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.
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The study, recently published in Nano-Micro Letters by Professor Jin-Chong Tan (University of Oxford) and Professor Hubin Zhao (University College London), introduces a piezoelectric sensor that leverages the unique properties of boron nitride nanotubes (BNNTs) to deliver high-precision data in real-world settings.
Why This Research Matters
Traditional methods for assessing joint torque are often limited to lab environments or involve complex setups, making them impractical for routine use. 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.
The 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. A 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.
Beyond its technical capabilities, the device’s design and material choices make it a practical option for widespread use. It operates efficiently in low-power environments and is constructed from cost-effective, scalable materials, offering an accessible solution for populations in both resource-rich and underserved regions.
Innovative Design and Mechanisms
Boron nitride nanotubes were selected for their exceptional mechanical strength, thermal stability, and intrinsic piezoelectric properties, which make them ideal for high-performance sensor applications. When uniformly dispersed within a PDMS matrix, the resulting composite forms a highly responsive piezoelectric film capable of capturing detailed and nuanced knee movement patterns.
The wearable also features an inverse-designed structure with a negative Poisson’s ratio, precisely engineered to match the biomechanics of the knee joint. This ensures 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 like torque, angle, and load, offering real-time insights into joint health without relying on external computing resources.
Applications and Future Outlook
This wearable has clear value in continuous joint health monitoring. It can support early detection of joint issues and track changes over time, making it especially useful for individuals with musculoskeletal conditions, older adults, and athletes. By delivering precise, real-time data, it enables personalized rehabilitation plans and more responsive clinical care.
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. In rehabilitation settings, it can guide safe, effective progress by ensuring joint loads remain within safe thresholds.
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. They’re also exploring the potential to integrate the technology with other systems—such as wearable robotics or exoskeletons—to expand its functionality across clinical, athletic, and assistive domains.
Journal Reference:
Chang, J., et al. (2025) AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring. Nano-Micro Letters. doi.org/10.1007/s40820-025-01753-w.