Bring Gait Lab to Everyday Life: Gait Analysis in Terms of Activities of Daily Living

With the development of the Internet of Things (IoT), wearable technologies have been proposed to measure gait parameters in everyday life. However, since both diseases and activities could influence gait patterns, clinicians cannot use the measured gait parameters for clinical applications without knowing the corresponding activities. To address this problem, a novel gait analysis method - 'gait analysis in terms of activities of daily living (ADLs)' - was proposed based on a wearable Smart Insole system. Twenty six gait parameters were extracted to realize a systematic gait analysis. Novel activity recognition algorithms based on characteristics of human gait were proposed to recognize ADLs, including 'sitting,' 'standing,' 'walking,' 'running,' 'ascend stairs,' and 'descend stairs' with high accuracy and low computation load. To evaluate the performance of 'gait analysis in terms of ADLs,' an experiment consisting of a sequence of different ADLs was designed to simulate the scenario of everyday life. In the result, gait parameters measured during different activities were automatically highlighted with different colors, which made it easy to see whether the gait pattern change was caused by activities or diseases. Besides, a refined gait analysis could be realized by individually extracting and analyzing the gait parameters of a specific activity. The results indicate that 'gait analysis in terms of ADLs' is a feasible method to reach the aim of bringing gait lab to everyday life.

Duke Scholars

Author Ming-Chun Huang DKU Faculty