{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:27:05Z","timestamp":1765268825439,"version":"build-2065373602"},"reference-count":111,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T00:00:00Z","timestamp":1653004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"],"award-info":[{"award-number":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"]}]},{"name":"the Zhejiang Provincial Natural Science Foundation of China","award":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"],"award-info":[{"award-number":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"]}]},{"name":"Open Fund of the State Key Laboratory of Fluid Power and Mechatronic Systems","award":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"],"award-info":[{"award-number":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"]}]},{"name":"DongGuan Innovative Research Team Program","award":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"],"award-info":[{"award-number":["52175033","U21A20120","U1913601","LZ20E050002","GZKF-202101","2020607202006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Electronics"],"abstract":"<jats:p>Gait recognition and rehabilitation has been a research hotspot in recent years due to its importance to medical care and elderly care. Active intelligent rehabilitation and assistance systems for lower limbs integrates mechanical design, sensing technology, intelligent control, and robotics technology, and is one of the effective ways to resolve the above problems. In this review, crucial technologies and typical prototypes of active intelligent rehabilitation and assistance systems for gait training are introduced. The limitations, challenges, and future directions in terms of gait measurement and intention recognition, gait rehabilitation evaluation, and gait training control strategies are discussed. To address the core problems of the sensing, evaluation and control technology of the active intelligent gait training systems, the possible future research directions are proposed. Firstly, different sensing methods need to be proposed for the decoding of human movement intention. Secondly, the human walking ability evaluation models will be developed by integrating the clinical knowledge and lower limb movement data. Lastly, the personalized gait training strategy for collaborative control of human\u2013machine systems needs to be implemented in the clinical applications.<\/jats:p>","DOI":"10.3390\/electronics11101633","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T13:56:12Z","timestamp":1653054972000},"page":"1633","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Measurement, Evaluation, and Control of Active Intelligent Gait Training Systems\u2014Analysis of the Current State of the Art"],"prefix":"10.3390","volume":"11","author":[{"given":"Yi","family":"Han","sequence":"first","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"},{"name":"Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology 185 Miyanokuchi, Tosayamada-cho, Kami-city 782-8502, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3325-0076","authenticated-orcid":false,"given":"Chenhao","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Bin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Ning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Rehabilitation Technical Aids Technology and System of the Ministry of Civil Affairs, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China"}]},{"given":"Shuoyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechanical Systems Engineering, Kochi University of Technology 185 Miyanokuchi, Tosayamada-cho, Kami-city 782-8502, Japan"}]},{"given":"Meimei","family":"Han","sequence":"additional","affiliation":[{"name":"Zhejiang Fuzhi Science and Technology Innovation Co., Ltd., Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0143-9421","authenticated-orcid":false,"given":"Jo\u00e3o P.","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Institute of Superior of Engineering of Coimbra, Quinta da Nora, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2797-0264","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Xiufeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Rehabilitation Technical Aids Technology and System of the Ministry of Civil Affairs, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,20]]},"reference":[{"key":"ref_1","first-page":"765","article-title":"A summary of 30 years\u2019 research on risk factors of stroke mortality in China","volume":"26","author":"Li","year":"2017","journal-title":"Chin. J. Behav. Med. Brain Sci."},{"key":"ref_2","first-page":"2","article-title":"China Stroke Prevention still faces Great Challenges: China Stroke Prevention Report 2018 Summary","volume":"34","author":"Wang","year":"2019","journal-title":"China Circ. J."},{"key":"ref_3","first-page":"635","article-title":"Gait Disturbances in Patients With Stroke","volume":"6","author":"Balaban","year":"2014","journal-title":"Pmr"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Ye, J., Chen, G., Liu, Q., Duan, L., and Wang, C. (2018, January 1\u20135). Gait Phase Estimation for FES Based on Pelvic Movement of a Novel Gait Rehabilitation Robot. Proceedings of the 2018 IEEE International Conference on Real-time Computing and Robotics (RCAR), Kudahuvadhoo, Maldives.","DOI":"10.1109\/RCAR.2018.8621668"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"419","DOI":"10.7736\/KSPE.2019.36.4.419","article-title":"An Assistive Control Strategy Using Arm Swing Information for 1DoF Hip Exoskeleton for Hemiplegic Gait Rehabilitation","volume":"36","author":"Seo","year":"2019","journal-title":"J. Korean Soc. Precis. