Real-Time Evaluation of Hands Position at Sport Training Machine

Abstract

The digitalization of sport training machines enables sensor-based applications for recognition of human movement at exercise performing. In this demo, we continue our development of the mobile application that uses evaluation of athlete's hands position in real-time. We show more effective solution (in terms of the position accuracy) than we demonstrated at the previous FRUCT conferences. Our previous solution is based on an accelerometer as a sensor for input data. Our successor solution combines an accelerometer and a gyroscope based on Kalman filter. This combination reduces the influence of acceleration on evaluating the angle of the lever of sport training machine relative to the vertical axis (the ``Bench Press'' exercise is used as a demo use case). The accurate measurement of hands position supports estimation of the total distance passed by hands (with given weight). This metric is important for training as well as for new class of sport competitions.

Keywords

• internet of things
• accelerometer
• mobile sensors
• data acquisition
• mobile application