Researchers Unveil AI-enabled Wearable for Joint Monitoring

In a significant step toward more effective and accessible joint health monitoring, researchers have created a new AI-powered wearable device designed to accurately measure joint torque.

In a significant step toward more effective and accessible joint health monitoring, researchers from University of Oxford and University College London have created a new AI-powered wearable device designed to accurately measure joint torque.

A recent study, published in Nano-Micro Letters by Professor Jin-Chong Tan (University of Oxford) and Professor Hubin Zhao (University College London),

revealed a piezoelectric sensor that leverages the unique properties of boron nitride nanotubes (BNNTs) to deliver high-precision data in real-world settings.

This new wearable device offers a portable, user-friendly alternative for continuous joint torque monitoring, which is essential for evaluating joint health, guiding therapeutic interventions, and tracking rehabilitation progress over time.

AI Tool for Joint Monitoring

As per reports, the AI tool is lightweight neural network integrated into the device processes complex signals on the spot, enabling accurate estimation of torque, angle, and load. This real-time feedback provides reliable, actionable data for assessing joint function.

Key innovation lies in the device’s high-sensitivity composite of BNNTs and polydimethylsiloxane (PDMS), which allows it to precisely detect dynamic knee motion signals.

The wearable also features an inverse-designed structure with a negative Poisson’s ratio, precisely engineered to match the biomechanics of the knee joint ensuring a secure and adaptive fit, improving motion tracking fidelity and enabling detailed sensing of complex loading conditions during movement.

Artificial intelligence plays a central role in the system’s effectiveness. A lightweight neural network embedded directly within the device analyzes the piezoelectric signals generated during movement.

It accurately extracts and interprets these signals, mapping them to corresponding physical parameters such as torque, angle, and load, offering real-time insights into joint health without relying on external computing resources.

The device could also play an important role in injury prevention and recovery. Its real-time torque analysis can alert users or clinicians to potentially harmful joint movements, helping to prevent overuse or misalignment during physical activity.

Looking forward, the researchers plan to further enhance the device by optimizing its sensing materials, refining its structural design, and advancing the AI algorithms to boost accuracy and adaptability.

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