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A Novel Digital Platform for a Monitored Home-based Cardiac Rehabilitation Program
Published on: April 19, 2019
Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance
Abdullah Alzahrani1, Arif Ullah2
1Department of Health Rehabilitation Sciences, College of Applied Medical Sciences at Shaqra, Shaqra University, Shaqra, Saudi Arabia.
Objective:
This study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions.
Methods:
The research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters.
Results:
Key performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies.
Conclusions:
The study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.
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