{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:40:20Z","timestamp":1792788020893,"version":"4.5.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:00:00Z","timestamp":1751587200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12102127"],"award-info":[{"award-number":["12102127"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M690872"],"award-info":[{"award-number":["2021M690872"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Changzhou Health Commission Technology Projects","award":["ZD202103"],"award-info":[{"award-number":["ZD202103"]}]},{"name":"Changzhou Health Commission Technology Projects","award":["ZD202103"],"award-info":[{"award-number":["ZD202103"]}]},{"name":"Changzhou Health Commission Technology Projects","award":["ZD202103"],"award-info":[{"award-number":["ZD202103"]}]},{"name":"2021 Annual Performance Evaluation of Technology Transfer in Jiangning District","award":["521053812\/2093"],"award-info":[{"award-number":["521053812\/2093"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J NeuroEngineering Rehabil"],"DOI":"10.1186\/s12984-025-01680-2","type":"journal-article","created":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T22:40:21Z","timestamp":1751582421000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Exploratory development of human\u2013machine interaction strategies for post-stroke upper-limb rehabilitation"],"prefix":"10.1186","volume":"22","author":[{"given":"Kang","family":"Xia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue-Dong","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong-Shuai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Hang","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Han","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Min","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin-Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,4]]},"reference":[{"key":"1680_CR1","doi-asserted-by":"publisher","first-page":"4089","DOI":"10.2147\/JPR.S438745","volume":"16","author":"L Zhang","year":"2023","unstructured":"Zhang L, Zhang H, Zhou X, Zhao J, Wang X. Bibliometric analysis of research on migraine-stroke association from 2013 to 2023. J Pain Res. 2023;16:4089\u2013112.","journal-title":"J Pain Res"},{"issue":"9","key":"1680_CR2","doi-asserted-by":"publisher","first-page":"946","DOI":"10.1177\/17474930221123175","volume":"17","author":"T Thayabaranathan","year":"2022","unstructured":"Thayabaranathan T, Kim J, Cadilhac DA, Thrift AG, Donnan GA, Howard G, et al. Global stroke statistics 2022. Int J Stroke. 2022;17(9):946\u201356.","journal-title":"Int J Stroke"},{"issue":"9","key":"1680_CR3","doi-asserted-by":"publisher","first-page":"e100","DOI":"10.1161\/01.STR.0000180861.54180.FF","volume":"36","author":"PW Duncan","year":"2005","unstructured":"Duncan PW, Zorowitz R, Bates B, Choi JY, Glasberg JJ, Graham GD, et al. Management of adult stroke rehabilitation care: a clinical practice guideline. Stroke. 2005;36(9):e100\u201343.","journal-title":"Stroke"},{"issue":"1","key":"1680_CR4","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1186\/s12984-021-00875-7","volume":"18","author":"F Grimm","year":"2021","unstructured":"Grimm F, Kraugmann J, Naros G, Gharabaghi A. Clinical validation of kinematic assessments of post-stroke upper limb movements with a multi-joint arm exoskeleton. J Neuroeng Rehabil. 2021;18(1):92.","journal-title":"J Neuroeng Rehabil"},{"key":"1680_CR5","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2016.2552201","volume":"9","author":"T Proietti","year":"2016","unstructured":"Proietti T, Crocher V, Roby-Brami A, Jarrasse N. Upper-limb robotic exoskeletons for neurorehabilitation: a review on control strategies. IEEE Rev Biomed Eng. 2016;9:4\u201314.","journal-title":"IEEE Rev Biomed Eng"},{"issue":"6","key":"1680_CR6","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1191\/0269215505cr901oa","volume":"19","author":"D Lynch","year":"2005","unstructured":"Lynch D, Ferraro M, Krol J, Trudell CM, Christos P, Volpe BT. Continuous passive motion improves shoulder joint integrity following stroke. Clin Rehabil. 