{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:29:16Z","timestamp":1792801756233,"version":"4.5.0"},"reference-count":41,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T00:00:00Z","timestamp":1773964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2023YFC3604300"],"award-info":[{"award-number":["2023YFC3604300"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging\u2013splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation.<\/jats:p>","DOI":"10.3390\/s26061952","type":"journal-article","created":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T09:44:41Z","timestamp":1773999881000},"page":"1952","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["sEMG-Based Muscle Synergy Analysis and Functional Driving Ratio for Quantitative Assessment During Robot-Assisted Upper-Limb Rehabilitation"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0589-1460","authenticated-orcid":false,"given":"Baitian","family":"Tan","sequence":"first","affiliation":[{"name":"Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China"},{"name":"Shanghai Rehabilitation Assistive Device Engineering Technology Research Center, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6022-4194","authenticated-orcid":false,"given":"Jiang","family":"Shao","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China"},{"name":"Shanghai Rehabilitation Assistive Device Engineering Technology Research Center, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingwen","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China"},{"name":"Shanghai Rehabilitation Assistive Device Engineering Technology Research Center, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sujiao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China"},{"name":"Shanghai Rehabilitation Assistive Device Engineering Technology Research Center, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliu","family":"Yu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Rehabilitation Engineering, University of Shanghai for Science and Technology, No. 516 Jungong Road, Shanghai 200093, China"},{"name":"Shanghai Rehabilitation Assistive Device Engineering Technology Research Center, Shanghai 200093, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Caplan, L.R., Simon, R.P., and Hassani, S. (2023). Cerebrovascular disease-stroke. Neurobiology of Brain Disorders, Academic. [2nd ed.].","DOI":"10.1016\/B978-0-323-85654-6.00044-7"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"795","DOI":"10.1016\/S1474-4422(21)00252-0","article-title":"Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: A systematic analysis for the Global Burden of Disease Study 2019","volume":"20","author":"Feigin","year":"2021","journal-title":"Lancet Neurol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.neuron.2020.01.039","article-title":"Advanced neurotechnologies for the restoration of motor function","volume":"105","author":"Micera","year":"2020","journal-title":"Neuron"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1964","DOI":"10.1109\/JBHI.2020.3024589","article-title":"Novel measures of similarity and asymmetry in upper limb activities for identifying hemiparetic severity in stroke survivors","volume":"25","author":"Datta","year":"2021","journal-title":"IEEE J. Biomed. Health Informat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.3233\/THC-161256","article-title":"Muscle synergy extraction during arm reaching movements at different speeds","volume":"25","author":"Sabzevari","year":"2017","journal-title":"Technol. Health Care"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1152\/japplphysiol.01070.2003","article-title":"The extraction of neural strategies from the surface EMG","volume":"96","author":"Farina","year":"2004","journal-title":"J. Appl. Physiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1109\/TNSRE.2015.2483375","article-title":"Robustness and reliability of synergy-based myocontrol of a multiple degree of freedom robotic arm","volume":"24","author":"Lunardini","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7601","DOI":"10.1073\/pnas.0901512106","article-title":"Simplified and effective motor control based on muscle synergies to exploit musculoskeletal dynamics","volume":"106","author":"Berniker","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1186\/s12984-016-0183-0","article-title":"Role of Muscle Synergies in Real-Time Classification of Upper Limb Motions using Extreme Learning Machines","volume":"13","author":"Antuvan","year":"2016","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"036004","DOI":"10.1088\/1741-2560\/6\/3\/036004","article-title":"Muscle synergies as a predictive framework for the EMG patterns of new hand postures","volume":"6","author":"Ajiboye","year":"2009","journal-title":"J. Neural Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1109\/TBME.2008.2007967","article-title":"Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses from the Surface Electromyographic Signal","volume":"56","author":"Ning","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/s11044-023-09887-8","article-title":"Kinematics, dynamics, and muscle-synergy analysis of single-leg Yoga postures","volume":"58","author":"Menegaldo","year":"2023","journal-title":"Multibody Syst. Dyn."