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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References
  1. E. Sebbag, R. Felten, F. Sagez, J. Sibilia, H. Devilliers et al., The world-wide burden of musculoskeletal diseases: a systematic analysis of the world health organization burden of diseases database. Ann. Rheum. Dis. 78(6), 844–848 (2019). https://doi.org/10.1136/annrheumdis-2019-215142
  2. A. Cieza, K. Causey, K. Kamenov, S.W. Hanson, S. Chatterji et al., Global estimates of the need for rehabilitation based on the global burden of disease study 2019: a systematic analysis for the global burden of disease study 2019. Lancet 396(10267), 2006–2017 (2021). https://doi.org/10.1016/S0140-6736(20)32340-0
  3. M.D. National Academies of Sciences, Selected Health Conditions and Likelihood of Improvement with Treatment. Selected Health Conditions and Likelihood of Improvement with Treatment (National Academies Press, Washington, D.C., 2020).
  4. A. Braybrooke, K. Baraks, R. Burgess, A. Banerjee, J.C. Hill, Quality indicators for the primary and community care of musculoskeletal conditions: a systematic review. Arch. Phys. Med. Rehabil. 106(3), 459–472 (2025). https://doi.org/10.1016/j.apmr.2024.08.022
  5. Musculoskeletal health. (2024). https://www.england.nhs.uk/elective-care-transformation/best-practice-solutions/musculoskeletal/
  6. H. Pang, S. Chen, D.M. Klyne, D. Harrich, W. Ding et al., Low back pain and osteoarthritis pain: a perspective of estrogen. Bone Res. 11(1), 42 (2023). https://doi.org/10.1038/s41413-023-00280-x
  7. Versus Arthritis, The State of Musculoskeletal Health 2024. The State of Musculoskeletal Health 2024 (Chesterfield, 2024).
  8. Office for Health Improvement and Disparities (OHID), Guidance Musculoskeletal health: applying All Our Health. (2022). https://www.gov.uk/government/publications/musculoskeletal-health-applying-all-our-health/musculoskeletal-health-applying-all-our-health
  9. J. Adamson, S. Ebrahim, P. Dieppe, K. Hunt, Prevalence and risk factors for joint pain among men and women in the West of Scotland twenty-07 study. Ann. Rheum. Dis. 65(4), 520–524 (2006). https://doi.org/10.1136/ard.2005.037317
  10. R.W. Nuckols, S. Lee, K. Swaminathan, D. Orzel, R.D. Howe et al., Individualization of exosuit assistance based on measured muscle dynamics during versatile walking. Sci. Robot. 6(60), eabj1362 (2021). https://doi.org/10.1126/scirobotics.abj1362
  11. Y. Jin, J.T. Alvarez, E.L. Suitor, K. Swaminathan, A. Chin et al., Estimation of joint torque in dynamic activities using wearable A-mode ultrasound. Nat. Commun. 15(1), 5756 (2024). https://doi.org/10.1038/s41467-024-50038-0
  12. A.E. Engin, M.S. Korde, Biomechanics of normal and abnormal knee joint. J. Biomech. 7(4), 325–334 (1974). https://doi.org/10.1016/0021-9290(74)90027-X
  13. E.G. Meyer, R.C. Haut, Excessive compression of the human tibio-femoral joint causes ACL rupture. J. Biomech. 38, 2311–2316 (2005). https://doi.org/10.1016/j.jbiomech.2004.10.003
  14. J. Verheul, N.J. Nedergaard, J. Vanrenterghem, M.A. Robinson, Measuring biomechanical loads in team sports–from lab to field. Sci. Med. Footb. 4(3), 246–252 (2020). https://doi.org/10.1080/24733938.2019.1709654
  15. D.S. Logerstedt, J.R. Ebert, T.D. MacLeod, B.C. Heiderscheit, T.J. Gabbett et al., Effects of and response to mechanical loading on the knee. Phys. Med. 52(2), 201–235 (2022). https://doi.org/10.1007/s40279-021-01579-7
  16. B. Innocenti, Biomechanics of the knee joint, in Human Orthopaedic Biomechanics. (Elsevier, Netherland, 2022), pp.239–263