2005;19(6):594\u20139.","journal-title":"Clin Rehabil"},{"issue":"5","key":"1680_CR7","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1136\/jnnp-2019-320441","volume":"90","author":"SC Cramer","year":"2019","unstructured":"Cramer SC. Intense rehabilitation therapy produces very large gains in chronic stroke. J Neurol Neurosurg Psychiatry. 2019;90(5):497\u2013497.","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"1680_CR8","doi-asserted-by":"publisher","first-page":"1172958","DOI":"10.3389\/fphys.2023.1172958","volume":"14","author":"Y Huo","year":"2023","unstructured":"Huo Y, Wang X, Zhao W, Hu H, Li L. Effects of EMG-based robot for upper extremity rehabilitation on post-stroke patients: a systematic review and meta-analysis. Front Physiol. 2023;14:1172958.","journal-title":"Front Physiol"},{"key":"1680_CR9","doi-asserted-by":"crossref","unstructured":"Haider F, Kadhim DA, Hussein JS: A comparative review of upper limb exoskeletons for rehabilitation. In: AIP Conference Proceedings. 2024.","DOI":"10.1063\/5.0205039"},{"issue":"1","key":"1680_CR10","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1515\/mr-2023-0054","volume":"4","author":"R Hong","year":"2024","unstructured":"Hong R, Li B, Bao Y, Liu L, Jin L. Therapeutic robots for post-stroke rehabilitation. Med Rev. 2024;4(1):55\u201367.","journal-title":"Med Rev"},{"issue":"1","key":"1680_CR11","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1186\/s12984-022-00991-y","volume":"19","author":"A Frisoli","year":"2022","unstructured":"Frisoli A, Barsotti M, Sotgiu E, Lamola G, Procopio C, Chisari C. A randomized clinical control study on the efficacy of three-dimensional upper limb robotic exoskeleton training in chronic stroke. J Neuroeng Rehabil. 2022;19(1):14.","journal-title":"J Neuroeng Rehabil"},{"issue":"4","key":"1680_CR12","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1109\/THMS.2021.3086003","volume":"51","author":"L Guo","year":"2021","unstructured":"Guo L, Lu Z, Yao L. Human-machine interaction sensing technology based on hand gesture recognition: a review. IEEE T Hum-Mach Syst. 2021;51(4):300\u20139.","journal-title":"IEEE T Hum-Mach Syst"},{"issue":"22","key":"1680_CR13","doi-asserted-by":"publisher","first-page":"6451","DOI":"10.3390\/s20226451","volume":"20","author":"N Nasri","year":"2020","unstructured":"Nasri N, Orts-Escolano S, Cazorla M. An sEMG-controlled 3D game for rehabilitation therapies: real-time time hand gesture recognition using deep learning techniques. Sensors-Basel. 2020;20(22):6451.","journal-title":"Sensors-Basel"},{"key":"1680_CR14","doi-asserted-by":"publisher","first-page":"84070","DOI":"10.1109\/ACCESS.2020.2991812","volume":"8","author":"SY Gordleeva","year":"2020","unstructured":"Gordleeva SY, Lobov SA, Grigorev NA, Savosenkov AO, Shamshin MO, Lukoyanov MV, et al. Real-time EEG\u2013EMG human\u2013machine interface-based control system for a lower-limb exoskeleton. IEEE Access. 2020;8:84070\u201381.","journal-title":"IEEE Access"},{"issue":"6","key":"1680_CR15","doi-asserted-by":"publisher","first-page":"2101","DOI":"10.3390\/app10062101","volume":"10","author":"Z Pang","year":"2020","unstructured":"Pang Z, Wang T, Wang Z, Yu J, Sun Z, Liu S. Design and analysis of a wearable upper limb rehabilitation robot with characteristics of tension mechanism. Appl Sci Basel. 2020;10(6):2101.","journal-title":"Appl Sci Basel."},{"issue":"4","key":"1680_CR16","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1177\/0278364917706743","volume":"36","author":"B Kim","year":"2017","unstructured":"Kim B, Deshpande AD. An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: design, modeling, control, and performance evaluation. Int J Robot Res. 2017;36(4):414\u201335.","journal-title":"Int J Robot Res"},{"issue":"1","key":"1680_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10846-021-01353-x","volume":"102","author":"H Majidi Fard Vatan","year":"2021","unstructured":"Majidi Fard Vatan H, Nefti-Meziani S, Davis S, Saffari Z, El-Hussieny H. A review: a comprehensive review of soft and rigid wearable rehabilitation and assistive devices with a focus on the shoulder joint. J Intel Robot Syst. 2021;102(1):1\u201324.","journal-title":"J Intel Robot Syst."