Basketball player motion detection and motion mode analysis based on biomechanical sensors
Abstract
Basketball player motion detection and analysis are crucial for optimizing performance and preventing injuries. Traditional methods often rely on visual observation and video analysis, lacking precision and real-time feedback. In this study, a unique novel Intelligent Bayesian tuned-augmented Support Vector Machine (IB-ASVM) was proposed for predicting basketball players’ motion modes and performance analysis using the biomechanical sensor data. Advancements in biomechanical sensors such as accelerometers, gyroscopes, and force sensors are deployed into ESP32 to build a player’s wearable gadget. This gadget provides dynamic players with real-time sensing data. Data are transmitted to the cloud via Wi-Fi 7.0 for motion analysis and this model is stimulated using Arduino IDE. The Kalman Filter reduces noise and smoothens sensor data such as acceleration, and angular velocity. Then, the filtered data is employed in the Discrete Wavelet Transform (DWT) to capture time-frequency characteristics of motion signals, making it ideal for extracting relevant features. The featured data are utilized in the ASVM model to classify and detect the motion modes of the basketball players via IB optimization. The Tensor Flow software is used to implement the IB-ASVM model. The result demonstrates that IB-ASVM most accurately predicts the jump shot, layup, dribbling, running, pivoting, passing, free throw, and motion states of the basketball players. The IB-ASVM model accurately classifies basketball motion states using biomechanical sensor data, enhancing performance optimization and injury prevention through precise motion detection.
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