Wearable Sensor System to Measure Velocity Adaptive Variability for Continuous Human Mobility Monitoring

Abstract

Variability of human mobility has become an important identifier for the assessment of human motor performance. For example, abnormally increased variability during movement has shown to correlate with higher falling risk. Various gait parameters, such as step length, stride time, and joint angle velocity have been studied to reveal the link between variability and movement impairment under the hospital or laboratory environments. Although the accuracy of the measurements with the laboratory equipment is relatively high and reliable, spatiotemporal limitation and lack of representativeness of ordinary mobility characteristics of a subject have been major challenges of previous approaches. This study proposes the velocity adaptive variability parameter to overcome the listed limitations. Among several major factors that affect level of variability, such as kinematic, pathological, and physiological changes, the parameter specifically absorbs the impact of varied walking speeds to get an instinct variability signature from the same subject regardless of walking speed. Since we utilize a single inertial sensor to measure variability of the subject, the approach will enable us to continuously monitor mobility-related problems in a free-living environment. The proof of concept experiment has shown practical advantages of our approach, and we also expect that the adaptive variability can be applied to future continuous mobility monitoring research.