Motivation:

1. “Development of a wearable computing device to detectspasms and log information to support therapy”Harald STIXMarkus MARTIN

2. Motivation: • Cerebral Palsy = disability in various areas of body • movement • Develop a sensor based device, to help/assist people • suffering from cerebral palsy • Alert patients of occurring spasms • Learning approach to deal with spasms

3. Expected Outcome: • Device that detects spasms, ... • ... alerts the user... • … provides feedback … • … and keeps track of when spasms occur • Resulting in: • A neuroplastic training effect

4. Splitting: • Markus MARTIN: • Theoretical Issues: • Spasms • Wearable devices • Neuroplasticity • Existing assistive devices • Practical Issues: • Design of the device • Implementation of the device • Evaluation of the device

5. Splitting: • Harald STIX: • Theoretical Issues: • Sensors • Feedback devices • Transmitting Data • Smart phone applications in health care • Practical Issues: • Evaluation of sensors • Programming of the sensors • Programming of the logger

6. Methodical approach: • Theoretical part consisting of: • An introduction to spasms • Current state of wearable computing • Neuroplasticity • Existing assistive technology • Sensors • Feedback Devices • Transmitting Data • Smart phone applications in health care

7. Methodical approach: • Qualitative interviews with: • An affected person: T. • T.’s father • A therapist

8. Methodical approach: • Combine the information gained from: • The literature research • The interviews • Create a specification for the device. • Build a first Proof of Concept Prototype.

9. Specification:

10. Proof of Concept Prototype: • Main parts: • Flex – Sensor • Arduino Uno • Bluetooth Mate • Android smartphone

11. Proof of Concept Prototype: • Logging: • Android App as Logger • Logging combinedwithvisual feedback tomotivate the user • Tactile or audible feedbackto alert the user

12. Proof of Concept Prototype:

13. Proof of Concept Prototype:

14. Building a prototype – Hardware: • Evolving the proof of concept prototype to a wearable device included the following steps: • Using smaller hardware • Arduino pro

15. Building a prototype – Hardware: • Evolving the proof of concept prototype to a wearable device included the following steps: • Using a battery • Step-up-boost • Charging unit

16. Building a prototype – Hardware: • Evolving the proof of concept prototype to a wearable device included the following steps: • Using a FTDI adapter

17. Building a prototype – Hardware: • Evolving the proof of concept prototype to a wearable device included the following steps: • Soldering the equipment

18. Building a prototype – Hardware: • Evolving the proof of concept prototype to a wearable device included the following steps: • Using a case for the hardware

19. Building a prototype – Hardware: The finished prototype

20. Building a prototype – Hardware evaluation with T.: • According to T. two components of the glove had to be redesigned: • The glove • The case

21. Building a prototype – Hardware evaluation with T.: Redesigned glove:

22. Building a prototype – Hardware evaluation with T.: Integrated hardware:

23. Building a prototype – Software: • Requirements: • Simple User Interface • Big Buttons • Switching between different Feedback modes • Independency of environment • Motivational System

24. Building a prototype – Software: • Solution: • Calibration to adopt the software to environment • Achievements to reward the user • Level system to enhance the learning effect • Logging of all events to enable analysis • Connection between phone and device via Amarino

25. Building a prototype – Software: • Software evaluation: • Detection of spasms works • Improvements after the user got used to the device • Logging system can replace the achievement system • User does not use the phone himself • User wants statistical data after the usage

26. Prototype of the spasm glove:

27. Findings 1/3: • Inconspicuous, light and small device for detection ofspasms in the wrist. • • By using the glove cramps can be detected almost error-free. • • Reduced number of occurred seizures. • • Weaker and much easier to resolve spasms.

28. Findings 2/3: • The operation of the application is easy to learn, also for users with motor difficulties. • • The feedback is clearly visible and allows the user to immediately focus on the convulsion. • • The detection of seizures can be configured easily and can be adapted to the needs of multiple users.

29. Findings 3/3: • The application stores all information about occurringcramps. This information can be used for an analysis by the therapist. • • In addition to the information about the cramp otherimportant information can be stored for example the activity the user has performed during the convulsion.

30. Thank you for your attention! Harald STIX Markus MARTIN