},{"issue":"2","key":"1680_CR18","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1080\/11762320902840179","volume":"6","author":"T Nef","year":"2009","unstructured":"Nef T, Guidali M, Riener R. ARMin III\u2014arm therapy rxoskeleton with an ergonomic shoulder actuation. Appl Bionics Biomech. 2009;6(2):127\u201342.","journal-title":"Appl Bionics Biomech"},{"issue":"4","key":"1680_CR19","doi-asserted-by":"publisher","first-page":"1880","DOI":"10.1109\/TMECH.2016.2558646","volume":"21","author":"N Vitiello","year":"2016","unstructured":"Vitiello N, Cempini M, Crea S, Giovacchini F, Cortese M, Moise M, et al. Functional design of a powered elbow orthosis toward its clinical employment. IEEE-ASME T Mech. 2016;21(4):1880\u201391.","journal-title":"IEEE-ASME T Mech"},{"issue":"4","key":"1680_CR20","doi-asserted-by":"publisher","first-page":"1485","DOI":"10.1109\/TMECH.2019.2907465","volume":"24","author":"E Trigili","year":"2019","unstructured":"Trigili E, Crea S, Moise M, Baldoni A, Cempini M, Ercolini G, et al. Design and experimental characterization of a shoulder-elbow exoskeleton with compliant joints for post-stroke rehabilitation. IEEE-ASME T Mech. 2019;24(4):1485\u201396.","journal-title":"IEEE-ASME T Mech"},{"issue":"1","key":"1680_CR21","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s12984-023-01298-2","volume":"21","author":"HY Hsu","year":"2024","unstructured":"Hsu HY, Koh CL, Yang KC, Lin YC, Hsu CH, Su FC, et al. Effects of an assist-as-needed equipped tenodesis-induced-grip exoskeleton robot (TIGER) on upper limb function in patients with chronic stroke. J Neuroeng Rehabil. 2024;21(1):5.","journal-title":"J Neuroeng Rehabil"},{"key":"1680_CR22","doi-asserted-by":"crossref","unstructured":"Pignolo L, Dolce G, Basta G, Lucca LF, Sannita WG. upper limb rehabilitation after stroke: ARAMIS a \u201crobo-mechatronic\u201d innovative approach and prototype. In: 2012 4th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob). 2012.","DOI":"10.1109\/BioRob.2012.6290868"},{"key":"1680_CR23","doi-asserted-by":"crossref","unstructured":"Guo S, Zhang W, Wei W, Guo J, Wang Y. A kinematic model of an upper limb rehabilitation robot system. In: 2013 IEEE International Conference on Mechatronics and Automation. 2013.","DOI":"10.1109\/ICMA.2013.6618046"},{"issue":"3","key":"1680_CR24","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1109\/TNSRE.2012.2225073","volume":"21","author":"Y Ren","year":"2013","unstructured":"Ren Y, Kang SH, Park HS, Wu YN, Zhang LQ. Developing a multi-joint upper limb exoskeleton robot for diagnosis, therapy, and outcome evaluation in neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2013;21(3):490\u20139.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1680_CR25","doi-asserted-by":"crossref","unstructured":"Rahman MH, Ouimet TK, Saad M, P. KJ, Archambault PS. Development and control of a wearable robot for rehabilitation of elbow and shoulder joint movements. In: IECON 2010-36th Annual Conference on IEEE Industrial Electronics Society. 2010.","DOI":"10.1109\/IECON.2010.5675459"},{"issue":"3","key":"1680_CR26","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TFUZZ.2014.2317511","volume":"23","author":"Z Li","year":"2015","unstructured":"Li Z, Su C-Y, Li G, Su H. Fuzzy approximation-based adaptive backstepping control of an exoskeleton for human upper limbs. IEEE Trans Fuzzy Syst. 2015;23(3):555\u201366.","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"13","key":"1680_CR27","doi-asserted-by":"publisher","first-page":"1656","DOI":"10.1177\/0278364912455366","volume":"31","author":"A M\u00f6rtl","year":"2012","unstructured":"M\u00f6rtl A, Lawitzky M, Kucukyilmaz A, Sezgin