Novel adaptive machine-learning-based smart wearable biosensors: Revolutionizing athlete health monitoring in biomedical perspective
Abstract
This study introduces novel Adaptive Machine-Learning-Based Smart Wearable Biosensors (AML-SWB) for real-time monitoring of athletes’ health. By integrating accelerometers, gyroscopes, and biometric sensors, AML-SWB can collect comprehensive physiological data. Machine learning algorithms, especially Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) units, are incorporated to analyze the data, enabling accurate assessment of athletes’ health status, injury risk prediction, and performance optimization. An evaluation of motion efficiency, identification of gait asymmetry, and measurement of joint stress are all parts of the biomechanical analysis that the proposed AML-SWB incorporates to improve conventional monitoring. These findings pave the way for individualized training modifications and early intervention to reduce the likelihood of injuries. Despite challenges such as data accuracy and user acceptance, continuous technological advancements and algorithm refinement are expected to overcome these obstacles.
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