, and pulmonary hypertension. Early detection and classification of the sleep apnea events are the first necessary steps to prompt treatment and prevent the development of more severe conditions.

At Onera, the future of sleep medicine is being developed: a sleep diagnostic system composed of wearable devices able to reliably acquire a comprehensive picture of a subject’s sleep. Physiological signals are recorded and processed by machine learning algorithms to automatically detect sleep disorders. The figure below shows a number of typical examples of apnea when measuring the nasal airflow and abdominal movement.

 Figure 1: Types of sleep apnea. Changes in breathing pattern as recorded from nasal airflow and abdominal movements. Figure is adapted from Scientific Reports 2020 Kang Sun et al.

The objective of this master thesis is the development and validation of an algorithm for the automatic detection and classification of apnea events using data acquired from the Onera Sleep Test System, a multi-sensor diagnostic system.

 The research will address the following questions:

• What is the current state of the art in sleep apnea detection algorithms?
• Which signal conditioning methods need to be applied to accurately and reliable detect apnea biomarkers (e.g., various signal processing filters, noise rejection/reduction methods, time – frequency analysis and processing)?
• What are the evaluation criteria needed to evaluate the performance of classification algorithms?
• Which classification algorithm to detect and classify apnea event perform best, and to what extend do apnea biomarkers improve classification results?

It is envisioned that the report of the research will be in the form of a scientific paper.

The following skills are required:

• Following one of the following educational programmes: Biomedical Engineering or Electrical Engineering with demonstrated interest in biomedical applications.  
• Good knowledge in Python (scikit-learn, pandas, matplotlib, seaborn) or Matlab.
• Interest and experience in signal processing, and machine learning (specifically classification)
• Good English communication skills.
• Positive attitude, can-do mentality, and flexibility.

Contact

The UT contact persons for this Master assignment are:

• Bert-Jan van Beijnum (b.j.f.vanbeijnum@utwente.nl)

The Onera contact persons for this assignment are:

• Flavio Raschella’ (Flavio.Raschella@onerahealth.com)
• Tineke de Vries (Tineke.deVries@onerahealth.com)

Supervisor: Juan Delgado Teran: j.d.delgadoteran@utwente.nl

Student assignment: Master BMT

Principal investigator: Prof. Dr Richard van Wezel: r.j.a.vanwezel@utwente.nl

Detection and eventual prediction of freezing-of-Gait in 
Parkinson’s Disease
My current research focuses on the detection and eventual prediction of Freezing-of-Gait in people living with Parkinson’s Disease. Making use of wearable sensors, such as insoles, ankle sensors and smartphones, we investigate freezing episodes in an at-home situation. 

Freezing-of-Gait detection in Parkinson’s Disease from wearable sensors

This project, “PROMPT” (Personalised care and Research On Motoric-dysfunctioning for Patient-specific Treatments), aims to better understand and diagnose a symptom of Parkinson’s disease – freezing-of-gait – in Parkinson’s disease https://youtu.be/3-wrNhyVTNE, in order to provide for personalized care.

Making use of the eHealth House at University of Twente www.utwente.nl/en/techmed/facilities/htwb-labs/ehealth-house/, your tasks are:1- to assist in the recruitment of participants, and 2- to assist in the collection and post-processing of data from participants. In this study, we aim to include a total of 25 individuals with Parkinson’s disease. During data collection, various motion sensors are attached to the joints of a person, who is then asked to perform everyday tasks (e.g. cooking, cleaning, etc.) in the eHealth House, as well as take a 10-minute walk outside. In the eHealth lab, videos are taken of the individual moving around, in order to label any freezing episodes. The labelled video data is then utilized to train classification algorithms to detect freezing-of-gait based on motion sensor data.

Experience with recruitment and management of Parkinson’s patients and/or usage of movement sensors would be ideal but is not required.

Start period: 09-2021

Supervisors: dr. Ruben Regterschot, dr. Bert-Jan van Beijnum.

Type of assignments: bachelor projects, master projects, internships.

Background student: biomechanical engineering, biomedical technology, movement science, health sciences, technical medicine, biomechanics, computer science, data science, or other relevant direction.

Project description & project aims:

The student projects contribute to the national ArmCoach4Stroke project: a large research project where multiple leading universities (UTwente, TU Delft, Erasmus MC, Amsterdam UMC), companies, and rehabilitation centres work closely together to develop the ArmCoach4Stroke (see Figure). The ArmCoach4Stroke is an innovative and new medical device that enables arm rehabilitation after stroke in the home environment.

