Wearable Technology and Analytics as a Complementary Toolkit to Optimize Workload and to Reduce Injury Burden

Area of Science:

• Sports Medicine
• Biomedical Engineering
• Data Science

Background:

• Wearable sensors offer real-time, non-invasive athlete monitoring.
• Analyzing extensive athlete datasets is time-consuming.
• Machine learning and AI can improve clinical decision-making in sports.

Purpose of the Study:

• To review commercial wearable sensor applications in sports.
• To discuss descriptive analytics for athlete monitoring.
• To highlight the role of data science in athlete health and performance.

Main Methods:

• Narrative review of current wearable sensor technology in sports.
• Analysis of descriptive analytics for workload, hydration, sleep, and cardiovascular health.
• Examination of return-to-sport assessment using sensor data.

Main Results:

• Commercial sensors are increasingly used for athlete monitoring.
• Descriptive analytics can track internal/external workload, hydration, sleep, and cardiovascular health.
• Data science facilitates informed decisions on athlete health, safety, and performance.

Conclusions:

• Wearable sensors combined with data science enhance athlete performance and reduce injury risk.
• AI and machine learning are crucial for translating sensor data into actionable insights.
• This approach benefits athletes of all ages by optimizing training and recovery.