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"An, Q., Ishikawa, Y., Funato, T., Aoi, S., Oka, H., Yamakawa, H., Yamashita, A., and Asama, H. (2016). Muscle Synergy Analysis of Human Standing-up Motion Using Forward Dynamic Simulation with Four Body Segment Model. Distributed Autonomous Robotics Systems, Springer.","DOI":"10.1007\/978-4-431-55879-8_32"},{"key":"ref_14","first-page":"441","article-title":"The effect of training trajectories on upper limb muscle synergy","volume":"38","author":"He","year":"2019","journal-title":"Beijing Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Sadaka-Stephan, A., Pirondini, E., Coscia, M., and Micera, S. (2015). Influence of trajectory and speed profile on muscle organization during robot-aided training. Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), IEEE.","DOI":"10.1109\/ICORR.2015.7281206"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s00221-019-05679-9","article-title":"Muscle activities in similar arms performing identical tasks reveal the neural basis of muscle synergies","volume":"238","author":"Pellegrino","year":"2020","journal-title":"Exp. Brain Res."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Maistrello, L., Rimini, D., Cheung, V.C.K., Pregnolato, G., and Turolla, A. (2021). Muscle synergies and clinical outcome measures describe different factors of upper limb motor function in stroke survivors undergoing rehabilitation in a virtual reality environment. Sensors, 21.","DOI":"10.3390\/s21238002"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.gaitpost.2013.01.020","article-title":"The influence of locomotor rehabilitation on module quality and post-stroke hemiparetic walking performance","volume":"38","author":"Routson","year":"2013","journal-title":"Gait Posture"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Roh, J., Rymer, W.Z., and Beer, R.F. (2015). Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment. Front. Hum. Neurosci., 9.","DOI":"10.3389\/fnhum.2015.00006"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Camardella, C., Tse, K.C., Frisoli, A., and Tong, R.K.Y. (2020). Investigating muscle synergies changes after rehabilitation robotics training on stroke survivors: A pilot study. Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE.","DOI":"10.1109\/EMBC44109.2020.9175726"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Israely, S., Leisman, G., Machluf, C.C., and Carmeli, E. (2018). Muscle synergies control during hand-reaching tasks in multiple directions post-stroke. Front. Comput. Neurosci., 12.","DOI":"10.3389\/fncom.2018.00010"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, S., Zhuang, C., Niu, C.M., Bao, Y., Xie, Q., and Lan, N. (2017). Evaluation of functional correlation of task-specific muscle synergies with motor performance in patients poststroke. Front. Neurol., 8.","DOI":"10.3389\/fneur.2017.00337"},{"key":"ref_23","unstructured":"Chinese Society of Neurology, and Cerebrovascular Disease Group (2019). Diagnostic Criteria for Major Cerebrovascular Diseases in China 2019. Chin. J. Neurol., 52, 710\u2013715."},{"key":"ref_24","unstructured":"Li, Y.Q. (2020). Effect of Upper Limb Rehabilitation Robot Combined with Conventional Upper Limb Rehabilitation Training on Upper Limb Function in Stroke Patients during the Recovery Period. [Ph.D. Thesis, Southern Medical University]."