The student projects focus on the development and validation of algorithms for the recognition of arm exercises and the calculation of arm exercise metrics based on data from body-fixed motion sensors (IMU sensors). In the project, you will develop algorithms for the recognition of specific arm movements (e.g., reaching) and the calculation of arm exercise metrics (e.g., the time duration of a reaching movement) based on motion sensor data. You will evaluate the sensor-based method in the lab of UTwente in comparison to camera data (Vicon system).

In the project, you will gain extensive experience with signal analysis, algorithm development (in Matlab, Python, R), sensor fusion, machine learning, body-fixed motion sensors (Xsens), camera systems for movement analysis (Vicon), and the biomechanics of the arm. This project provides you with an exciting opportunity to train research skills and to work on a national research project with leading technical and clinical universities. The project is technically oriented but with a clear clinical application as goal.

Contact:

Dr. Ruben Regterschot (University of Twente & Erasmus MC): g.r.h.regterschot@utwente.nl

Dr. Bert-Jan van Beijnum (University of Twente): b.j.f.vanbeijnum@utwente.nl

Assignment:                  Bachelor, Master, Internship

Educational program:   BME, TM, HS, PSY, CREATE

Daily supervisor:           Kim Wijlens, MSc or ir. Lian Beenhakker

Project description

Due to improved treatment and early diagnosis of breast cancer, there are more survivors. However, this also means that more patients are struggling from the late effects of treatment. One of these late effects is cancer-related fatigue (CRF), which is defined by the American National Comprehensive Cancer Network as “a distressing, persistent, subjective sense of physical, emotional and/or cognitive tiredness or exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning.”

In the Personalized cAnceR TreatmeNt and caRe (PARTNR) project, we aim to help breast cancer patients suffering from CRF. We will holistically assess patients, considering all aspects that can cause/influence the fatigue and predict who might be at risk of developing CRF. Using multimodal data, we will advise an intervention to reduce the fatigue which is based on the holistically assessed patient information about CRF and personal preferences patients might have for types of intervention. In the end, we will combine all information from patients into an intelligent self-learning platform that patients can use to keep track of and improve their fatigue.

The PARTNR project is a collaboration between the University of Twente, Ziekenhuis groep Twente (ZGT), Helen Dowling Institute (HDI), Roessingh Rehabilitation Centre, University Medical Center Groningen (UMCG), Dutch breast cancer association (BVN), Netherlands comprehensive cancer organisation (IKNL), Ivido and Evidencio.

If you would like to work on the PARTNR project during your internship or thesis, please contact either Kim Wijlens (k.a.e.wijlens@utwente.nl) or Lian Beenhakker (l.beenhakker@utwente.nl). Supervisors will depend on the chosen assignment.

PARTNR Team:

dr. Annemieke Witteveen (BSS), prof. dr. Sabine Siesling (HTSR), dr. Christina Bode (PGT), 

Teachers: Jan R. Buitenweg, j.r.buitenweg@utwente.nl

Educational Programme: BSc - BMT, TG

Background

Chronic pain often results from disturbed processes in the central nervous system. Once chronic pain is established, treatment is relatively ineffective, with – at best – one patient in three or four achieving 50% pain intensity reduction. Early detection and therapeutic action would mean better treatment outcomes and fewer clinical efforts per patient, but appropriate diagnostic tools are lacking.

Increased sensitivity to noxious stimuli is widely recognized as a key factor in chronic pain development. Noxious stimuli are processed by neural mechanisms at several stages in the ascending pathway from the periphery to the brain, into a conscious pain experience. As a response to injury or disease, maladaptive changes in this pathway may result in increased pain sensitivity. Clinical observation of the specific malfunctioning of peripheral and central components of this pathway is limited at present but would permit a better understanding and early selection of interventions for treatment or prevention of chronic pain.

Recently, we developed a new method for observing the properties of nociceptive processing utilizing subjective detection of electrocutaneous stimuli in combination with objective neurophysiological brain responses. In this method, nociceptive afferents are activated by temporally defined current stimuli with a varying number of pulses and varying interpulse intervals. As these different temporal stimulus properties result in different excitation of nociceptive processing mechanisms of the ascending system, subsequent processing of stimulus-response pairs (SRPs) into estimated nociceptive detection thresholds (NDTs) and Evoked brain Potentials (EPs) of multiple stimulus types may provide information about the properties of these mechanisms.

