Study of wearable monitoring of molecular and cellular biomechanics during physical training
Abstract
The present research work aimed to evaluate the integration of wearable biosensors for real-time monitoring of molecular and cellular biomechanics during physical training. The present work assessed their accuracy, applicability, and predictive capabilities in optimizing athlete performance and preventing injuries. This research used wearable biosensors to monitor biomarkers such as lactate, glucose, cortisol, and heart rate variability in athletes. Data was collected from 100 participants actively engaged in professional or semi-professional training regimens across endurance and high-intensity sports, including long-distance running, cycling, soccer, and basketball. Statistical, machine learning and physiological modelling techniques are employed to analyze the data. The findings revealed that wearable biosensors effectively track key biomarkers related to metabolic stress and hydration status, providing insights into performance optimization. However, challenges like sensor stability, motion artefacts, and individual variability in sweat composition were noted. We could use this extra enhancement with artificial intelligence AI algorithms to do predictive analytics to predict injury or train and to use a much better-sliding scale on how to train and load them. However, we are not standardized in all the data from all the different sensor platforms. Wearable biosensors have great promise in revolutionizing medicine in sports, altering sports performance and injury prevention. However, accuracy, data standardization and motion artefact reduction are required to adopt these devices in the broader population.
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