AI-Enabled Piezoelectric Wearable to Revolutionize Joint Torque Monitoring

This poses a major barrier to effective joint health monitoring, which is critical for evaluating joint function, guiding clinical interventions, and tracking rehabilitation. Accurate, real-time monitoring is particularly important for individuals with musculoskeletal conditions, older adults, and athletes, where early detection of abnormalities and injury prevention are key. Now, researchers have developed a portable and non-invasive wearable device capable of continuously monitoring joint torque with high sensitivity and accuracy, offering a promising solution for real-world applications.

The artificial intelligence (AI)-enabled wearable device, developed through a collaboration between the University of Oxford (Oxford, UK) and University College London (London, UK), incorporates a boron nitride nanotube (BNNT)/polydimethylsiloxane (PDMS) composite film, chosen for its exceptional mechanical strength, thermal stability, and inherent piezoelectric properties. The BNNTs are uniformly dispersed within the PDMS matrix to form a high-performance piezoelectric sensor capable of capturing dynamic knee motion signals. The device features an inverse-designed structure with a negative Poisson's ratio, tailored to match the biomechanics of the human knee, enhancing compatibility and motion-tracking fidelity. Integrated with a lightweight on-device artificial neural network, the system processes complex piezoelectric signals in real time and maps them to physical indicators such as joint torque, angle, and load, allowing for instantaneous assessment during daily activities.

The system demonstrated high sensitivity in detecting knee motion and torque, with its AI component enabling real-time, accurate signal interpretation. The technology is both low-cost and compatible with low-power environments, making it suitable for use in diverse healthcare settings. According to the findings presented in Nano-Micro Letters, the device provides actionable data for joint evaluation, supports injury prevention, and enables personalized rehabilitation strategies. Looking forward, the researchers aim to optimize the sensor materials, device architecture, and AI algorithms to further improve performance and adaptability. Additional efforts will explore combining the wearable with robotic systems or exoskeletons to expand its applications in physical therapy and advanced mobility solutions. This innovative device represents a significant leap in wearable technology for joint health, combining advanced sensing materials with AI to enable real-time, accessible monitoring that could transform rehabilitation and injury prevention.