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.3233\/THC-231076","article-title":"Research on multi-dimensional intelligent quantitative assessment of upper limb function based on kinematic parameters","volume":"32","author":"Li","year":"2024","journal-title":"Technol. Health Care"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Delis, I., Berret, B., Pozzo, T., and Panzeri, S. (2013). Quantitative evaluation of muscle synergy models: A single-trial task decoding approach. Front. Comput. Neurosci., 7.","DOI":"10.3389\/fncom.2013.00008"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1657","DOI":"10.1109\/TNSRE.2024.3389022","article-title":"Muscle synergy plasticity in motor function recovery after stroke","volume":"32","author":"Sheng","year":"2024","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"14652","DOI":"10.1073\/pnas.1212056109","article-title":"Muscle synergy patterns as physiological markers of motor cortical damage","volume":"109","author":"Cheung","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"19563","DOI":"10.1073\/pnas.0910114106","article-title":"Stability of muscle synergies for voluntary actions after cortical stroke in humans","volume":"106","author":"Cheung","year":"2009","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1152\/jn.00825.2009","article-title":"Merging of Healthy Motor Modules Predicts Reduced Locomotor Performance and Muscle Coordination Complexity Post-Stroke","volume":"103","author":"Clark","year":"2010","journal-title":"J. Neurophysiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s12984-023-01301-w","article-title":"Decoding hand and wrist movement intention from chronic stroke survivors with hemiparesis using a user-friendly, wearable EMG-based neural interface","volume":"21","author":"Meyers","year":"2024","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1109\/LRA.2020.2969942","article-title":"Temporal muscle synergy features estimate effects of short-term rehabilitation in sit-to-stand of post-stroke patients","volume":"5","author":"Yang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hesam-Shariati, N., Trinh, T., Thompson-Butel, A.G., Shiner, C.T., and McNulty, P.A. (2017). A Longitudinal Electromyography Study of Complex Movements in Poststroke Therapy. 2: Changes in Coordinated Muscle Activation. Front. Neurol., 8.","DOI":"10.3389\/fneur.2017.00277"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chan, H.L., Meng, L.F., Kao, Y.A., Chang, Y.J., Chang, H.W., Chen, S.W., and Wu, C.Y. (2024). Myoelectric, Myo-Oxygenation, and Myotonometry Changes during Robot-Assisted Bilateral Arm Exercises with Varying Resistances. Sensors, 24.","DOI":"10.3390\/s24041061"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"8491","DOI":"10.1109\/JSEN.2023.3240170","article-title":"Continuous estimation of the lower-limb multi-joint angles based on muscle synergy theory and state-space model","volume":"23","author":"Qin","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_36","first-page":"66","article-title":"Muscle Coactivation Analysis during Upper-Limb Rehabilitation using Haptic Robotics in Stroke Survivors","volume":"45","author":"Oh","year":"2024","journal-title":"J. Biomed. Eng. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/OJEMB.2024.3363137","article-title":"Feedback type may change the EMG pattern and kinematics during robot supported upper limb reaching task","volume":"5","author":"Kato","year":"2024","journal-title":"IEEE Open J. Eng. Med. Biol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Daszkiewicz, M., Prill, R., Reichert, P., Becker, R., Oleksy, \u0141., Ku\u017aniecow, M., Lech, M., Ku\u0142akowski, M., Kentel, M., and Kentel, M. (2024). The Development and Reliability of a Surface Electromyography-Based Index for Quantifying Knee Muscle Coactivation During the Lower Quarter Y-Balance Test. Appl. Sci., 14.","DOI":"10.3390\/app14219788"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, S., Hase, K., and Funato, T. (2023). Computational prediction of muscle synergy using a finite element framework for a musculoskeletal model on lower limb. Front. Bioeng. Biotechnol., 11.","DOI":"10.3389\/fbioe.2023.1130219"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ye, S., Tao, L., Gong, S., Ma, Y., Wu, J., Li, W., Kang, J., Tang, M., Zuo, G., and Shi, C. (2024). Upper limb motor assessment for stroke with force, muscle activation and interhemispheric balance indices based on sEMG and fNIRS. Front. Neurol., 15.","DOI":"10.3389\/fneur.2024.1337230"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, K., He, C., Xiang, W., Zhou, Y., Zhang, Z., Li, J., and Scano, A. (2023). Evidence of synergy coordination patterns of upper-limb motor control in stroke patients with mild and moderate impairment. Front. Physiol., 14.","DOI":"10.3389\/fphys.2023.1214995"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1952\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T05:19:56Z","timestamp":1774156796000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/6\/1952"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,20]]},"references-count":41,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["s26061952"],"URL":"https:\/\/doi.org\/10.3390\/s26061952","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,20]]}}}