rehabilitation of stroke patients with sensor-based systems
Jeremia Held is a PhD student in the research group Biomedical Signals and Systems. His supervisors are prof.dr.ir. P.H. Veltink from the Faculty of Electrical Engineering, Mathematics and Computer Science and prof.dr. A.R. Luft from the University Hospital Zürich.
Stroke is the third cause of long-term disability worldwide and the most common cause of disablement. Common, persisting disabilities are upper and lower extremity deficits, cognitive dysfunction, incontinence, and speech difficulties. Around 80% of stroke patients experience a unilateral motor deficit, which limits functionality and engagement in social life. Stroke patients require assistance for various activities of daily living and receive rehabilitation services to treat these disabilities.
Stroke rehabilitation is complex because of the varieties of brain lesions and diversity of physical and psychological issues. To tackle the complexity of post-stroke characteristics, it is important to assess the patients, set realistic goals, execute interventions, and reassess patients’ abilities.
Stroke patients are assessed in a laboratory environment, where patients are encouraged by the therapist to perform predefined tasks. To measure patients’ actions in their daily lives, clinicians and researchers traditionally rely on semi-structured interviews. Sensor-based technologies have been developed to objectively measure daily life activities and to allow for the continuous monitoring of daily life performance. Currently, it is not known how patients’ performance, objectively measured by sensor-based systems during daily life, match and complement standard clinical assessments.
Interventions in stroke rehabilitation are well organised in a clinical setting within the first weeks after a stroke. However, the interventions after discharge are also important to improve performance and prevent deterioration in functioning. As a first step to prevent deterioration, sensor-based devices combined with feedback modalities are developed to motivate stroke patients to use and train their affected extremities more in daily life.
The present thesis focuses on the application of sensor-based systems in stroke rehabilitation. The objectives of this thesis are: 1) to evaluate a sensor-based system that can quantify stroke patients’ upper limb activities in the rehabilitation clinic and in their home environment (Chapter 2); and 2) to evaluate the usability and efficacy of sensor-based systems with feedback modalities for stroke rehabilitation interventions that can be used in the patients’ home environment (Chapters 3-7).
To assess the stroke patients, a sensor system called ‘INTERACTION’ was developed to explore parallels between standard clinical assessments (i.e. capacity) and daily-life measures (i.e. performance) in an observational study (Chapter 2). The measurements were performed in the rehabilitation clinic and in the patients’ home environment. Newly developed metrics for classifying the activity and quality of upper extremity movement were applied. Their arm motor function, measured with standard clinical assessments, improved during the inpatient rehabilitation but declined in the first four weeks after discharge. Despite this deterioration in the clinical assessments, patients increased the number of reaches they performed of the affected side during daily life in their home environment. The metrics derived from the sensor system are likely to be more sensitive to change than clinical assessments. Furthermore, this study revealed that the motor function measured with clinical assessments did not reflect patients’ behaviour in relation to their impaired upper limb during activities of daily living. Stroke patients who score well in clinical assessments do not necessarily use their paretic arm in daily life.
For stroke rehabilitation intervention, a sensor-based system combined with vibrotactile feedback, the ‘Arm Usage Coach’, was tested with patients in a laboratory environment (Chapter 3). The system aimed to influence arm use by monitoring performance and providing real-time feedback. The acceptance and usability were evaluated. Most patients found the vibrotactile feedback intuitive, agreeable and reported that the system could complement their current therapy.
The findings of Chapter 3 led to the development of a new sensor-based feedback system, the ARYSTM me (Yband Therapy AG, Reinach, Switzerland), which is described in Chapter 4 and is now being tested in a randomised clinical trial. The system monitors arm use in daily life and coaches stroke patients to increase use of their paretic arm in daily life with an integrated feedback system. The ongoing randomised controlled trial aims to test the efficacy of the feedback system in stroke patients who have completed their clinical rehabilitation. Results of this study will positively influence the development of sensor-based feedback systems for rehabilitation of real-world arm use in patients’ homes, beyond interventions in rehabilitation clinics.
Technologies for in-home interventions, including structured exercises, are being developed and evaluated to promote recovery without increasing demands on therapists’ time. Two feasibility studies (Chapters 5 and 7), the ‘ArmeoSenso’ for the upper extremity and the ‘REWIRE’ for balance and gait, evaluated the use and safety of self-directed interventions in stroke patients’ homes without supervision of a therapists. Compliance with using these self-directed, sensor-based systems was high in patients who had mild-to-moderate upper extremity and gait impairments. Patients with severe disabilities tended to not use these systems. In addition, there were no safety issues in using the self-directed, sensor-based systems.
To evaluate the efficacy of the performance feedback and reward provision, an adapted sensor-based ‘ArmeoSenso’ system is currently being investigated in a randomised controlled trial described in Chapter 6. The results of this trial could emphasise the role of reward in stroke rehabilitation. With this knowledge, therapists and healthcare providers will be able to adapt strategies to diminish stroke patients’ disabilities.
The use of new rehabilitation technologies, such as sensor-based systems, have made it possible to monitor motor function and observe stroke patients’ behaviour in their daily lives. Furthermore, these technologies can help measure function and activities in rehabilitation trials and profile the patients’ recovery. In addition, sensor-based systems for stroke rehabilitation interventions allow training in the patients’ home environments, thereby increasing the specificity of the therapy. As such, they can facilitate the integration of therapy into stroke patients’ daily lives.
Nevertheless, the presented and tested sensor-based methods need to be further investigated in terms of efficacy as well as the carryover of gains achieved in clinics for mildly to moderately affected stroke patients’ performance in their daily lives.