M, Basdogan C, Hirche S. The role of roles: physical cooperation between humans and robots. Int J Robot Res. 2012;31(13):1656\u201374.","journal-title":"Int J Robot Res"},{"issue":"19","key":"1680_CR28","doi-asserted-by":"publisher","first-page":"eaaz8693","DOI":"10.1126\/sciadv.aaz8693","volume":"6","author":"M Zhu","year":"2020","unstructured":"Zhu M, Sun Z, Zhang Z, Shi Q, He T, Liu H, et al. Haptic-feedback smart glove as a creative human-machine interface (HMI) for virtual\/augmented reality applications. Sci Adv. 2020;6(19):eaaz8693.","journal-title":"Sci Adv"},{"issue":"11","key":"1680_CR29","first-page":"682","volume":"9","author":"K Xia","year":"2022","unstructured":"Xia K, Chen X, Chang X, Liu C, Guo L, Xu X, et al. Hand exoskeleton design and human\u2013machine interaction strategies for rehabilitation. Bioeng. 2022;9(11):682.","journal-title":"Bioeng"},{"issue":"4","key":"1680_CR30","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1007\/s11370-022-00435-5","volume":"15","author":"P Xu","year":"2022","unstructured":"Xu P, Xia D, Li J, Zhou J, Xie L. Execution and perception of upper limb exoskeleton for stroke patients: a systematic review. Intel Serv Robot. 2022;15(4):557\u201378.","journal-title":"Intel Serv Robot"},{"issue":"4","key":"1680_CR31","doi-asserted-by":"publisher","first-page":"741","DOI":"10.25259\/JNRP_134_2023","volume":"14","author":"WM Alzahrani","year":"2023","unstructured":"Alzahrani WM, Aljundi ZE, Sulaiman AA, Bagadood RM. Acute spasticity secondary to ischemic stroke involving superior frontal gyrus and anterior cingulate gyrus. J Neurosci Rural Pract. 2023;14(4):741\u20133.","journal-title":"J Neurosci Rural Pract"},{"key":"1680_CR32","doi-asserted-by":"publisher","first-page":"2100211","DOI":"10.1109\/JTEHM.2020.3042931","volume":"9","author":"MMC Bhagubai","year":"2021","unstructured":"Bhagubai MMC, Wolterink G, Schwarz A, Held JPO, Van Beijnum BF, Veltink PH. Quantifying pathological synergies in the upper extremity of stroke subjects with the use of inertial measurement units: a pilot study. IEEE J Transl Eng Health Med. 2021;9:2100211.","journal-title":"IEEE J Transl Eng Health Med"},{"issue":"6","key":"1680_CR33","doi-asserted-by":"publisher","first-page":"5903","DOI":"10.1109\/LRA.2024.3398565","volume":"9","author":"RS Zarrin","year":"2024","unstructured":"Zarrin RS, Zeiaee A, Langari R. A variable-admittance assist-as-needed controller for upper-limb rehabilitation exoskeletons. IEEE Robot Autom Lett. 2024;9(6):5903\u201310.","journal-title":"IEEE Robot Autom Lett"},{"issue":"2","key":"1680_CR34","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1109\/TNSRE.2012.2207462","volume":"21","author":"H Kim","year":"2013","unstructured":"Kim H, Miller LM, Fedulow I, Simkins M, Abrams GM, Byl N, et al. Kinematic data analysis for post-stroke patients following bilateral versus unilateral rehabilitation with an upper limb wearable robotic system. IEEE Trans Neural Syst Rehabil Eng. 2013;21(2):153\u201364.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1680_CR35","unstructured":"Ding M, Ueda J, Ogasawara T. Pinpointed muscle force control using a power-assisting device: System configuration and experiment. In: 2008 2nd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics. 2008."},{"key":"1680_CR36","doi-asserted-by":"crossref","unstructured":"Ding M, Hirasawa K, Kurita Y, Takemura H, Takamatsu J, Mizoguchi H, et al. Pinpointed muscle force control in consideration Of human motion and external force. In: 2010 IEEE International Conference on Robotics and Biomimetics. 2010.","DOI":"10.1109\/ROBIO.2010.5723418"},{"issue":"3","key":"1680_CR37","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1109\/TNSRE.2008.918389","volume":"16","author":"ET Wolbrecht","year":"2008","unstructured":"Wolbrecht ET, Chan V, Reinkensmeyer DJ, Bobrow JE. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation. IEEE Trans Neural Syst Rehabil Eng. 2008;16(3):286\u201397.