The EPs comprises mainly of two elements: (1) activity related to the processing of the nociceptive stimulus and (2) task-related activity. At present, the majority of the activity seems to be related to the task. For an improved characterization of the nociceptive processing, we would like to better evaluate the activity related to the processing of the nociceptive stimulus. By providing an enriched stimulation protocol to the participant, we might be able to reduce the extent of task-related activity. This could allow us to better evaluate the activity related to nociceptive processing. Thus far, we have however not experimentally evaluated this protocol. In this study, we want to evaluate whether this enriched stimulation protocol is feasible and useful.

Assignment – Prepare and execute experiments at the University of Twente to evaluate the effect of the enriched stimulation protocol on the EP's.
Components – (1) Perform a literature study. (2) Implement and conduct a human subject experiment. (4) Analyze the data and (5) report and (6) present the results.

Student assignment:          Master assignment (28-32 weeks, 40-45 ECs)

Contact:                              Dr. Yavuz US.

email:                                     s.u.yavuz@utwente.nl

Background

Lower limb rehabilitation exoskeletons support and assist patients in locomotion after neurological injuries. Identifying the mechanisms underlying neural adaptations to exoskeleton training is key to design optimal strategies for assistance. Previous studies^1,2 indicate reduced muscle activation (EMG) after short-term exoskeleton training.  However, the specific mechanisms for this adaptation remain unclear.

High-density surface EMG (HD-EMG) is a non-invasive technique to measure neural activity from multi-channel grids on the muscle. Blind source separation (BSS) techniques, such as Convolution Kernel Compensation (CKC) ³, enable exploiting the high-resolution capability of HD-EMG to open a window into constituent motor neuron spike trains. By these means, we aim at investigating the neural mechanisms that govern locomotor adaptations to short-term exoskeleton training.

Goal: The main goal of this assignment is to develop and validate methodologies for identifying motoneuron adaptations to short-term exoskeleton training.

Keywords: HD-EMG, motor neuron, decomposition, blind source separation, exoskeleton training

Main Activities:

·       Literature study on motor neuron properties (recruitment threshold, variability, discharge rate and synchronicity, stretch reflex, H-reflex and exoskeleton training.

·       Development of a methodology to identify physiological changes between before and after-training trials (in time or frequency domain).

·       Statistical analysis to compare results.

·       Scientific report about the findings.

References:

1.        Kao PC, Lewis CL, Ferris DP. Short-term locomotor adaptation to a robotic ankle exoskeleton does not alter soleus Hoffmann reflex amplitude. J Neuroeng Rehabil. 2010;7(1):33. doi:10.1186/1743-0003-7-33

2.        Gordon KE, Ferris DP. Learning to walk with a robotic ankle exoskeleton. J Biomech. 2007;40(12):2636-2644. doi:10.1016/j.jbiomech.2006.12.006

3.        Holobar A, Zazula D. Gradient Convolution Kernel Compensation Applied to Surface Electromyograms. Indep Compon Anal Signal Sep. 2007:617-624. doi:10.1007/978-3-540-74494-8_77

Student assignment:  BSS master assignment (28-32 weeks, 40-45 ECs)

Contact person:          Dr. Utku S. Yavuz, Biomedical Signals and Systems research group,

Email:                            s.u.yavuz@utwente.nl

Background

High-density surface EMG (HD-EMG) is a non-invasive technique to measure neural activity from multi-channel grids on the muscle. Blind source separation (BSS) techniques, such as Convolution Kernel Compensation (CKC), exploit the high-resolution capability of HD-EMG to open a window into constituent motor neuron spike trains. However, although these techniques offer an accurate decomposition, they are often suboptimal in dynamic or noisy conditions, and most of them only work offline. For this assignment, we aim at developing a supervised learning framework to decompose HD-EMG signals, based on the output of CKC decomposition.

Goal: To develop a supervised decomposition technique to extract motor neuron spike trains from HD-EMG recordings.

Keywords: HD-EMG, motor unit, decomposition, BSS, deep learning, machine learning

Main Activities:

•Literature study on state-of-the-art techniques for HD-EMG decomposition.

•Developing a supervised decomposition framework using HD-EMG data and decomposed spike trains recorded from isometric plantar- and dorsi-flexion trials.

