Indexed by:会议论文

Date of Publication:2016-01-01

Included Journals:CPCI-S、SCIE

Page Number:644-649

Key Words:wearable sensors; phase detection; motion measurement; machine learning; sport monitoring

Abstract:This paper presents a monitoring system (CanoeSense) for canoe motion based on wearable Body Sensor Networks (BSNs). An effective motion segmentation method was applied to competitive sport, which can segment human motion phases automatically based on raw time series data that was acquired through wearable Inertial Measurement Units (IMUs). Orientation estimation algorithm was adopted to measure the attitude information of athletes' stroke motion of the canoe. By fusing the data of motion phases and attitude changes, the monitoring data may provide coach with a new performance monitoring method for improving coordination motions of two partners or adjusting the training plan in time. The experimental results showed that our system is able to simultaneously monitor motion phases and attitude changes of two athletes during training on the water.