A patterned mechanical–electrical coupled sensing patch for multimodal muscle function evaluation

Jiangtao Xue , Yang Zou , Zhirong Wan , Minghao Liu , Yiqian Wang , Huaqing Chu , Puchuan Tan , Li Wu , Engui Wang , Han Ouyang , Yulin Deng , Zhou Li

InfoMat ›› 2025, Vol. 7 ›› Issue (3) : e12631

PDF
InfoMat ›› 2025, Vol. 7 ›› Issue (3) : e12631 DOI: 10.1002/inf2.12631
RESEARCH ARTICLE

A patterned mechanical–electrical coupled sensing patch for multimodal muscle function evaluation

Author information +
History +
PDF

Abstract

Muscles, the fundamental components supporting all human movement, exhibit various signals upon contraction, including mechanical signals indicating tremors or mechanical deformation and electrical signals responsive to muscle fiber activation. For noninvasive wearable devices, these signals can be measured using surface electromyography (sEMG) and force myography (FMG) techniques, respectively. However, relying on a single source of information is insufficient for a comprehensive evaluation of muscle condition. In order to accurately and effectively evaluate the various states of muscles, it is necessary to integrate sEMG and FMG in a spatiotemporally synchronized manner. This study presents a flexible sensor for multimodal muscle state monitoring, integrating serpentine-structured sEMG electrodes with fingerprint-like FMG sensors into a patch approximately 250 μm thick. This design achieves a multimodal assessment of muscle conditions while maintaining a compact form factor. A thermo-responsive adhesive hydrogel is incorporated to enhance skin adhesion, improving the signal-to-noise ratio of the sEMG signals (33.07 dB) and ensuring the stability of the FMG sensor during mechanical deformation and tremors. The patterned coupled sensing patch demonstrates its utility in tracking muscular strength, assessing fatigue levels, and discerning features of muscle dysfunction by analyzing the time-domain and frequency-domain characteristics of the mechanical–electrical coupled signals, highlighting its potential application in sports training and rehabilitation monitoring.

Keywords

mechanical–electrical coupled / multimodal sensing / muscle function evaluation

Cite this article

Download citation ▾
Jiangtao Xue, Yang Zou, Zhirong Wan, Minghao Liu, Yiqian Wang, Huaqing Chu, Puchuan Tan, Li Wu, Engui Wang, Han Ouyang, Yulin Deng, Zhou Li. A patterned mechanical–electrical coupled sensing patch for multimodal muscle function evaluation. InfoMat, 2025, 7(3): e12631 DOI:10.1002/inf2.12631

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Enoka RM, Duchateau J. Muscle fatigue: what, why and how it influences muscle function. J Physiol. 2008; 586(1): 11-23.

[2]

Kuriki HU, Mello EM, De Azevedo FM, Takahashi LSO, Alves N, de Faria Negrão Filho R. The relationship between electromyography and muscle force. In: EMG Methods for Evaluating Muscle and Nerve Function. Rijeka: INTECH Open Access Publisher, 2012. 33-54.

[3]

Cifrek M, Medved V, Tonković S, Ostojić S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech. 2009; 24(4): 327-340.

[4]

Orizio C. Muscle sound: bases for the introduction of a mechanomyographic signal in muscle studies. Crit Rev Biomed Eng. 1993; 21(3): 201-243.

[5]

Krueger E, Scheeren EM, Nogueira-Neto GN, Button VLSN, Nohama P. Advances and perspectives of mechanomyography. Rev Bras Eng Bioméd. 2014; 30(4): 384-401.

[6]

Esposito D, Andreozzi E, Fratini A, et al. A piezoresistive sensor to measure muscle contraction and mechanomyography. Sensors. 2018; 18(8): 2553.

[7]

Tan P, Han X, Zou Y, et al. Self-powered gesture recognition wristband enabled by machine learning for full keyboard and multicommand input. Adv Mater. 2022; 34(21): 2200793.

[8]

Zou Y, Tan P, Shi B, et al. A bionic stretchable nanogenerator for underwater sensing and energy harvesting. Nat Commun. 2019; 10(1): 2695.

[9]

Al-Mulla MR, Sepulveda F, Colley M. A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors. 2011; 11(4): 3545-3594.

[10]

Alvarez JT, Gerez LF, Araromi OA, et al. Towards soft wearable strain sensors for muscle activity monitoring. IEEE Trans Neural Syst Rehabil Eng. 2022; 30: 2198-2206.