•Statistical analysis to compare several quality and efficiency metrics (accuracy,

computational time, rates of agreement, …) between the proposed supervised framework against CKC decomposition.

References:

K.Clarke et al., "Deep Learning for Robust Decomposition of High-Density Surface EMG Signals," inIEEE Transactions on Biomedical Engineering, doi: 10.1109/TBME.2020.3006508.

C.Chen, Y. Yu, X. Sheng and X. Zhu, "Analysis of motor unit activities during multiple motor tasks by real-time EMG decomposition: perspective for myoelectric control," 2020 42nd Annual International Conference ofthe IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 4791-4794,doi: 10.1109/EMBC44109.2020.9176362

Teachers:

Bettina Schwab

Student assignments:  

MSc project assignment (exceptions possible for BSc projects and internships)

Educational program:  

Biomedical Engineering, Electrical Engineering, (M3 for Technical Medicine)

Topics:                           

Transcranial alternating current stimulation (tACS), deep brain stimulation (DBS), brain-computer interfaces, neuromodulation, neural network dynamics, EEG data analysis

Required pre-knowledge:

Programming in matlab, signal analysis

Introduction:

We are working on a range of different topics related to brain stimulation and modulation of networks in the brain. Using computational (neural network and neural mass) modeling and field simulations, we aim to predict and further enhance the effects of this stimulation, and to use it specifically to steer functional connectivity. Thus, projects range from a technical level via quantitative neurophysiology to clinical applications. Dependent on the project, collaborations with the University Medical Center Hamburg-Eppendorf (Germany) and the University of Oxford (UK) are possible.

Motivated students are encouraged to send me an e-mail (b.c.schwab@utwente.nl) with their interests, course list and CV. Projects can then be adapted to personal interests and skills.

Example assignments:

-        Development of a software phase-locked loop to couple DBS and tACS

-        Measurement & analysis of tACS effects on EEG dynamics in healthy control participants

-        Computational modeling to predict and enhance the effects of tACS

-        Electric field simulations for tACS and/or DBS

-        Closed-loop stimulation dependent on EEG or kinematic signals

-        Clinical optimization of brain stimulation for Parkinson’s disease or stroke

Email contact: b.c.schwab@utwente.nl

Research Question: Wat is de accuraatheid en betrouwbaarheid van de Everion biosensor en de Fitbit Charge 3 in gezonde vrijwilligers?

Department:                     Chirurgie Universitair Medisch Centrum Groningen

Language:                        Dutch/English

Educational programme:  BME

Description project content and methods: 

Bij de chirurgie loopt een groot project waarbij telemonitoring toepassingen worden gevalideerd en geïmplementeerd voor patiënten die grote operaties ondergaan. Het gebruik van telemonitoring, als onderdeel van eHealth, komt tegemoet aan de tendens naar personalized medicine en is geassocieerd met betere klinische uitkomsten en kosteneffectiviteit van zorg in verschillende takken van de gezondheidszorg, met name in chronische ziekten. Het inzichtelijk maken van het welzijn van een patiënt zorgt ervoor dat eerder of beter omgegaan kan worden met veranderingen in gezondheid. De verwachting is dat telemonitoring ook in de perioperatieve periode, van toegevoegde waarde kan zijn. Telemonitoring zou kunnen bijdragen aan de optimalisatie en individualisatie van het perioperatieve (herstel)proces en daarbij te vroeg ontslag, onnodig lange ziekenhuisopnames of ongeplande heropnames kunnen reduceren. 

Op dit moment zijn draadloze biosensoren beschikbaar om fysieke parameters te monitoren. De Everion biosensor van Biovotion (Biovotion AG, Zürich, Switzerland) is een CE-gecertificeerde sensor die op de bovenarm wordt gedragen (zie afbeelding) en vitale parameters en activiteit meet met een frequentie van 1Hz, waaronder: hartslag, hartslag variabiliteit, oxygenatie, huid temperatuur, ademhalingsfrequentie, aantal stappen en type activiteit. Hartritme is al een gevalideerde parameter. Bekend is dat de hoeveelheid postoperatieve dagelijkste stappen en fysieke activiteit van een patiënt tijdens ziekenhuisopname geassocieerd is respectievelijk het risico op heropname binnen 30 dagen na de operatie en de lengte van de ziekenhuisopname. Om deze informatie te implementeren in de perioperatieve zorg van hoog-risicopatiënten, moeten in kaart worden gebracht wat de betrouwbaarheid van verschillende sensoren is wat betreft het meten van vitale parameters, het aantal stappen en activiteit (en de gevoeligheid van vitale parameters voor activiteit). Daarom gaan we een validatiestudie doen bij gezonde vrijwilligers.

