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Optimization of Sports Training Systems Based on Wireless Sensor Networks Algorithms

Published in : IEEE Sensors Journal (Volume: 21, Issue: 22, November 2021)
Authors : Jun Yang and Wu Lv
Summary Contributed by:  Anupama

The continuous development of micro-electromechanical technology has created a revolution in the application of sensors. The researchers developed a Wireless Sensor Network (WSN) to expand their efforts to various human monitoring applications ranging from military, environmental, industrial and healthcare to smart-home systems.

WSN is a self-configuring network system consisting of hundreds of low-cost, self-powered sensors, a high-capacity base node, and a management terminal (client). Sensor nodes are randomly arranged or grouped in a detection field. These sensors have a limited range of detection, collection, and processing capabilities. They could communicate with each other and the base node at radio frequency (RF). The base node is the command station of the WSN, passing the processed information to the cloud for analysis and sending commands to the sensor nodes. Data analysis and evaluation take place at the management terminal of the system.

The researchers developed sensor-based monitoring systems to provide sports trainers with real-time performance monitoring and biofeedback to optimize performance techniques and scientifically train sportspersons. As expected, this WSN-based sports training system includes hardware and software systems. The hardware system is designed and configured for real-time capturing of movement parameters and the software system collects, stores, and analyzes these motion parameters.

The sports training systems currently use inertial measurement units for motion sensing. Haptic sensors are the standard choice for measuring physiological parameters. For convenience, the WSNs are designed as wearable devices to be placed on the arm, chest, or leg. These wearable devices collect the data generated during motions or exercise. For processing the collected data, a low-power Bluetooth (BLE) transmits data between the wearable devices and the base station, usually a smartphone.

The base station or smartphone consists of a user interface and a software module. The software module reads/displays information from the user interface, interacts periodically with the sensor device, and synchronizes data to and from the cloud. The user interface helps select different exercise modes, parameter settings, and training data management and reads the results.

Training experiments were conducted with multiple users and in different ways to determine the efficiency of the system. Accelerometers, gyroscopes and magnetometers were used as motion sensors. Feedback module sent data on 100ms start-up time with low latency, hence matching to the real-time feedback condition, to the user via vibration alert only when the result fell outside the permissible reference range. The study found that users could improve their performance based on the real-time tactile feedback provided by the system.

The real-time readings, online data storage and analysis, and the instant response provided by the WSN can considerably improve sports training and rehabilitation. The interactive control module of the system would enable the training personnel to track and manage the athletes' health and performance with a more rational and scientific approach.

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