AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring

Area of Science:

• Biomedical Engineering
• Materials Science
• Artificial Intelligence

Background:

• Musculoskeletal (MSK) conditions affect a third of the global population, necessitating effective joint health monitoring.
• Current technologies lack the precision, wearability, and accessibility for widespread joint torque measurement.
• Accurate joint torque monitoring is crucial for injury risk assessment and rehabilitation.

Purpose of the Study:

• To develop a novel, AI-enabled wearable device for precise and accessible joint torque monitoring.
• To create a low-cost, long-term solution for evaluating joint health and guiding rehabilitation.
• To address the limitations of existing technologies in diverse environmental and developmental settings.

Main Methods:

• Fabrication of wearable materials with biomechanically matched Poisson's ratio using iterative inverse design.
• Construction of a sensitive piezoelectric film using boron nitride nanotubes (BNNTs) and polydimethylsiloxane for motion capture and energy harvesting.
• Implementation of an on-device artificial neural network for signal processing and mapping to physical characteristics (torque, angle, loading).

Main Results:

• The BNNT-based wearable device accurately captures knee motion and estimates joint torque, angle, and loading in real-time.
• The integrated AI effectively extracts and interprets complex piezoelectric signals.
• The device demonstrates self-sufficient energy harvesting capabilities.

Conclusions:

• This AI-enabled, piezoelectric BNNT wearable offers a low-cost, effective solution for regular joint torque monitoring.
• The technology has the potential for wide-reaching global impact on joint health, MSK conditions, and rehabilitation.
• The device's accessibility makes it suitable for diverse populations and varying developmental levels.