De studenten zullen telemonitoring devices valideren door het uitwerken van strategieën om looppatronen te simuleren, mee ontwikkelen van protocol en meetopstelling en uitvoeren van tests bij gezonde vrijwilligers. In dit project hebben studenten de mogelijkheid om zelf metingen uit te voeren en kennis te maken met de faciliteiten van het eHealth house (Techmed Centre, Universiteit Twente) en de nieuwste ontwikkelingen op het gebied van telemonitoring.

Supervisors: 

dr.ir. M. Tabak, associate professor vakgroep Biomedische Signalen en Systemen, UTwente

dr. R.C.L. Schuurmann, post-doc multi-modality medical imaging group, UTwente

prof. dr. J.P.P.M. de Vries, vaatchirurg UMCG

M.E. Haveman, MSc, promovendus UMCG

Contact e-mail: m.e.haveman@umcg.nl / m.tabak@utwente.nl

Contact person:                     Utku Yavuz

Student assignments:            MSc project assignment

Educational programme:        BME, TM, EE, Create

Project Summary:

The dramatic increase in average life expectancy during the 20th century is one of society’s greatest achievements. In many countries, the oldest age (people aged 85 or older) are now the fastest-growing proportion of the total population (the 85-and-over population is projected to increase 351% between 2010 and 2050). As people grow older they are increasingly at risk of falling with resultant injuries. One out of three persons older than 65 and 50% of those older than 80, fall at least once per year (Todd & Skelton, World Health Organization, 2004) based on various reasons. In some cases, fall may be an indication of chronic neurological disorders affecting postural stability and gait (such as Parkinson disease).  Therefore, understanding the neural mechanism underlying the postural control has utmost importance to develop an accurate intervention. 

It is well established that the sensory information conveyed from muscle and joint proprioceptors play an important role in the control of posture and gait in humans. In a recent study, we showed that cutaneous mechanoreceptive inflow from the neck is also integrated into the control of posture. More importantly, we showed that the electro-tactile stimulation of cutaneous afferents at the neck region, with a subtle (140% of perception threshold) stimulation current intensity, led to a drift towards forward (leaning forward) in the centre of pressure (Nunzio et.al., Experimental Brain Research 2018). Within the proposed study, we aim to investigate the integration of cutaneous receptors located at the foot sole and determining the pattern and strength of stimulation current that leads to an efficient effect on body orientation (posture).  The findings are relevant for the exploitation of electro-tactile stimulation for rehabilitation interventions where induced body orientation is desired.

Assignment:

In this study, you will use biomedical equipment to record the electrical activity produced by muscles contraction (electromyography - EMG) and to acquire the oscillations of the human body through force platforms during quite upright stance conditions. You will then administer a series of electro-tactile stimulation and analyze the postural effects induced by such stimuli.

The acquired data will be analyzed with standardized and validated algorithms to study the physiological basis of these postural effects. This will lead to the development of new rehabilitation protocols and devices to enhance postural control in the elderly population and neurological patients. You will learn:

-        neurophysiology of postural control

-        recording muscle electrical signals and the movement of the centre of foot pressure using professional biomedical equipment

-        developing acquisition systems with stimulation units

-        statistically analyze the acquired data

Contact:

The student will work at the laboratory of the Department of Biomedical Systems and Signals, faculty of EEMCS, University of Twente, under the supervision of S. U. Yavuz, PhD. email: s.u.yavuz@utwente.nl, tel:+31534898158

Teachers:                           Wendy d’Hollosy, Anouk Middelweerd en Eclaire Hietbrink 

Student assignments:      BSc and MSc project assignments

Educational programme:  HS, TG, CS, HMI/Create, BME

Topics: tailoring and personalization, technology supported lifestyle coaching, experimental designs, mHealth, machine learning, human computer interfaces

Project Summary:

Over 5.3 million people in the Netherlands have a chronic disease. The importance of a healthy lifestyle in chronic disease management has been increasingly recognized in recent years. There is a growing body of evidence that lifestyle interventions contribute to a reduction in disease burden and to an improvement in the Quality of Life (QoL) in people with chronic diseases. eHealth technologies show promising results regarding lifestyle coaching. Compared to regular face-to-face lifestyle coaching, eHealth interventions have several advantages as eHealth is less effort-, time- and cost intensive and enables more continuous support in chronic disease management in daily life.

