An Athletic Training Analysis System Research Based on Physiological Computation

An Athletic Training Analysis System Research Based on Physiological Computation

Ding Xiong (Hunan International Economics University, Changsha, China), Lu Yan (Hunan International Economics University, Changsha, China) and Peng Qiong (Hunan International Economics University, Changsha, China)
DOI: 10.4018/IJHISI.2018040104

Abstract

This article is about how physiological status data is more important for athlete support training and competition. The Physiological Plan system is designed and implemented in this article, and the system is divided into the hardware layer, the data processing layer, the algorithm layer and the interface layer. The hardware layer adopts the Berkeley Tricorder platform. The data processing layer integrates the data of each sensor on the adapter mode. The algorithm layer includes a filtering algorithm, a peak detection algorithm and an Outlier detection algorithm. At the interface level, the coach interacts with the athlete, and the results are presented to the coach. In the system, the ECG, EMG and 3D acceleration of the athletes can be collected and analyzed at the same time, and the resultant data analysis are fed back to the coaches, these can solve problems in the athletes' physical data which cannot be collected and analyzed in real time. The experimental results show that the system can effectively assist the coaches in monitoring and analyzing the state of the athletes during training and competition.
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1. Introduction

Under physiological computing mode (Fairclough, 2009; Chen et al., 2015), the user does not need to actively input interactive information, the user's physiological state is collected and analyzed in real time through the system physiological sensing equipment, and these states are converted into control input, and feedback is provided to the user. Physiological Computing provides a new way for the perception of the user's physiological state and intention, a new research direction is opened up for human-computer interaction, but also a number of challenges is brought. Physiological computing technology can be used in a practical system, the following three key issues must be solved (Allanson and Fairclough, 2004; Wu et al., 2012): (1) physiological psychology theory and practical application; (2) physiological signal processing algorithms; (3) physiological computing software tools. How the physiological states of the user are collected and analyzed? How they are used in interactive system? These is a hot issue for current research.

The common used signals of physical calculation include EMG, ECG, blood oxygen saturation and skin impedance. The EMG can be used to perceive the muscle (Malmo and Malmo, 2000), and to identify the finger activity (Saponas et al., 2008) and to perceive the degree of user happy (Cacioppo et al., 1990). ECG signal is influenced by physical activity and mental stimulation (Porges and Byrne, 1992), it can be used to control the game (Calvert and Tan, 1994) and to understand the psychological state (Blascovich et al., 1999).

Physiological computing has made some important progress in the aspect of system development. Björn Hartmann developed the exemplar to help users in a sensor interaction (Hartmann et al., 2007). The model programming method is used in exemplar, it supports image programming, it is convenient to man-machine interaction of non-machine learning experts. Similarly, Daniel Ashbrook developed a gesture design tool MAGIC based on action of the sensor (Ashbrook, 2010), the dynamic time warping (DTW) is used as a gesture recognition algorithm, designers are helped faster and more convenient to use the interaction based on sensor device.

Car Coach project provide computer aided adaptive based on user driven behavior modeling (Arroyo et al., 2006); Human Dynamics research team has developed a set of known as the “I Sensed” system(http://cba.mit.edu/events/02.01.retreat/Pentland.ppt), a set of wearable devices is used in the system, which contains a variety of sensors, the daily behavior is recorded, and then the analysis and modeling of the sensor data are made, and it can be able to predict the user's daily behavior. Carnegie Mellon University's human computer interaction Institute researchers studied how the computer system can identify the user state as people, the user is interrupted appropriately in a natural way (Fogarty et al., 2005). The multi-channel sensing technology is used based on sensor when the user's attention is are obtained. Under the guidance of Gregory D. Abowd professor in Georgia Institute of technology, the psychology data modeling and medical assistance of mentally handicapped children were studied deeply. Abaris is a therapy system for supporting clinicians in autistic children psychological intervention (Kientz et al., 2005).

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