  17. L. Zhang, G. Liu, B. Han, Z. Wang, Y. Yan et al., Knee joint biomechanics in physiological conditions and how pathologies can affect it: a systematic review. Appl. Bionics Biomech. 2020, 7451683 (2020). https://doi.org/10.1155/2020/7451683
  18. S.E. Forrester, M.R. Yeadon, M.A. King, M.G. Pain, Comparing different approaches for determining joint torque parameters from isovelocity dynamometer measurements. J. Biomech. 44(5), 955–961 (2011). https://doi.org/10.1016/j.jbiomech.2010.11.024
  19. J. Holder, U. Trinler, A. Meurer, F. Stief, A systematic review of the associations between inverse dynamics and musculoskeletal modeling to investigate joint loading in a clinical environment. Front. Bioeng. Biotechnol. 8, 603907 (2020). https://doi.org/10.3389/fbioe.2020.603907
  20. M. Safaei, R. Michael Meneghini, S.R. Anton, Force detection, center of pressure tracking, and energy harvesting from a piezoelectric knee implant. Smart Mater. Struct. 27(11), 114007 (2018). https://doi.org/10.1088/1361-665X/aad755
  21. C. Jacq, T. Maeder, S. Emery, M. Simoncini, E. Meurville et al., Investigation of polymer thick-film piezoresistors for medical wrist rehabilitation and artificial knee load sensors. Procedia Eng. 87, 1194–1197 (2014). https://doi.org/10.1016/j.proeng.2014.11.380
  22. M. Arvanitidis, D. Jiménez-Grande, N. Haouidji-Javaux, D. Falla, E. Martinez-Valdes, People with chronic low back pain display spatial alterations in high-density surface EMG-torque oscillations. Sci. Rep. 12(1), 15178 (2022). https://doi.org/10.1038/s41598-022-19516-7
  23. S. Yang, J. Cheng, J. Shang, C. Hang, J. Qi et al., Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention. Nat. Commun. 14(1), 6494 (2023). https://doi.org/10.1038/s41467-023-42149-x
  24. J. Chapman, A. Dwivedi, M. Liarokapis, A wearable, open-source, lightweight forcemyography armband: on intuitive, robust muscle-machine interfaces. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). September 27-October 1, 2021. Prague, Czech Republic. IEEE, (2021). pp. 4138–4143. https://doi.org/10.1109/iros51168.2021.9636345
  25. Z.G. Xiao, C. Menon, Performance of forearm FMG and sEMG for estimating elbow, forearm and wrist positions. J. Bionic Eng. 14(2), 284–295 (2017). https://doi.org/10.1016/S1672-6529(16)60398-0
  26. Z. Qing, Z. Lu, Z. Liu, Y. Cai, S. Cai et al., A simultaneous gesture classification and force estimation strategy based on wearable A-mode ultrasound and cascade model. IEEE Trans. Neural Syst. Rehabil. Eng. 30, 2301–2311 (2022). https://doi.org/10.1109/TNSRE.2022.3196926
  27. D. Kim, J. Ko, Y.-K. Kim, S.S. Lee, S. Ahn et al., Spontaneous alignment of boron nitride nanotubes into polycrystalline film arrays for enhanced piezoelectric nanogeneration. Small Struct. 5(11), 2400259 (2024). https://doi.org/10.1002/sstr.202400259
  28. A. Niguès, A. Siria, P. Vincent, P. Poncharal, L. Bocquet, Ultrahigh interlayer friction in multiwalled boron nitride nanotubes. Nat. Mater. 13(7), 688–693 (2014). https://doi.org/10.1038/nmat3985
  29. Y. Huang, J. Lin, J. Zou, M.-S. Wang, K. Faerstein et al., Thin boron nitride nanotubes with exceptionally high strength and toughness. Nanoscale 5(11), 4840–4846 (2013). https://doi.org/10.1039/c3nr00651d
  30. X. Zeng, J. Sun, Y. Yao, R. Sun, J.-B. Xu et al., A combination of boron nitride nanotubes and cellulose nanofibers for the preparation of a nanocomposite with high thermal conductivity. ACS Nano 11(5), 5167–5178 (2017). https://doi.org/10.1021/acsnano.7b02359