For this reason, this project focuses on the development, implementation and evaluation of the E-manager Chronic Diseases for people with type 2 diabetes mellitus, asthma, chronic obstructive pulmonary disease (COPD) and heart failure. In the E-Manager project, a digital coaching platform, the E-Supporter, is being developed for people with chronic diseases who want to improve their lifestyle. The E-Supporter motivates and helps patients in everyday life to pursue lifestyle goals by providing tailored e-coaching that ties in with the patients’ behavioural state, character and abilities. The coaching content that is developed will be integrated into existing apps. For example, the current content of the E-Supporter for people with type 2 diabetes mellitus is built into the “Diameter” app and the “MiGuide” app.

Assignments:

The E-Supporter has not yet been fully developed and evaluated, which is why bachelor and master assignments are available focusing on different topics:

·       Developing and evaluating more tailored coaching content for physical activity and/or nutrition;

·       Broadening of the existing coaching content to other chronic conditions (e.g., COPD) or lifestyle domains (e.g., smoking);

·       Developing a rule based decision support system to automate the provision of tailored lifestyle coaching;

·       Using data science techniques to tailor the coaching strategies to an individual patient.

Email contact: a.middelweerd@utwente.nl

Possible project supervisors:

Bouke Scheltinga                   B.Scheltinga@rrd.nl 

Robbert van Middelaar          R.P.vanmiddelaar@utwente.nl       

Jasper Reenalda                    J.Reenalda@rrd.nl

Jaap Buurke                          J.Buurke@rrd.nl

Educational programmes: EE and BMT

Topic: Sensing and analyzing (sports) biomechanics with (inertial)sensors

Project summaries:

Several PhD students are involved in research related to (sports) biomechanics and (inertial) sensors. Below you can find a short description of the different topics. If you have an interest in a Bachelor of Master project related to one of these topics, please contact the PhD student mentioned by the project.

Bouke Scheltinga: Model and quantify physiological and biomechanical training load over time using workload data. Ideally with a minimal sensor setup, which can consist of inertial sensors and physiological sensors.

Robbert van Middelaar: Development of new algorithms and tools to monitor an athlete (runner, rower, volleyball etc.) in the sports-specific setting to improve their performance or to prevent them from injuries; detect changes in their movement or posture and give feedback via an interactive system". (www.sports-data-interaction.com)

The project is related to 'Sports, Data & Interaction Team', meaning there is close collaboration with projects from Human Media Interaction and Mathematics. 

Teachers:                               Monique Tabak, Mattienne van der Kamp

Student assignments:         BSc and MSc project assignments

Educational programme:     BMT, TG, EE, TCS, CT, CS, ATLAS

Contact:                                 m.tabak@utwente.nl

Topic:                                     eHealth in the pediatric asthma care: From smart monitoring to implementing a new care standard for personalized health.

Project Summary:

This project focuses on innovative, personalized eHealth for pediatric asthma patients. Currently, pediatric asthma care heavily relies on information acquired during scheduled visits when children usually are asymptomatic. Relevant (symptomatic) information can therefore be missed due to for example recall bias, hampering the treatment of the child. Smart monitoring (e.g. sensing, ESM, video) allows regular monitoring of asthma control at home and helps to identify worsening of asthma control (e.g. prediction modelling, decision-support). This enables the pediatrician to anticipate timely for personalized treatment and self-management of the child and parents, potentially resulting in less symptoms and a better quality of life.

The challenges of this project lie in;

·       finding the right combination of monitoring tools and devices for the right physiological parameters (i.e. spirometry, smart inhalers, activity, EMG, etc),

·       developing a child-friendly and attractive monitoring platform,

·       creating evidence-based clinical decision support algorithms,

·       increasing self-management of asthmatic children and parents (with i.e. automated feedback),

·       and implementing eHealth in the current clinical practice in a clear and efficient way. 

During the course of this project, we as Medisch Spectrum Twente, University of Twente and Roessingh Research and Development devoted ourselves to many of the above-mentioned challenges and are working hard to progress towards extending, personalizing and validating eHealth strategies in pediatric asthma care. So that it benefits both the caregivers and the care providers. 

