Human Bio-kinematic Parameter Estimation using Inertial Sensors

Abstract

This is an attempt to accurately capture human bio-kinematic parameters for physical tele-rehabilitation using measurements from inertial sensors. The contributions can be classified into three categories: accurately capturing human kinematics despite intrinsic uncertainties omnipresent with human movements, improving the tracking accuracy by correcting the sensor misalignment error and assessing rehabilitation exercises quantitatively or qualitatively in a systematic way for evaluating the progress of people with disabilities. Firstly, a dynamic model for human kinematics is proposed and different data fusion algorithms are applied to fuse inertial sensor measurements for obtaining accurate movement angles. Specially, a novel robust extended Kalman filter with linear measurements (REKFLM) is proposed to improve accuracy in estimated angles. Secondly, a sensor misalignment calibration method is proposed. In addition, a method for estimating the limb's length for assessing a common musculoskeletal disorder called Limb Length Discrepancy is proposed. Importantly, these two methods are proposed considering the curvature in limb trajectories which has not previously used in similar problems. The qualitative and statistical analyses for trunk movements are conducted to distinguish Parkinson's patients from healthy subjects. Finally, these advancements led to a prototype of a mobile cloud-based physical telerehabilitation system for motion capturing and evaluation of patients. This prototype is developed in the web cloud to facilitate convenient access to patients using mobile devices. A multi-level encoding scheme is proposed to avoid limitations of mobile and sensor devices to ensure reliable and efficient rehabilitation services.