STRETCHABLE STRAIN SENSOR FOR FULL HAND POSE RECOGNITION UNDER DEXTEROUS ARTICULATION IN DAILY ACTIVITIES
Abstract
The demographic shift of Malaysian population towards an older population increases the risk of stroke substantially, creating a heavy economic burden and high healthcare costs to the nation. Tele- rehabilitation greatly improves the quality of life for the stroke survivors. It allows the therapy to be conducted from home and is especially useful for patients with low mobility and in situations such as in COVID19, where people are encouraged to stay at home. The objective of this research is to develop a stretchable strain sensor and its machine learning algorithm for full hand pose recognition under dexterous articulation in daily activities for tele rehabilitation. The effectiveness and reliability of strain sensors depends on their sensitivity and tolerance to multiple stretching and release. The reliability of on-skin strain sensors will be investigated and a statistical model on its performance will be developed. The sensors with various materials will be tested and its stretchability and flexibility will be evaluated. Different substrates such as adhesive bandages will be used to evaluate its conformability to the skin. The statistical model will be developed based on the experimental measurements: resistivity, impedance, and tensile testing. For the full hand pose recognition, the data collected from the sensor will be pre-processed and the important features for the model will be extracted. The best machine learning algorithm will be determined for the full hand pose recognition using the sensor signals as the input and the hand pose as the output. The whole system will be integrated for tele-rehabilitation. Simulation and hardware experimental tests will be conducted to evaluate the performance of the complete system. The significance of this research is it will help the health service providers to deliver a better treatment to the patients so they may recover and regain their original hand function faster and more effectively.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 10, 2022
- Source ID
- FA23862114026XX49
Entities
People
- Norsinnira Zainul Azlan
Organizations
- Air Force Office of Scientific Research
- International Islamic University Malaysia
- United States Air Force