SUPERVISOR: DR. MARTIN SCHMETTOW (2-3 students)
Minimal invasive surgery (MIS, keyhole surgery) is one of the most important developments in surgery during the past decade. In the past and present, MIS training followed the apprenticeship model, where trainees watch a hundred or so procedures, before putting their own hands on a patient. With the emerge of virtual reality training simulators this is soon going to change. This new technology is promising as it provides safe and highly repeatable training opportunities, as well as assessment of a person’s capabilities.
Minimally invasive procedures are quite demanding:
- there is no perception of depth
- the procedure often require counter-intuitive movements
- haptic feedback is limited
Research has shown that doctors differ in how fast they acquire the necessary skills, and the proficiency they reach in the long term. Figure 1 shows three example learning curves.
Figure 1. Learning curves showing how efficiency of motions improve in laparoscopy.
These differences can partly be explained by individual differences between doctors, such as amount of training and cognitive capabilities, like visual-spatial processing ability.
The research field has long been on the quest for assessing such abilities reliably as to predict whether someone is likely to become a good MI surgeon.
MIS simulators also have great potential during the process of training. In every simulator session an abundance of performance variables are recorded (e.g., motion efficiency, see Fig 1). It is compelling to use such data to track the training progress of individual trainees. Furthermore, using learning curves, it may be possible to predict ahead of time, what performance level a trainee is going to reach (for example, participant 06 will reach a much better level than 03). In the future it may be possible to use learning curves from simulator data to select high potentials for a training program.
In this bachelor project you will set up a suite of simulator tasks and run a learning experiment on novice participants. Based on the results you will draw conclusions on designing simulator based trainings and simulator-based assessment. During your project you will work as in a team with other students and in close collaboration with Marleen Groenier from Technical Medicine.
SUPERVISOR: DR. MARTIN SCHMETTOW (3 students)
Imagine the year 2035. Fully automatic cars have become so common in the industrial countries that nobody actually takes driving lessons anymore. That is a very comfortable situation, unless you to travel to a less-developed country and want to move around, freely. You cannot drive and there is no-one around anymore to teach you.
Our prediction is that by then, simulator-based drivers training will be dominant; and we start our research now about how to design efficient driver trainings.
With this pilot experiment, we want to transfer our key insights from another domain where simulator-based training is on the verge, minimally invasive surgery. One important insight is that if the training situation is without risks, the training can be designed more freely to optimize the learning. For example, we found evidence that speed episodes (where safety is ignored) improves learning a lot.
On the newly acquired driving simulator of the BMS Lab, you will run a training experiment, where you compare different training conditions on how they affect the training process.
This assignment is suited for a team of three bachelor students.
Interested? Send an email to firstname.lastname@example.org
SUPERVISOR: DR. SIMONE BORSCI (2 students)
Innovation design needs the support of system thinking and the development of systematic approaches and tools to assess in a reliable way innovation and drive the product development toward the delivery of satisfactory efficient and effective tools.
Your thesis will deal with one or more case scenario (provided by industries), as a product to explore and develop evaluation approaches for complex interaction settings. The scenarios will include, for instance, systems for the interaction “in the wild”, virtual reality environments, and/or AI-based tools.
On the basis of the scenarios you will select, your work will consist of at least the following aspects:
- identify in literature, by a systematic review (PRISMA approach) reliable approaches to test the specific system.
- Build specific expectations or hypotheses regarding methods for assessment in the specific context and define criteria to compare different methods of evaluation and generalise results.
- Evaluate the innovation and proposed solutions to the identified gaps.
- Report about generalizable advantages and disadvantages of different evaluation methods.
SUPERVISOR: DR. SIMONE BORSCI (3 students)
Every day people use multiple technologies to perform complex tasks, such as buying products online, informing their decision making, or supporting their work activities. Several independent pieces of evidence in literature converge on the idea that multiple elements affect people’s expectations toward the use of technology, including individual attitudes, skills and capabilities and technology-related aspects, such as: product’s aesthetics and usability perceived before the use, fluency, brand and price etc.
