Multi-sensors for realization of home tele-rehabilitation - Seanglidet, Yean (Nanyang Technological University, 2020)
Research in assistive healthcare, in particular home rehabilitation, has spawn huge potential owing to the recent advancement of internet-of-things technology and the wearable hardware, Inertial Measurement Unit (IMU) in wearable sensors and smartphones become a affordable for community usage. However, using low cost IMU sensors or smartphones face certain challenges, such as accurate orientation estimation for lower-limb motion tracking, which is usually less of a problem in specialized motion tracking sensor devices. To address these issues, the candidate has made three main contributions: a new and better orientation estimation algorithm which combines quaternion-based Kalman filter with corrector estimates using gradient descent (KFGD), an auto-detector of post-filtered lower-limb orientation signal oscillation and the machine-learning based state identification of rehabilitation exercise.