Reducing power and increasing accuracy of on-body sensing in motion capture application.

Motion capture coupled with on-body sensing and biofeedback are key enabling technologies for assisted motor rehabilitation. However, wearability, power efficiency and measurement repeatability remain the principle challenges that need to be addressed before widespread adoption of such systems becomes possible. The weight and the size of the on-body sensing system needs to be kept small, and the system should not interfere with the user's movements or actions, but in general they are bulky due to their power consumption requirements. Furthermore, on-body sensors are very sensitive to positioning, which causes increased variability in the motion data. Isolating the characteristic patterns that represent the most important motion data affected by random positioning errors, while also reducing the power consumption, is the authors' main concern. An automated computational approach is considered to address the two problems. The use of functional principal component analysis is investigated for signal separation, whilst accounting for variability in the sensor position. To generate motion data, movements of human subjects and a robot arm are captured. As joint angles are considered in the analysis, the results are independent from the technology used to measure motion. The proposed post-processing technique can compensate for uncertainties due to sensor positional changes, whilst allowing greater energy efficiency of the sensors, thus enabling improved flexibility and usability of on-body sensing