Tracking Elite Swimmers in Real Time with Wearable Low-power Wireless Sensor Networks

Abstract

Monitoring elite athletes in real-time remains challenging. A lot of research has been conducted towards the application of inertial measurement systems for the quantification of performance in sports applications. The challenge however, remains in creating systems that meet high comfort standards in order to be able to apply them on a daily basis. The problem is mainly related to the requested volume of existing systems, which can be linked to the high power requirements and hence large batteries. This paper presents research towards the performance assessment of elite swimmers using wearable low-power sensor networks. This is the first work to focus on the reduction of power consumption by optimizing the power cost of signal processing and sensor use on a small and comfortable system. A built-in algorithm exploits the sensor data in order to predict which information is valuable to assess the motion of the athlete in real-time. Moreover, the algorithm is designed to run on a low-power microcontroller unit and uses lightweight computational techniques that further reduce the power requirements. By intelligently putting the sensor system into a low-power mode whenever possible, a reduction in sensor power consumption of 96% is estimated over minimally 80% of the swimming time, depending on the swimmer's lap times. Stroke types and turns were detected with high accuracy for each tested swimmer, despite different stroke techniques.