Energy-Efficient Wearable EPTS Device Using On-Device DCNN Processing for Football Activity Classification

Abstract

This paper presents an energy-optimized electronic performance tracking system (EPTS) device for analyzing the athletic movements of football players. We first develop a tiny battery-operated wearable device that can be attached to the backside of field players. In order to analyze the strategic performance, the proposed wearable EPTS device utilizes the GNSS-based positioning solution, the IMU-based movement sensing system, and the real-time data acquisition protocol. As the life-time of the EPTS device is in general limited due to the energy-hungry GNSS sensing operations, for the energy-efficient solution extending the operating time, in this work, we newly develop the advanced optimization methods that can reduce the number of GNSS accesses without degrading the data quality. The proposed method basically identifies football activities during the match time, and the sampling rate of the GNSS module is dynamically relaxed when the player performs static movements. A novel deep convolution neural network (DCNN) is newly developed to provide the accurate classification of human activities, and various compression techniques are applied to reduce the model size of the DCNN algorithm, allowing the on-device DCNN processing even at the memory-limited EPTS device. Experimental results show that the proposed DCNN-assisted sensing control can reduce the active power by 28%, consequently extending the life-time of the EPTS device more than 1.3 times.

Real-Time Littering Activity Monitoring Based on Image Classification Method

“…The recent research on human activity recognition and classification that was conducted by the researchers have been applied in many parts, for instance as presented in Table 1. The usage of the EFTS and IMU in paper [13] helped the authors to analyze the activities of the football players, such as remaining stationary, walking, jogging, running, slow turning, and fast turning. In references [14,15], the author conducted research by detecting the fall by using Channel State Information (CSI) to recognize the activity of falling [14] and used wearable sensors, such as an accelerometer and a guroscope to detect the fall [15].…”

The Design of GNSS/IMU Loosely-Coupled Integration Filter for Wearable EPTS of Football Players

“…Motion analysis becomes important for improving athlete performance and reducing athletes’ injury risk. IMU (Inertial Measurement Unit) sensors which consist of three-axis accelerometers, three-axis gyroscopes, and three-axis magnetometers have been used to estimate and provide the attitude, position, and velocity of athletes [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. The head or foot injuries of sports players can be monitored by analyzing G-impacts and reaction forces using the measured acceleration data from IMU sensors [ 13 , 14 , 15 ].…”

“…The different IMU sensor positions can be possible to provide various physical load estimates of athletes and analysis the motion of athletes, i.e., football players movement intensity information [ 16 ], runners’ stride length and stride velocity, analysis at ground contacts [ 17 ], postural demands of professional soccer players [ 18 ], velocity measurements for team sports [ 19 ], and the analysis of foot swing at football kicks [ 20 ]. Deep learning techniques using IMU sensor information were also used to classify football activities [ 21 , 22 , 23 ].…”

“…Many studies on sensor technology have been performed in sports science [ 13 , 14 , 15 , 16 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 47 ], but few on GNSS/IMU integration have been performed [ 21 , 47 ]. In this study, we investigate the loosely-coupled integration of GNSS/IMU for a wearable EPTS with football players because most commercial wearable EPTS systems do not provide GNSS raw measurement data (e.g., pseudorange).…”