  31. J.H. Kang, G. Sauti, C. Park, V.I. Yamakov, K.E. Wise et al., Multifunctional electroactive nanocomposites based on piezoelectric boron nitride nanotubes. ACS Nano 9(12), 11942–11950 (2015). https://doi.org/10.1021/acsnano.5b04526
  32. J. Zhang, S. Ye, H. Liu, X. Chen, X. Chen et al., 3D printed piezoelectric BNNTs nanocomposites with tunable interface and microarchitectures for self-powered conformal sensors. Nano Energy 77, 105300 (2020). https://doi.org/10.1016/j.nanoen.2020.105300
  33. P. Snapp, C. Cho, D. Lee, M.F. Haque, S. Nam et al., Tunable piezoelectricity of multifunctional boron nitride nanotube/poly(dimethylsiloxane) stretchable composites. Adv. Mater. 32, 2004607 (2020). https://doi.org/10.1002/adma.202004607
  34. B.J. Stetter, T. Stein, Machine learning in biomechanics: enhancing human movement analysis, in Artificial Intelligence in Sports, Movement, and Health. (Springer Nature Switzerland, Cham, 2024), pp.139–160
  35. Y. Guo, H. Zhang, L. Fang, Z. Wang, W. He et al., A self-powered flexible piezoelectric sensor patch for deep learning-assisted motion identification and rehabilitation training system. Nano Energy 123, 109427 (2024). https://doi.org/10.1016/j.nanoen.2024.109427
  36. Y. Chen, X. Zhang, C. Lu, Flexible piezoelectric materials and strain sensors for wearable electronics and artificial intelligence applications. Chem. Sci. 15(40), 16436–16466 (2024). https://doi.org/10.1039/d4sc05166a
  37. Y. Luo, Y. Li, P. Sharma, W. Shou, K. Wu et al., Learning human–environment interactions using conformal tactile textiles. Nat. Electron. 4(3), 193–201 (2021). https://doi.org/10.1038/s41928-021-00558-0
  38. U.D. Larsen, O. Signund, S. Bouwsta, Design and fabrication of compliant micromechanisms and structures with negative Poisson’s ratio. J. Microelectromech. Syst. 6(2), 99–106 (1997). https://doi.org/10.1109/84.585787
  39. L. Mizzi, E. Salvati, A. Spaggiari, J.-C. Tan, A.M. Korsunsky, 2D auxetic metamaterials with tuneable micro-/nanoscale apertures. Appl. Mater. Today 20, 100780 (2020). https://doi.org/10.1016/j.apmt.2020.100780
  40. L. Mizzi, E. Salvati, A. Spaggiari, J.-C. Tan, A.M. Korsunsky, Highly stretchable two-dimensional auxetic metamaterial sheets fabricated via direct-laser cutting. Int. J. Mech. Sci. 167, 105242 (2020). https://doi.org/10.1016/j.ijmecsci.2019.105242
  41. J.C. Lagarias, J.A. Reeds, M.H. Wright, P.E. Wright, Convergence properties of the nelder: mead simplex method in low dimensions. SIAM J. Optim. 9(1), 112–147 (1998). https://doi.org/10.1137/s1052623496303470
  42. A.F. Möslein, M. Gutiérrez, B. Cohen, J.-C. Tan, Near-field infrared nanospectroscopy reveals guest confinement in metal-organic framework single crystals. Nano Lett. 20(10), 7446–7454 (2020). https://doi.org/10.1021/acs.nanolett.0c02839
  43. D. Griffin, J. Lim, Signal estimation from modified short-time Fourier transform. IEEE Trans. Acoust. Speech Signal Process. 32(2), 236–243 (1984). https://doi.org/10.1109/TASSP.1984.1164317
  44. A.C. Belkina, C.O. Ciccolella, R. Anno, R. Halpert, J. Spidlen et al., Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets. Nat. Commun. 10(1), 5415 (2019). https://doi.org/10.1038/s41467-019-13055-y
  45. Dina Zhabinskaya, University of California Davis: Physics 7B General Physics. University of California Davis: Physics 7B General Physics (University of California Davis, n.d.).
  46. R. Riemer, E.T. Hsiao-Wecksler, Improving joint torque calculations: optimization-based inverse dynamics to reduce the effect of motion errors. J. Biomech. 41(7), 1503–1509 (2008). https://doi.org/10.1016/j.jbiomech.2008.02.011
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