Issue

Vol. 17 (2025)

AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring

https://doi.org/10.1007/s40820-025-01753-w
Jinke Chang
Multifunctional Materials and Composites (MMC) Laboratory, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Jinchen Li
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Jiahao Ye
Multifunctional Materials and Composites (MMC) Laboratory, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Bowen Zhang
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Jianan Chen
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Yunjia Xia
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Jingyu Lei
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Tom Carlson
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Rui Loureiro
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK
Alexander M. Korsunsky
Trinity College, University of Oxford, Oxford OX1 3BH, UK
Jin‑Chong Tan
Multifunctional Materials and Composites (MMC) Laboratory, Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK
Hubin Zhao
HUB of Intelligent Neuro‑Engineering (HUBIN), Aspire CREATe, DSIS, University College London, London HA7 4LP, UK

Corresponding Author: Hubin Zhao

hubin.zhao@ucl.ac.uk

Nano-Micro Letters, Vol. 17 (2025), Article Number: 247

Abstract

Joint health is critical for musculoskeletal (MSK) conditions that are affecting approximately one-third of the global population. Monitoring of joint torque can offer an important pathway for the evaluation of joint health and guided intervention. However, there is no technology that can provide the precision, effectiveness, low-resource setting, and long-term wearability to simultaneously achieve both rapid and accurate joint torque measurement to enable risk assessment of joint injury and long-term monitoring of joint rehabilitation in wider environments. Herein, we propose a piezoelectric boron nitride nanotubes (BNNTs)-based, AI-enabled wearable device for regular monitoring of joint torque. We first adopted an iterative inverse design to fabricate the wearable materials with a Poisson’s ratio precisely matched to knee biomechanics. A highly sensitive piezoelectric film was constructed based on BNNTs and polydimethylsiloxane and applied to precisely capture the knee motion, while concurrently realizing self-sufficient energy harvesting. With the help of a lightweight on-device artificial neural network, the proposed wearable device was capable of accurately extracting targeted signals from the complex piezoelectric outputs and then effectively mapping these signals to their corresponding physical characteristics, including torque, angle, and loading. A real-time platform was constructed to demonstrate the capability of fine real-time torque estimation. This work offers a relatively low-cost wearable solution for effective, regular joint torque monitoring that can be made accessible to diverse populations in countries and regions with heterogeneous development levels, potentially producing wide-reaching global implications for joint health, MSK conditions, ageing, rehabilitation, personal health, and beyond.


Highlights:
1 Artificial intelligence-enabled wearable device with boron nitride nanotubes (BNNTs)-based piezoelectric film for accurate joint torque sensing.
2 Inverse-designed structure optimizes biomechanical compatibility for enhanced knee motion tracking.
3 High-sensitivity BNNTs/polydimethylsiloxane composite enables precise and dynamic knee motion signal detection.
4 Lightweight neural network processes complex signals for accurate torque, angle, and load estimation.
5 Real-time monitoring system provides instant knee torque assessment for daily use.

Keywords

Artificial intelligence wearables Joint torque monitoring Boron nitride nanotubes Piezoelectric devices Inverse design
Chang, Jinke, Jinchen Li, Jiahao Ye, Bowen Zhang, Jianan Chen, Yunjia Xia, Jingyu Lei, Tom Carlson, Rui Loureiro, Alexander M. Korsunsky, Jin‑Chong Tan, and Hubin Zhao. 2025. “AI-Enabled Piezoelectric Wearable for Joint Torque Monitoring”. Nano-Micro Letters 17 (May):247. https://doi.org/10.1007/s40820-025-01753-w.
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