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"1680_CR38","doi-asserted-by":"crossref","unstructured":"Balasubramanian S, Wei R, Perez M, Shepard B, He J. RUPERT: an exoskeleton robot for assisting rehabilitation of arm functions. In: 2008 Virtual Rehabilitation. 2008.","DOI":"10.1109\/ICVR.2008.4625154"},{"key":"1680_CR39","doi-asserted-by":"crossref","unstructured":"Zhang H, Austin H, Buchanan S, Herman R, Koeneman J, He J. Feasibility Studies of Robot-Assisted Stroke Rehabilitation at Clinic and Home Settings Using RUPERT. In: 2011 IEEE International Conference on Rehabilitation Robotics. 2011.","DOI":"10.1109\/ICCME.2011.5876812"},{"issue":"10","key":"1680_CR40","doi-asserted-by":"publisher","first-page":"2867","DOI":"10.1109\/TBME.2011.2161671","volume":"58","author":"H Huang","year":"2011","unstructured":"Huang H, Zhang F, Hargrove LJ, Dou Z, Rogers DR, Englehart KB. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion. IEEE Trans Biomed Eng. 2011;58(10):2867\u201375.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"1680_CR41","doi-asserted-by":"publisher","first-page":"1047","DOI":"10.3390\/s24041047","volume":"24","author":"IB Abdallah","year":"2024","unstructured":"Abdallah IB, Bouteraa Y. An optimized stimulation control system for upper limb exoskeleton robot-assisted rehabilitation using a fuzzy logic-based pain detection approach. Sensors. 2024;24(4):1047.","journal-title":"Sensors"},{"issue":"6","key":"1680_CR42","doi-asserted-by":"publisher","first-page":"1649","DOI":"10.1109\/TBME.2012.2191551","volume":"59","author":"X Zhang","year":"2012","unstructured":"Zhang X, Zhou P. High-density myoelectric pattern recognition toward improved stroke rehabilitation. IEEE Trans Biomed Eng. 2012;59(6):1649\u201357.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"1680_CR43","doi-asserted-by":"publisher","DOI":"10.1088\/1741-2552\/ab9db9","volume":"17","author":"F Xiao","year":"2020","unstructured":"Xiao F, Chen Y, Zhu Y. GADF\/GASF-HOG:feature extraction methods for hand movement classification from surface electromyography. J Neural Eng. 2020;17(4): 046016.","journal-title":"J Neural Eng"},{"key":"1680_CR44","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1109\/TNSRE.2022.3172974","volume":"30","author":"Z Tang","year":"2022","unstructured":"Tang Z, Zhang L, Chen X, Ying J, Wang X, Wang H. Wearable supernumerary robotic limb system using a hybrid control approach based on motor imagery and object detection. IEEE Trans Neural Syst Rehabil Eng. 2022;30:1298\u2013309.","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"issue":"4","key":"1680_CR45","doi-asserted-by":"publisher","first-page":"5012","DOI":"10.1109\/JSEN.2020.3033256","volume":"21","author":"J Tryon","year":"2021","unstructured":"Tryon J, Trejos AL. Classification of task weight during dynamic motion using EEG\u2013EMG fusion. IEEE Sens J. 2021;21(4):5012\u201321.","journal-title":"IEEE Sens J"},{"issue":"4","key":"1680_CR46","doi-asserted-by":"publisher","first-page":"825","DOI":"10.3390\/s17040825","volume":"17","author":"C Tunca","year":"2017","unstructured":"Tunca C, Pehlivan N, Ak N, Arnrich B, Salur G, Ersoy C. Inertial sensor-based robust gait analysis in non-hospital settings for neurological disorders. Sensors-Basel. 2017;17(4):825.","journal-title":"Sensors-Basel"},{"issue":"5","key":"1680_CR47","doi-asserted-by":"publisher","first-page":"579","DOI":"10.3390\/e22050579","volume":"22","author":"S Tahir","year":"2020","unstructured":"Tahir S, Jalal A, Kim K. Wearable inertial sensors for daily activity analysis based on adam optimization and the maximum entropy markov model. Entropy. 2020;22(5):579.","journal-title":"Entropy"},{"key":"1680_CR48","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.future.2021.01.020","volume":"119","author":"L Liu","year":"2021","unstructured":"Liu L. Objects detection toward complicated high remote basketball sports by leveraging deep CNN architecture. Future Gener Comp Sy. 2021;119:31\u20136.","journal-title":"Future Gener Comp Sy"},{"issue":"7","key":"1680_CR49","doi-asserted-by":"publisher","first-page":"6429","DOI":"10.1109\/JIOT.2020.2985082","volume":"7","author":"X Zhou","year":"2020","unstructured":"Zhou X, Liang W, Wang KIK, Wang H, Yang LT, Jin Q. Deep-learning-enhanced human activity recognition for internet of healthcare things. IEEE Internet Things. 