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. 2024 May 27:10:20552076241256745.
doi: 10.1177/20552076241256745. eCollection 2024 Jan-Dec.

Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance

Abdullah Alzahrani  1 , Arif Ullah  2
Affiliations

Advanced biomechanical analytics: Wearable technologies for precision health monitoring in sports performance

Abdullah Alzahrani et al. Digit Health. .

Abstract

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.

Keywords: Biomechanics; athlete optimization; precision health monitoring; sports performance; wearable technologies.

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Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Sensors used (a) IMU sensor, (b) EMG sensor, (c) haptic feedback sensor.
Figure 2.
Figure 2.
Software simulation.
Figure 3.
Figure 3.
Telerehab with advanced biomechanical analytics in physiotherapy.
Figure 4.
Figure 4.
Relationship between hypotheses.
Figure 5.
Figure 5.
Distribution of sensors planted on athletes.
Figure 6.
Figure 6.
Athelete A1 metrics distribution.
Figure 7.
Figure 7.
Athlete A2 metrics distribution.
Figure 8.
Figure 8.
Athlete A3 metrics distribution.
See this image and copyright information in PMC

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