New Tech Tuesdays: Edge AI and Sensors Boost Athletic Performance

New Tech Tuesdays

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Published December 16, 2025

Artificial intelligence (AI) and sensor technology are reshaping how athletes train, compete, and recover. In sports, where milliseconds or millimeters separate victory from defeat, the fusion of edge AI, microelectromechanical systems (MEMS), and advanced sensor systems is empowering coaches, analysts, and athletes to unlock new levels of performance. As AI processing migrates from cloud to edge devices, wearables and smart sensors now deliver actionable, real-time insights directly on the playing field, enabling faster and energy-efficient decision-making.

This week’s New Tech Tuesdays explores how edge AI and high-precision sensing technologies are revolutionizing sports science through smart wearables, real-time motion analytics, and embedded intelligence that enhances both performance and safety.

AI-driven wearables and sensor systems are enabling unprecedented data precision. From inertial sensors to bio-signal monitors, these technologies collect massive volumes of physiological and biomechanical data—including acceleration, velocity, muscle fatigue, and heart rate—that were once impossible to measure accurately in live settings. Edge AI algorithms running locally on devices can now analyze this data instantly, eliminating latency and dependence on cloud networks.

The integration of AI in sports science extends far beyond basic performance tracking, though. Modern AI models, powered by machine learning (ML) and deep learning architectures, process data from sensors, video feeds, and wearables to uncover complex patterns related to stamina, injury risks, or performance inefficiencies. Research in this field emphasizes that AI enables “context-specific” and “evidence-based feedback” within strict timeframes for optimizing training load, recovery, and injury prevention in both individual and team sports.^[1] For instance, during the 2022–2023 season, injury-related expenses in Europe’s top five men’s football leagues surged by nearly 30 percent, climbing from €553.62 million to €704.89 million—a stark indicator of the growing financial toll of player injuries. These edge computing applications allow coaches to adapt programs dynamically, ensuring athletes perform at their physiological peak while minimizing burnout and overtraining.

Wearables powered by edge AI have become an essential part of this ecosystem. For example, a host of major international sporting organizations, including English Premier League football clubs, use the STATSports Apex tracker powered by a Nordic Semiconductor nRF52840 system-on-chip (SoC). The Apex tracker incorporates inertial sensors and Bluetooth^® Low Energy connectivity to process movement data locally, transforming raw inputs into high-precision metrics such as launch angle, spin rate, acceleration, and workload intensity.^[2] Many of these inertial sensing functions rely on compact MEMS structures, enabling precise motion capture in lightweight athletic devices. The insights feed directly into mobile dashboards, helping sports scientists assess biomechanical efficiency and tactical execution in near real time.

The Newest Products for Your Newest Designs^®

In the quest for real-time monitoring that leads to instant insights by combining AI and sensor technology, the STMicroelectronics LSM6DSV320X six-axis inertial measurement unit (IMU) embodies the integration of sensor precision and embedded intelligence. This miniature device (Figure 1) integrates a three-axis low-g accelerometer, a three-axis high-g accelerometer, and a three-axis gyroscope—delivering unmatched motion detection and sensor fusion capabilities. Designed with a quad-channel architecture, this IMU processes acceleration and angular rate data on independent channels, allowing simultaneous motion analysis for various use cases, such as sports tracking, IoT devices, and crash detection.

Figure 1: The STMicroelectronics LSM6DSV320X six-axis IMU is a sophisticated IMU that features a dedicated high-g accelerometer sensor for high-g shock detection, making it suitable for applications requiring robust impact detection. (Source: Mouser Electronics)

The LSM6DSV320X features a machine learning core (MLC) and a finite state machine (FSM) that enable context awareness and configurable motion tracking. With adaptive self-configuration (ASC), the IMU adjusts autonomously based on real-time movement patterns, ensuring precision while minimizing power consumption. Its high-g accelerator—capable of detecting shocks up to ±320g—makes it ideal for high-impact sports, from football tackles to gymnastics landings. Along with the smart FIFO, offering up to 4.5KB data buffering, and embedded sensor fusion low-power (SFLP) algorithms, this IMU delivers a “smart, always aware” experience that aligns seamlessly with edge AI-driven athletic monitoring systems.

By integrating the LSM6DSV320X IMU within wearable platforms, engineers can enable on-device analytics that quantify biomechanical dynamics with exceptional accuracy.

The convergence of advanced MEMS, edge AI, and advanced sensor technologies is redefining the limits of human performance. From wearable motion trackers to embedded IMUs like the LSM6DSV320X, the synergy between data precision, local intelligence, and adaptive analytics is equipping athletes with the tools once reserved for scientific laboratories. The integration of AI into sports science has shifted the discipline from reactive performance monitoring to predictive, preventive, and personalized athletic management—a paradigm that places data-driven insights at the center of decision making

Sources

[1]https://www.mdpi.com/1424-8220/25/1/139

[2]https://blog.nordicsemi.com/getconnected/how-next-gen-wearables-and-edge-ai-improve-sports-performance-analytics