2020;7(7):6429\u201338.","journal-title":"IEEE Internet Things"},{"issue":"6","key":"1680_CR50","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"A Krizhevsky","year":"2017","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84\u201390.","journal-title":"Commun ACM"},{"key":"1680_CR51","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1680_CR52","unstructured":"Lipton ZC, Berkowitz J, Elkan C. A critical review of recurrent neural networks for sequence learning. arXiv preprint. 2015. arXiv:1506.00019."},{"issue":"8","key":"1680_CR53","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735\u201380.","journal-title":"Neural Comput"},{"key":"1680_CR54","unstructured":"Bai S, Kolter JZ, Koltun V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint. 2018. arXiv:1803.01271."},{"key":"1680_CR55","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: 31st International Conference on Neural Information Processing Systems. 2017. p. 6000\u20136010."},{"key":"1680_CR56","first-page":"22419","volume":"34","author":"H Wu","year":"2021","unstructured":"Wu H, Xu J, Wang J, Long M. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Adv Neural Inf Process Syst. 2021;34:22419\u201330.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"1680_CR57","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3533382","volume":"55","author":"K Benidis","year":"2022","unstructured":"Benidis K, Rangapuram SS, Flunkert V, Wang Y, Maddix D, Turkmen C, et al. Deep learning for time series forecasting: tutorial and literature survey. ACM Comput Surv. 2022;55(6):1\u201336.","journal-title":"ACM Comput Surv"},{"issue":"3","key":"1680_CR58","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1097\/01241398-199805000-00022","volume":"18","author":"JCY Cheng","year":"1998","unstructured":"Cheng JCY, Leung SSF, Chiu BSK, Tse PWT, Xu YYY. Can we predict body height from segmental bone length measurements? A study of 3,647 children. J Pediatr Orthoped. 1998;18(3):387\u201393.","journal-title":"J Pediatr Orthoped"},{"key":"1680_CR59","first-page":"259","volume-title":"Wearable robots: biomechatronic exoskeletons","author":"JC Perry","year":"2008","unstructured":"Perry JC, Rosen J. Case study: an upper limb powered exoskeleton. In: Pons JL, editor. Wearable robots: biomechatronic exoskeletons. Chichester: John Wiley & Sons Ltd; 2008. p. 259\u201369."},{"issue":"9","key":"1680_CR60","doi-asserted-by":"publisher","first-page":"2467","DOI":"10.3390\/s20092467","volume":"20","author":"A Jaramillo-Yanez","year":"2020","unstructured":"Jaramillo-Yanez A, Benalcazar ME, Mena-Maldonado E. Real-time hand gesture recognition using surface electromyography and machine learning: a systematic literature review. Sensors Basel. 2020;20(9):2467.","journal-title":"Sensors Basel"},{"issue":"6","key":"1680_CR61","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/S0003-9993(97)90427-5","volume":"78","author":"B Kopp","year":"1997","unstructured":"Kopp B, Kunkel A, Flor H, Platz T, Rose U, Mauritz K-H, et al. The arm motor ability test: reliability, validity, and sensitivity to change of an instrument for assessing disabilities in activities of daily living. Arch Phys Med Rehab. 1997;78(6):615\u201320.","journal-title":"Arch Phys Med Rehab"},{"key":"1680_CR62","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint. 2017. arXiv:1704.04861."},{"key":"1680_CR63","unstructured":"Wu H, Hu T, Liu Y, Zhou H, Wang J, Long M. Timesnet: temporal 2d-variation modeling for general time series analysis. arXiv preprint. 2022. arXiv:2210.02186."},{"key":"1680_CR64","doi-asserted-by":"crossref","unstructured":"Zhou H, Zhang S, Peng J, Zhang S, Li J, Xiong H, et al. Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI conference on artificial intelligence. 2021. p. 11106\u201311115.","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"1680_CR65","doi-asserted-by":"crossref","unstructured":"Howard A, Sandler M, Chu G, Chen LC, Chen B, Tan M, et al. Searching for mobilenetv3. In: Proceedings of the IEEE\/CVF international conference on computer vision. 2019. p. 1314\u20131324.","DOI":"10.1109\/ICCV.2019.00140"},{"issue":"71","key":"1680_CR66","first-page":"1","volume":"59","author":"B Sundararaman","year":"2023","unstructured":"Sundararaman B, Jain P. Fault detection and classification in electrical power transmission system using wavelet transform. Eng Proc. 2023;59(71):1\u20138.","journal-title":"Eng Proc"},{"issue":"9","key":"1680_CR67","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1249\/MSS.0b013e3182923b2a","volume":"45","author":"WH Ho","year":"2013","unstructured":"Ho WH, Shiang TY, Lee CC, Cheng SY. Body segment parameters of young Chinese men determined with magnetic resonance imaging. Med Sci Sports Exerc. 2013;45(9):1759\u201366.","journal-title":"Med Sci Sports Exerc"},{"key":"1680_CR68","first-page":"1233","volume":"50","author":"Y Shao","year":"2020","unstructured":"Shao Y, Liu L, Huang L, Deng N. Key issues of support vector machines and future prospects. Acta Math Sin Chin Ser. 2020;50:1233\u201348.","journal-title":"Acta Math Sin Chin Ser."},{"key":"1680_CR69","doi-asserted-by":"crossref","unstructured":"Yan S, Xiong Y, Lin D. Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition. In: Proceedings of the AAAI conference on artificial intelligence. 2018.","DOI":"10.1609\/aaai.v32i1.12328"},{"key":"1680_CR70","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, Sermanet P, Rabinovich A. Going Deeper with Convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2014. p. 1\u20139.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"1680_CR71","unstructured":"Bagnall A, Dau HA, Lines J, Flynn M, Large J, Bostrom A, et al. The UEA multivariate time series classification archive. arXiv preprint. 2018. arXiv:1811.00075."},{"key":"1680_CR72","doi-asserted-by":"crossref","unstructured":"Jefferson, Koonce B. ResNet 34. In: Koonce B, editors. Convolutional neural networks with swift for tensorflow: image recognition dataset categorization. Berkeley: Apress; 2021. p. 51\u201361.","DOI":"10.1007\/978-1-4842-6168-2_5"}],"container-title":["Journal of NeuroEngineering and Rehabilitation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12984-025-01680-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12984-025-01680-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12984-025-01680-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,4]],"date-time":"2025-07-04T00:03:14Z","timestamp":1751587394000},"score":1,"resource":{"primary":{"URL":"https:\/\/jneuroengrehab.biomedcentral.com\/articles\/10.1186\/s12984-025-01680-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,4]]},"references-count":72,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1680"],"URL":"https:\/\/doi.org\/10.1186\/s12984-025-01680-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5002266\/v1","asserted-by":"object"}]},"ISSN":["1743-0003"],"issn-type":[{"value":"1743-0003","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,4]]},"assertion":[{"value":"30 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Prior to participation, all subjects were informed of the experimental procedure, and each provided written consent. Health subjects involved were authors of this study. The proof-of-concept study was conducted in the Third Affiliated Hospital of Soochow University and was approved by the institutional review board (IRB) and ethical committee of The First People\u2019s Hospital of Changzhou (2024 Science No. 143).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"All participants provided written and informed consent for the publication of these data.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"144"}}