[11]

Gong Q, Jiang X, Liu Y, Yu M, Hu Y. A flexible wireless sEMG system for wearable muscle strength and fatigue monitoring in real time. Adv Electron Mater. 2023; 9(9): 2200916.

[12]

Chapman J, Dwivedi A, Liarokapis M. A wearable, open-source, lightweight forcemyography armband: on intuitive, robust muscle-machine interfaces. IEEE; 2021: 4138-4143.

[13]

Ergeneci M, Gokcesu K, Ertan E, Kosmas P. An embedded, eight channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. IEEE Trans Biomed Circ Syst. 2017; 12(1): 68-79.

[14]

Islam MA, Sundaraj K, Ahmad RB, Ahamed NU. Mechanomyogram for muscle function assessment: a review. PLoS One. 2013; 8(3): e58902.

[15]

Knezevic OM, Mirkov DM, Kadija M, Milovanovic D, Jaric S. Evaluation of isokinetic and isometric strength measures for monitoring muscle function recovery after anterior cruciate ligament reconstruction. J Strength Cond Res. 2014; 28(6): 1722-1731.

[16]

Bosco C, Colli R, Bonomi R, von Duvillard SP, Viru A. Monitoring strength training: neuromuscular and hormonal profile. Med Sci Sports Exerc. 2000; 32(1): 202-208.

[17]

Lerario A, Bonfiglio S, Sormani M, et al. Quantitative muscle strength assessment in duchenne muscular dystrophy: longitudinal study and correlation with functional measures. BMC Neurol. 2012; 12(1): 1-8.

[18]

Wang C, Qu X, Zheng Q, et al. Stretchable, self-healing, and skin-mounted active sensor for multipoint muscle function assessment. ACS Nano. 2021; 15(6): 10130-10140.

[19]

Phinyomark A, Campbell E, Scheme E. Surface electromyography (EMG) signal processing, classification, and practical considerations. Biomedical Signal Processing: Advances in Theory, Algorithms and Applications. Singapore: Springer; 2020: 3-29.

[20]

Sung JH, Baek S-H, Park J-W. Surface electromyography-driven parameters for representing muscle mass and strength. Sensors.2023; 23(12): 5490.

[21]

Zhang S, Guo S, Gao B, et al. Muscle strength assessment system using sEMG-based force prediction method for wrist joint. J Med Biol Eng. 2016; 36(1): 121-131.

[22]

Clancy EA, Morin EL, Hajian G, et al. Surface electromyogram (sEMG) amplitude estimation: Best practices. J Electromyogr Kinesiol. 2023; 72: 102807.

[23]

Athavale Y, Krishnan S. Biosignal monitoring using wearables: observations and opportunities. Biomed Signal Process Control. 2017; 38: 22-33.

[24]

Xiao ZG, Menon C. A review of force myography research and development. Sensors.2019; 19(20): 4557.

[25]

Zheng Z, Wu Z, Zhao R, Ni Y, Jing X, Gao S. A review of EMG-, FMG-, and EIT-based biosensors and relevant human-machine interactivities and biomedical applications. Biosensors. 2022; 12(7): 516.

[26]

Boyas S, Guével A. Neuromuscular fatigue in healthy muscle: underlying factors and adaptation mechanisms. Ann Phys Rehabil Med. 2011; 54(2): 88-108.

[27]

Wan J, Qin Z, Wang P, Sun Y, Liu X. Muscle fatigue: general understanding and treatment. Exp Mol Med. 2017; 49(10): e384.

[28]

Garcia-Retortillo S, Romero-Gómez C, Ivanov PC. Network of muscle fibers activation facilitates inter-muscular coordination, adapts to fatigue and reflects muscle function. Commun Biol. 2023; 6(1): 891.

[29]

Ferigo D, Merhi L-K, Pousett B, Xiao ZG, Menon C. A case study of a force-myography controlled bionic hand mitigating limb position effect. J Bionic Eng. 2017; 14(4): 692-705.

[30]

Truong H, Zhang S, Muncuk U, et al. Capband: battery-free successive capacitance sensing wristband for hand gesture recognition; 2018: 54-67.

[31]

Choi Y, Lee S, Sung M, Park J, Kim S, Choi Y. Development of EMG-FMG based prosthesis with PVDF-film vibrational feedback control. IEEE Sens J. 2021; 21(20): 23597-23607.

[32]

Wu YT, Gomes MK, da Silva WH, Lazari PM, Fujiwara E. Integrated optical fiber force myography sensor as pervasive predictor of hand postures. Biomed Eng Comput Biol. 2020; 11: 1179597220912825.