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qin, T., Meng, X., Qiu, J., Zhu, D., and Zhang, J. (2019). Dynamics Analysis of the Human-Machine System of the Assistive Gait Training Robot. Intelligent Robotics and Applications, ICIRA, Springer.","DOI":"10.1007\/978-3-030-27529-7_24"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1097\/MRR.0000000000000409","article-title":"The utilization of an overground robotic exoskeleton for gait training during inpatient rehabilitation-single-center retrospective findings","volume":"43","author":"Swank","year":"2020","journal-title":"Int. J. Rehabil. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.apmr.2019.10.192","article-title":"Exoskeleton-assisted Gait Training in Persons With Multiple Sclerosis: A Single-Group Pilot Study","volume":"101","author":"Afzal","year":"2020","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hoyer, E., and Opheim, A. (2020). Implementing the exoskeleton Ekso GTTM for gait rehabilitation in a stroke unit\u2013feasibility, functional benefits and patient experiences. Disabil. Rehabil. Assist. Technol.","DOI":"10.1080\/17483107.2020.1800110"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, F.C., Li, Y.C., Wu, K.L., Chen, P.Y., and Fu, L.C. (2020). Online gait detection with an automatic mobile trainer inspired by neuro-developmental treatment. Sensors, 20.","DOI":"10.3390\/s20123389"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ma, W., Huang, R., Chen, Q., Song, G., and Li, C. (2020, January 27\u201330). Dynamic Movement Primitives based Parametric Gait Model for Lower Limb Exoskeleton. Proceedings of the 2020 39th Chinese Control Conference (CCC), Shenyang, China.","DOI":"10.23919\/CCC50068.2020.9188594"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2750936","DOI":"10.1155\/2021\/2750936","article-title":"Design and Simulation Analysis of a Robot-Assisted Gait Trainer with the PBWS System","volume":"2021","author":"Ji","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_13","first-page":"2000","article-title":"Rehabilitation-assisted robot and their physical human-computer interaction methods","volume":"44","author":"Liang","year":"2018","journal-title":"J. Autom."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jensen, R.R., Paulsen, R.R., and Larsen, R. (2009, January 9). Analysis of gait using a treadmill and a time-of-flight camera. Proceedings of the Workshop on Dynamic 3D Vision, Jena, Germany.","DOI":"10.1007\/978-3-642-03778-8_12"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.gaitpost.2015.10.007","article-title":"Agreement of spatio-temporal gait parameters between a vertical ground reaction force decomposition algorithm and a motion capture system","volume":"43","author":"Veilleux","year":"2016","journal-title":"Gait Posture"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1007\/s40430-021-03016-2","article-title":"Research on a gait detection system and recognition algorithm for lower limb exoskeleton robot","volume":"43","author":"Zeng","year":"2021","journal-title":"J. Braz. Soc. Mech. Sci. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mazhar, O., Bari, A.Z., and Faudzi, A. (2016, January 27\u201329). Real-time gait phase detection using wearable sensors. Proceedings of the Control Conference, Chengdu, China.","DOI":"10.1109\/ASCC.2015.7244853"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/TASE.2018.2884723","article-title":"Inertial Sensor-Based Slip Detection in Human Walking","volume":"16","author":"Trkov","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, G., Song, J., Wang, X., Lan, F., and Zou, F. (August, January 29). Research on Lower Limb Exoskeleton Based on Multi-Sensor Information Mature Technology. Proceedings of the 2019 IEEE 9th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Suzhou, China.","DOI":"10.1109\/CYBER46603.2019.9066613"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1108\/AA-11-2018-0221","article-title":"Wirerope-driven exoskeleton to assist lower-limb rehabilitation of hemiplegic patients by using motion capture","volume":"40","author":"Xie","year":"2020","journal-title":"Assem. Autom."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bao, W., Villarreal, D., and Chiao, J. (2020, January 26\u201328). Vision-Based Autonomous Walking in a Lower-Limb Powered Exoskeleton. Proceedings of the 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), Cincinnati, OH, USA.","DOI":"10.1109\/BIBE50027.2020.00141"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Steinert, A., Sattler, I., Otte, K., R\u00f6hling, H., Mansow-Model, S., and M\u00fcller-Werdan, U. (2020). Using New Camera-Based Technologies for Gait Analysis in Older Adults in Comparison to the Established GAITRite System. Sensors, 20.","DOI":"10.3390\/s20010125"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tran, T.-H., Nguyen, D.T., and Phuong Nguyen, T. (2021, January 13\u201315). Human Posture Classification from Multiple Viewpoints and Application for Fall Detection. Proceedings of the 2020 IEEE Eighth International Conference on Communications and Electronics (ICCE), Phu Quoc Island, Vietnam.","DOI":"10.1109\/ICCE48956.2021.9352140"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Toshev, A., and Szegedy, C. (2014, January 23\u201328). DeepPose: Human Pose Estimation via Deep Neural Networks. