The application of infrared photosensitive π-conjugated materials in the diagnosis and rehabilitation of table tennis sports injury
Abstract
The health and performance of athletes can be negatively impacted when table tennis injuries are misdiagnosed, leading to subpar recovery and an increased risk of recurrence. The inability of conventional approaches to gather detailed information in real-time during gameplay necessitates a fresh approach. This paper examines machine learning techniques such as eXtreme Gradient Boost (XGBoost) methods and Long Short-Term Memory (LSTM) networks to assess real-time physiological data acquired by wearable devices in response to this need. This study proposes the Machine Learning-based Diagnosis and Rehabilitation of Table Tennis Sports Injuries (ML-DRTTSI) approach, which employs infrared radiation-sensitive π-conjugated materials. It paves the way for precise tracking of temperature fluctuations and blood flow as they pertain to athletic injuries. Wearable sensors allow for the accurate recording of physiological changes that occur during matches. LSTM networks can discover injury-related signatures through the extraction of correlations and patterns. XGBoost is a gradient-boosting method that enhances the precision of injury diagnostics and severity evaluation by applying learned features in classification and regression tasks. Experts and players can use the hybrid model’s quick and accurate insights into injury aspects to predict which rehabilitation methods will be most effective for table tennis injuries. An innovative solution is the goal of this interdisciplinary project that brings together specialists in machine learning, materials science, and sports medicine. More than just a giant leap for sports technological advancements, the hybrid model also holds enormous promise for improved injury detection and recovery. This finding can revolutionize sports therapy and injury treatment, not just in table tennis.
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