Educational programme: BMT, TG, HMI, ATLAS

Achtergrond en probleem

Diabetes Mellitus Type 2 (T2DM) is een chronische ziekte waarbij het risico op complicaties is verhoogd door ontregelde glucosewaarden. Deze patiënten hebben hulp nodig om meer grip te krijgen op de diabetes. Een belangrijke factor dat winst kan opleveren bij T2DM is een gezondere leefstijl, echter ontbreekt de tijd in de gezondheidszorg om patiënten hier optimaal in te kunnen begeleiden. Uit vooronderzoek is gebleken dat patiënten niet voldoen aan de norm gezonde voeding en gezond bewegen en de kennis over de voordelen van een gezonde leefstijl ontbreekt. Daarom is het ZGT in samenwerking met de Universiteit Twente bezig om een gepersonaliseerde diabetes coach, in de vorm van een mobiele app, te ontwikkelen die de diabetespatiënt helpt de glucosewaarde op peil te houden. De coach, de Diameter, maakt gebruik van continue monitoring van glucosewaarden, lichaamsbeweging, hartslag, voeding en medicatie op basis waarvan een individueel voorspellend model voor glucoseregulatie wordt ontwikkeld. Dit model dient o.a. als input voor de coaching module die gebaseerd op theorieën van gedragsverandering en motivatietheorieën de patiënt ondersteunt in het maken en volhouden van verantwoorde keuzes. De verwachting is dat patiënten hiervan voordelen zullen merken op de korte termijn (groter gevoel van welbevinden) en lange termijn (verminderde kans op complicaties).

Doel

Het doel van het onderzoek is het ontwikkelen van de mobiele applicatie die diabetes patiënten kan ondersteunen. De ontwikkeling van de app bestaat uit meerdere subonderdelen. De verschillende app versies moeten bijvoorbeeld getest worden op usability, de coachingsmodule moet (verder) ontwikkeld worden en verschillende onderdelen van de app, zoals ziektebeleving en mogelijkheid tot een community, moeten nog vormgegeven worden.

Niveau: Bachelor/master

Supervisor: Dr. G. Laverman

UT-BSS,  ZGT Almelo, afdeling: nefrologie

E-mail: g.d.laverman@utwente.nl  of g.laverman@zgt.nl

Teachers:                       Ciska Heida

Student assignments:  BSc and MSc project assignments

Educational program:  BMT/BME, TG/TM, EE

Topics:                           Modeling and signal analysis

Introduction

Parkinson's disease (PD) is a long-term degenerative disorder of the central nervous system that affects the motor system. The cardinal symptoms are (rest) tremor, rigidity (muscle stiffness), and akinesia/bradykinesia (lack or slowness of movement, resp.). These symptoms result from the loss of cells in the substantia nigra, a region of the midbrain that produces dopamine, an essential neurotransmitter for relaying neuronal information that plan and control voluntary movements.

A number of phenomena occur in Parkinson’s disease (PD) that are related to tremor, which have not yet been combined into a single conceptual and/or computational model, but probably all involve basal ganglia and cerebellar circuitry:

-          (Rest) Tremor in PD is hypothesized to involve the basal ganglia as well as the cerebellum with the basal ganglia switching tremor on and off, and the cerebellar circuit modulating tremor amplitude (known as the dimmer-switch hypothesis).

-          Rest tremor is reduced/suppressed by voluntary movements.

-          Rest tremor disappears during sleep.

-          Rest tremor may respond to Levodopa medication, but this is not necessarily the case in all patients. Furthermore, while in a single patient rigidity and slowness of movement may respond well to Levodopa, tremor may not, which may suggest that tremor is not a direct effect of the dopaminergic deficiency.

-          Rest tremor responds to deep brain stimulation (DBS) in the STN and Vim.                                                                                

A) Schematic overview of the connections of the basal ganglia with the thalamus and cortex, and the cerebello-thalamo-cortical circuit. (Blue: inhibitory connections; Red: excitatory connections)  

B) DBS electrode in STN: continuous stimulation of the STN reduces tremor.

Assignments

-          Development of a computational model of the neuronal networks involved in central motor control and couple a number of the phenomena related to tremor at the neuronal circuit level.

-          Analyzing experimental data containing movement registrations performed by PD patients during different (movement) tasks that can be used to validate computational models of the mechanisms and neuronal circuits involved in tremor under different behavioural conditions.

Email contact: t.heida@utwente.nl