An adaptive complementary filter for inertial sensor based data fusion to track upper body motion

TLDR: In this article, an adaptive complementary filter and inertial measurement sensors are used to identify human upper arm movements. And the proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system.

Abstract: Remote human activity monitoring is critical and essential in physiotherapy with respect to the skyrocketing healthcare expenditure and the fast aging population. One of frequently used method to monitor human activity is wearing inertial sensors since it is low-cost and accurate. However, the measurements of those sensors are able only to estimate the orientation and rotation angles with respect to actual movement angles, because of differences in the body's co-ordination system and the sensor's co-ordination system. There were numerous studies being conducted to improve the accuracy of estimation, though there is potential for further discussions on improving accuracy by replacing heavy algorithms to less complexity. This research is an attempt to propose an adaptive complementary filter for identifying human upper arm movements. Further, this article discusses a feasibility of upper arm rehabilitation using the proposed adaptive complementary filter and inertial measurement sensors. The proposed algorithm is tested with four healthy subjects wearing an inertial sensor against gold standard, which is the VICON system. It demonstrated root mean squared error of 8.77° for upper body limb orientation estimation when compared to gold standard VICON optical motion capture system.