A MobileCoach app to support stroke patients during robot- assisted hand rehabilitation

Stroke survivors suffering from hand impairment are often severely limited in the execution of activities of daily living. At the Rehabilitation Engineering Laboratory, we have developed robotic devices to support hand and wrist rehabilitation in stroke survivors. With the objective of increasing the amount of therapy patients can get access to, we designed these systems so that they can be used in a minimally-supervised way, i.e., without direct supervision from a therapist, thereby making them suitable for use in the home environment. Unsupervised therapy, however, raises important challenges in terms of motivation to train, compliance to therapy program, and knowledge/feedback on progress and performance. For this purpose, we want to develop an interactive smartphone-based and chatbot-delivered coaching app that could act as an intermediate layer between patients and therapists. It could provide help to patients during their rehabilitation with the robot, e.g. by delivering motivational reminders of when they need to train, proposing questionnaires to rate therapy, or informing them more about stroke and the progress in their therapy. We expect that this app could improve motivation, performance and mood, which might positively influence the functional outcome of therapy. The overall objectives of this project are to develop a coaching concept for the users of a rehabilitation robot, and to implement a first prototype in the form of a mobile coaching app. A research on the state of the art will be conducted to identify important technical and behavioral aspects that will help support efficient therapy through the use of the app. These aspects will then be implemented in a mobile coaching app with the help of MobileCoach, an open source software platform, and in close collaboration with the Centre for Digital Health Interventions led by Dr. Tobias Kowatsch.

Stroke survivors suffering from hand impairment are often severely limited in the execution of activities of daily living. At the Rehabilitation Engineering Laboratory, we have developed robotic devices to support hand and wrist rehabilitation in stroke survivors. With the objective of increasing the amount of therapy patients can get access to, we designed these systems so that they can be used in a minimally-supervised way, i.e., without direct supervision from a therapist, thereby making them suitable for use in the home environment.
Unsupervised therapy, however, raises important challenges in terms of motivation to train, compliance to therapy program, and knowledge/feedback on progress and performance. For this purpose, we want to develop an interactive smartphone-based and chatbot-delivered coaching app that could act as an intermediate layer between patients and therapists. It could provide help to patients during their rehabilitation with the robot, e.g. by delivering motivational reminders of when they need to train, proposing questionnaires to rate therapy, or informing them more about stroke and the progress in their therapy. We expect that this app could improve motivation, performance and mood, which might positively influence the functional outcome of therapy.
The overall objectives of this project are to develop a coaching concept for the users of a rehabilitation robot, and to implement a first prototype in the form of a mobile coaching app. A research on the state of the art will be conducted to identify important technical and behavioral aspects that will help support efficient therapy through the use of the app. These aspects will then be implemented in a mobile coaching app with the help of MobileCoach, an open source software platform, and in close collaboration with the Centre for Digital Health Interventions led by Dr. Tobias Kowatsch.

- identify the requirements for mobile coaching in stroke neurorehabilitation; - adapt these requirements to best interface with a rehabilitation robot and to match the target user group (patients and therapists for remote monitoring); - develop a first version of the mobile coaching app; - test and evaluate key features of the app with healthy subjects; - if possible: conduct a pilot test with a few stroke patients.

- identify the requirements for mobile coaching in stroke neurorehabilitation;
- adapt these requirements to best interface with a rehabilitation robot and to match the target user group (patients and therapists for remote monitoring);
- develop a first version of the mobile coaching app;
- test and evaluate key features of the app with healthy subjects;
- if possible: conduct a pilot test with a few stroke patients.

- Literature research (20%) - Design and implementation of the coaching app (50%) - Test and evaluation (20%) - Report and presentation (10%)

- Literature research (20%)
- Design and implementation of the coaching app (50%)
- Test and evaluation (20%)
- Report and presentation (10%)

- Background in Computer Science, Software Engineering, Information Systems or related fields - Prior experience with programming (and in particular App programming, Java, Javascript/React Native) - ETH lectures "Digital Health (363-1130-00L)” and “Digital Health Project (363-1135-00L)” are advantageous - Independent worker - Interest in neurorehabilitation and interdisciplinary research

- Background in Computer Science, Software Engineering, Information Systems or related fields
- Prior experience with programming (and in particular App programming, Java, Javascript/React Native)
- ETH lectures "Digital Health (363-1130-00L)” and “Digital Health Project (363-1135-00L)” are advantageous
- Independent worker
- Interest in neurorehabilitation and interdisciplinary research

giada.devittori@hest.ethz.ch

giada.devittori@hest.ethz.ch