[33]

Jiang S, Gao Q, Liu H, Shull PB. A novel, co-located EMG-FMG-sensing wearable armband for hand gesture recognition. Sens Actuators, A.2020; 301: 111738.

[34]

Shahandashti PF, Pourkheyrollah H, Jahanshahi A, Ghafoorifard H. Highly conformable stretchable dry electrodes based on inexpensive flex substrate for long-term biopotential (EMG/ECG) monitoring. Sens Actuators, A. 2019; 295: 678-686.

[35]

Jung P-G, Lim G, Kim S, Kong K. A wearable gesture recognition device for detecting muscular activities based on air-pressure sensors. IEEE Trans Industr Inform. 2015; 11(2): 485-494.

[36]

Cai P, Wan C, Pan L, et al. Locally coupled electromechanical interfaces based on cytoadhesion-inspired hybrids to identify muscular excitation-contraction signatures. Nat Commun. 2020; 11(1): 2183.

[37]

Herrera-Luna I, Rechy-Ramirez EJ, Rios-Figueroa HV, Marin-Hernandez A. Sensor fusion used in applications for hand rehabilitation: a systematic review. IEEE Sens J.2019; 19(10): 3581-3592.

[38]

Tang W, Li C, Wang T, et al. Deep-learning model coupling wearable bioelectric and mechanical sensors for refined muscle strength assessment. Research. 2024; 7: 0366.

[39]

Nordez A, Gallot T, Catheline S, Guével A, Cornu C, Hug F. Electromechanical delay revisited using very high frame rate ultrasound. J Appl Physiol. 2009; 106(6): 1970-1975.

[40]

Gurev V, Constantino J, Rice J, Trayanova NA. Distribution of electromechanical delay in the heart: insights from a three-dimensional electromechanical model. Biophys J. 2010; 99(3): 745-754.

[41]

Ahmadizadeh C, Merhi LK, Pousett B, Sangha S, Menon C. Toward intuitive prosthetic control: solving common issues using force myography, surface electromyography, and pattern recognition in a pilot case study. IEEE Robotics & Automation Magazine; 2017, 24(4): 102-111.

[42]

Chen P, Li Z, Togo S, Yokoi H, Jiang Y. A layered sEMG-FMG hybrid sensor for hand motion recognition from forearm muscle activities. IEEE Transactions on Human-Machine Systems; 2023, 53(5): 935-944.

[43]

Islam MRU, Waris A, Kamavuako EN, Bai S. A comparative study of motion detection with FMG and sEMG methods for assistive applications. J Rehabil Assist Technol Eng. 2020; 7: 2055668320938588.

[44]

Chen Z, Wang H, Chen H, Wei T. Continuous motion finger joint angle estimation utilizing hybrid sEMG-FMG modality driven transformer-based deep learning model. Biomed Signal Process Control. 2023; 85: 105030.

[45]

Zou Y, Gai Y, Tan P, et al. Stretchable graded multichannel self-powered respiratory sensor inspired by shark gill. Fundam Res. 2022; 2(4): 619-628.

[46]

Fu R, Tu L, Zhou Y, et al. A tough and self-powered hydrogel for artificial skin. Chem Mater. 2019; 31(23): 9850-9860.

[47]

Liu Y, Wang C, Xue J, et al. Body temperature enhanced adhesive, antibacterial, and recyclable ionic hydrogel for epidermal electrophysiological monitoring. Adv Healthc Mater. 2022; 11(15): 2200653.

[48]

Liu Y, Wang C, Liu Z, et al. Self-encapsulated ionic fibers based on stress-induced adaptive phase transition for non-contact depth-of-field camouflage sensing. Nat Commun. 2024; 15(1): 663.

[49]

Wang C, Liu Y, Qu X, et al. Ultra-stretchable and fast self-healing ionic hydrogel in cryogenic environments for artificial nerve fiber. Adv Mater. 2022; 34(16): 2105416.

[50]

Nam S, Mooney D. Polymeric tissue adhesives. Chem Rev. 2021; 121(18): 11336-11384.

[51]

Yuk H, Varela CE, Nabzdyk CS, et al. Dry double-sided tape for adhesion of wet tissues and devices. Nature. 2019; 575(7781): 169-174.

RIGHTS & PERMISSIONS

2024 The Author(s). InfoMat published by UESTC and John Wiley & Sons Australia, Ltd.

AI Summary AI Mindmap
PDF

3

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/