Framework for Intelligent Swimming Analytics with Wearable Sensors for Stroke Classification.

Joana Costa,Miguel Santos,Catarina Silva,Telmo Fernandes,Sérgio Faria

doi:10.3390/s21155162

Abstract

Intelligent approaches in sports using IoT devices to gather data, attempting to optimize athlete’s training and performance, are cutting edge research. Synergies between recent wearable hardware and wireless communication strategies, together with the advances in intelligent algorithms, which are able to perform online pattern recognition and classification with seamless results, are at the front line of high-performance sports coaching. In this work, an intelligent data analytics system for swimmer performance is proposed. The system includes (i) pre-processing of raw signals; (ii) feature representation of wearable sensors and biosensors; (iii) online recognition of the swimming style and turns; and (iv) post-analysis of the performance for coaching decision support, including stroke counting and average speed. The system is supported by wearable inertial (AHRS) and biosensors (heart rate and pulse oximetry) placed on a swimmer’s body. Radio-frequency links are employed to communicate with the heart rate sensor and the station in the vicinity of the swimming pool, where analytics is carried out. Experiments were carried out in a real training setup, including 10 athletes aged 15 to 17 years. This scenario resulted in a set of circa 8000 samples. The experimental results show that the proposed system for intelligent swimming analytics with wearable sensors effectively yields immediate feedback to coaches and swimmers based on real-time data analysis. The best result was achieved with a Random Forest classifier with a macro-averaged of 95.02%. The benefit of the proposed framework was demonstrated by effectively supporting coaches while monitoring the training of several swimmers.

Highlights

Ubiquitous sensing opportunities are kindling interest in many areas, where improving performance in sports is a paradigmatic example
Considering only the 1 s feature representation, the one that presents the best results regarding stroke classification, Random Forest seems to be the best classifier when using ensembles, which might be related to the robustness of the Random Forest algorithm itself
The system is supported by wearable inertial sensors and biosensors, which are attached to the swimmer’s body and communicate to the base station through a radiofrequency system

Summary

Introduction

Ubiquitous sensing opportunities are kindling interest in many areas, where improving performance in sports is a paradigmatic example. Different approaches for developing wearable sensors for sports are being sought [1,2,3,4]. Initial methodologies were developed essentially for monitoring purposes, and initial approaches employing intelligent systems in sports analytics, namely in swimming, have neglected the possibility of using wearable sensors, focusing essentially on statistics or crowdsourced data [5] and recorded video [6,7,8,9]. The opportunity to integrate intelligent pattern recognition techniques and prediction ability brings an enormous potential to cope with more ambitious challenges, namely to achieve athlete performance improvements through intelligent analytics [10,11,12]. A robust and efficient intelligent system that can support coaches’ decisions in real time must be conceived

Methods

Results

Discussion

Conclusion