LSTM-Guided Coaching Assistant for Table Tennis Practice

Abstract

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

Sport Biomechanics Applications Using Inertial, Force, and EMG Sensors: A Literature Overview

“…They performed skill assessments that sought to differentiate between professional, subelite, and amateur badminton players just from their stroke performance. Similar approaches and methods were used in [30], where authors devised a system with three IMUs attached to the hand, wrist, and elbow of the athlete. The system employed deep learning methods for providing useful information to coaches in table tennis practice.…”

Evaluating Martial Arts Punching Kinematics Using a Vision and Inertial Sensing System

“…The purpose of this research is to use vision and inertial based sensing analysis to provide insight into the kinematics of martial arts punching techniques for the purposes of enhanced training through system feedback. Sensor-based coaching for sports has been a rising area of focus in recent years and the use of technologies in sports has become more widespread [ 6 , 7 , 8 ]. Accelerometers are also commonly used to observe properties of physical activity and provide important information about joint and body orientations [ 9 ].…”

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

“…Martin et al [ 30 ] presented a novel vision-based stroke classification system of Table Tennis with a new Twin Spatiotemporal CNN algorithm. Lim et al [ 31 ] developed a coaching assistant system of Table Tennis with three body-worn IMUs. LSTM networks (unidirectional and bidirectional) with probabilistic features were applied to classify the strokes.…”