In many cases, (high risk) processes are dependent on the technology to deliver the appropriate service. It is perhaps reasonable to assume that the implicit agreement of this technology-driven world is that: people trust the technology they are using to perform task and decision making in terms of: performance, functionalities and reliability of outcomes. Trust towards technology does not happen immediately, but rather, it is built throughout the relationship between user and artefacts. This is a set of beliefs about a product’s characteristics – i.e., functioning, reliability, safety, etc. And it derives from the gained experience of people in the use of different technologies over time. User’s overall trust is, therefore, strongly related to the concept of user experience, i.e., experience with (and the exposition to) different products enable people to develop a set of general attitudes and beliefs toward those technologies, including the overall trust.
Your work will build upon previous studies, and you will be involved in an ongoing study on trust by applying an experiment already designed in PsychoPy to a large population.
You will gather data regarding the mechanism of trust, and you will be invited to develop your own hypotheses in tune with your analysis of the literature and your personal interest.
SUPERVISOR: PROF. DR. WILLEM VERWEY
Since the introduction of automated driving (AD) on public roads it has become clear that the subject is so complicated that in order to achieve full automated driving (SAE-5, or 4) we have to bridge a development phase where a human ‘driver’ in the car is needed in order to intervene in traffic situations that the AD system cannot master safely.
The I-AT project (www.I-AT.eu) has developed a new automated shuttle, called Mission, that is the first of its kind to feature a dedicated work environment for the automated shuttle driver. It has a standard driver seat with a full set of driving controls plus three additional ways to switch of the AD system and take back vehicle control. In total there are five ways to take back control: brake pedal, steering wheel, 2 switches in the software layer + 1 hard wired ‘safety switch’. These five switches are distributed over the area the driver can reach.
The Mission bus will be ready by the end of 2019 to commence automated driving on test tracks. The project has to prove that the driver-take-over procedure can be realised within 1 second and 70 cm lateral displacement of the vehicle at 30km/hr. For this purpose, a system will be made to induce navigation faults at random moments.
The driver take-over will be demonstrated during an event on March 10-12. Further test results of the driver take over procedure can be presented at the seminar that will be organized at the end of the I-AT project in June 2020.
- Can the driver take-over be realised within 1 second and 70 cm lateral displacement of the vehicle at 30km/hr?
- How reliably can this be repeated and how large is the deviation between different drivers?
- How do the five alternative driver take-over switches compare in performance? Which of the five switches works fastest and best?
- How much does this vary between persons and does performing this procedure require special skills? To what extend does training improve performance?
- The driver is required to have his hands on the steering wheel, his foot near the brake and his eyes on the road. The research can be extended with performance measurements of a driver that is less attentive.
Measurements and report need to have scientific quality.
- The Mission bus is available on only two locations: Vliegkamp Valkenburg in Katwijk (feb) and the automotive test centre in Aldenhoven near Aachen (March) and on either of these two locations after mid-March.
- Research has to be matched with the AD test program of the Mission bus that runs until mid-March.
- Demonstration for selected guests on Aldenhoven test centre on March 10-12 – POC and presentation of first results.
- Preparations for this demonstration might include testing with professional bus drivers of ASEAG, the Aachener public transport bus operator.
- Possibility for extended testing in the period mid-March – May.
- Presentation of, more extended, test results at the I-AT seminar in June 2020.
Note: given that the study is carried out at a test track, the student is expected to be able to drive there.
SUPERVISOR: FRANCESCO WALKER; PROF. DR. WILLEM VERWEY
Trust is one of the main factors slowing down the adoption of automated driving technology. This study aims to improve trust by tailoring automated vehicle (AV) behaviour to each user. In the driving simulator of the UT, while being driven by an AV, participants will continuously report how much they trust its behaviour. They will indicate their trust through a slider, with “0” indicating “No trust” and 100 indicating “Full trust”. An alert will be sent to the experimenter if, for 30 seconds straight, the participant’s trust will be 10% below or above the previously reported value. The experimenter will decrease or increase AV’s speed by 5 km/h every time an alert is received. We hypothesize that, when compared to two control groups (Control1: speed stays constant; Control2: speed randomly changes), adapting vehicle’s speed to the user’s trust will lead to higher trust levels in individuals that tend to distrust automation.
If you want to know more about this project, please send an email to Francesco (email@example.com).
SUPERVISOR: MARLISE WESTERHOF (1-3 students)
With the rising concern about climate change, Electric Vehicles (EVs) are seen as a promising way to reduce carbon emissions. However, although the amount of electric vehicles (EVs) on the European roads is increasing, there is still a long way to go to achieve the ambitious climate goals such as 30% market share for EVs in 2030 (EAFO, 2019). Limited range is one of the major barrier towards the acceptance of EVs among consumers (Biresselioglu, Demirba, & Yilmaz, 2018). Also among EV-drivers is range a topic of discussion, since many EV-drivers experience range anxiety: the fear of not reaching one’s destination due to insufficient range of the EV (Rauh, Franke, & Krems, 2015).
To encourage an EV-driver to drive more energy-efficient and thus maximize the vehicle’s range, electric vehicles are provided with various eco-driving assistance tools which present information to reduce energy consumption. Examples of such eco-driving tools are displays providing real-time feedback about the driving efficiency or showing a post-ride summary. These eco-driving assistance systems contribute to environmental sustainability, but on the other hand might distract drivers and thus interfere with safe driving.
In this bachelor assignment, you will conduct experiments using a driving simulator. The study investigates whether and which effects the focus on energy-efficient driving has on driving behaviour in electric vehicles. For example, two different designs of displays showing energy-efficiency in an electric car could be compared based on workload.
3 students will work on this project, each having his/her own research topic. Example topics for research questions are range anxiety, workload, trust in range estimation, actual driving behaviour etc.
Bingham, C., Walsh, C., & Carroll, S. (2012). Impact of driving characteristics on electric vehicle energy consumption and range. IET Intelligent Transport Systems, 6(1). doi:29-35. 10.1049/iet-its.2010.0137
Biresselioglu, M. E., Demirbag Kaplan, M., & Yilmaz, B. K. (2018). Electric mobility in Europe: A comprehensive review of motivators and barriers in decision making processes. Transportation Research Part A: Policy and Practice, 109(October 2017), 1–13. doi:10.1016/j.tra.2018.01.017
EAFO, https://www.eafo.eu/vehicles-and-fleet/m1, accessed on 15-12-2019
McIlroy, R. C., Stanton, N. A., & Harvey, C. (2013). Getting drivers to do the right thing: a review of the potential for safely reducing energy consumption through design. IET Intelligent Transport Systems, 8(4), 388-397. doi: 10.1049/iet-its.2012.0190
Rauh, N., Franke, T., & Krems, J. F. (2015). Understanding the impact of electric vehicle driving experience on range anxiety. Human factors, 57(1), 177-187. doi: 10.1177/0018720814546372
SUPERVISOR: PROF. DR. JAN MAARTEN SCHRAAGEN (2 students)
Performing high-risk and high-stake tasks requires a team that is able to collaborate effectively and efficiently. Margins for error are often small, and time is critical, putting great demands on the work environment to optimally support the team.
This type of work environment, often a control room, is carefully tailored to the type of work, and the size and composition of the team. An optimally designed work environment can enable and support collaboration and increase team performance.
At TNO we are conducting ongoing applied research on the best practices regarding collaboration.
One aspect that is considered is the inter-personal distance between team members. Certain team members may have to interact closely and even monitor each other’s screens. While for others it may be acceptable to be within a line of sight, or even at walking distance.
We are now taking on two bachelor students who may do a joint research project on the optimal inter-personal distance for close collaboration. While this is closely related to the study of ‘personal space’, the focus of this study is on determining a ‘good’ distance to comfortably enable good collaboration. For instance, at which distances do people no longer feel like a unified team?
You will work with researchers at TNO in Soesterberg in answering these questions by exploring current literature on inter-personal distance when collaborating face-to-face, and how this relates to collaborating in mixed/virtual reality. We would also be interested in conducting an empirical study to validate the existing literature within a specific collaboration context, seeing how this affects work-processes taking place, and exploring whether these effects can be observed and evaluated by using Immersive Environments as well.
Due to the nature of the work and the security regulations at TNO, applicants should be prepared to submit a Verklaring Omtrent Gedrag (NL) or the equivalent from their country of citizenship. Only European citizens can apply.
Bailenson, J. N., Blascovich, J., Beall, A. C., & Loomis, J. M. (2003). Interpersonal Distances in Virtual Environments. Personality and Social Psychology Bulletin, 29(7), 819–833. https://doi.org/10.1177/0146167203253270