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.214"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lee, B., Kim, J., and Jung, S.-U. (2020, January 21\u201323). Light-weighted Network based Human Pose Estimation for Mobile AR Service. Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Korea.","DOI":"10.1109\/ICTC49870.2020.9289085"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schicketmueller, A., Rose, G., and Hofmann, M. (2019). Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training. Sensors, 19.","DOI":"10.3390\/s19214804"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7157","DOI":"10.1038\/s41598-019-43628-2","article-title":"Gait training using a robotic hip exoskeleton improves metabolic gait efficiency in the elderly","volume":"9","author":"Martini","year":"2019","journal-title":"Sci Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/978-3-030-01845-0_88","article-title":"A Novel Gait Assistance System Based on an Active Knee Orthosis and a Haptic Cane for Overground Walking","volume":"Volume 21","author":"Lee","year":"2019","journal-title":"Converging Clinical and Engineering Research on Neurorehabilitation III"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Schicketmueller, A., Lamprecht, J., Hofmann, M., Sailer, M., and Rose, G. (2020). Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training. Sensors, 20.","DOI":"10.3390\/s20123399"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1631\/FITEE.1900455","article-title":"An untethered cable-driven ankle exoskeleton with plantarflexion-dorsiflexion bidirectional movement assistance","volume":"21","author":"Wang","year":"2020","journal-title":"Front. Inform. Technol. Electron. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1007\/s12555-019-0260-9","article-title":"A Robotic Gait Training System with Stair-climbing Mode Based on a Unique Exoskeleton Structure with Active Foot Plates","volume":"18","author":"Bae","year":"2020","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_32","unstructured":"Livolsi, C., Conti, R., Giovacchini, F., Vitiello, N., and Crea, S. (2021). A Novel Wavelet-Based Gait Segmentation Method for a Portable hip Exoskeleton. IEEE Trans. Robot., 1\u201315."},{"key":"ref_33","first-page":"166","article-title":"Design Of A Control System For A Lower-Limb Exoskeleton Rehabilitation Robot With Gait Phase Detection Algorithm Using Inertial Sensor","volume":"33","author":"Bae","year":"2021","journal-title":"Assist. Technol."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chen, S., Bangaru, S.S., Yigit, T., Trkov, M., Wang, C., and Yi, J. (2021, January 12\u201316). Real-Time Walking Gait Estimation for Construction Workers using a Single Wearable Inertial Measurement Unit (IMU). Proceedings of the 2021 IEEE\/ASME International Conference on Advanced Intelligent Mechatronics (AIM), Delft, The Netherlands.","DOI":"10.1109\/AIM46487.2021.9517592"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/TR.2020.3030952","article-title":"RFID-Pose: Vision-Aided Three-Dimensional Human Pose Estimation With Radio-Frequency Identification","volume":"70","author":"Yang","year":"2021","journal-title":"IEEE Trans. Reliab."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhao, M., Liu, Y., Raghu, A., Zhao, H., and Katabi, D. (November, January 27). Through-Wall Human Mesh Recovery Using Radio Signals. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.01021"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.mechatronics.2015.04.005","article-title":"Recent development of mechanisms and control strategies for robot-assisted lower limb rehabilitation","volume":"31","author":"Meng","year":"2015","journal-title":"Mechatronics"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1109\/TMECH.2019.2932312","article-title":"Design and Control of a Series Elastic Actuator With Clutch for Hip Exoskeleton for Precise Assistive Magnitude and Timing Control and Improved Mechanical Safety","volume":"24","author":"Zhang","year":"2019","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104302","DOI":"10.1063\/1.5006461","article-title":"A brain-controlled lower-limb exoskeleton for human gait training","volume":"88","author":"Liu","year":"2017","journal-title":"Rev. Sci. Instrum."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neuroimage.2014.12.040","article-title":"Automated model selection in covariance estimation and spatial whitening of MEG and EEG signals","volume":"108","author":"Engemann","year":"2015","journal-title":"Neuroimage"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.1109\/TNSRE.2021.3115490","article-title":"Hierarchical Decoding Model of Upper Limb Movement Intention From EEG Signals Based on Attention State Estimation","volume":"29","author":"Bi","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/TII.2018.2875729","article-title":"Admittance Control Based on EMG-Driven Musculoskeletal Model Improves the Human\u2013Robot Synchronization","volume":"15","author":"Zhuang","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhang, L., Ma, Y., Wang, C., Yan, Z., and Wu, X. (2019, January 3\u20135). A Method for Arm Motions Classification and A Lower-limb Exoskeleton Control Based on sEMG signals. Proceedings of the 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM), Toyonaka, Japan.","DOI":"10.1109\/ICARM.2019.8833708"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Xie, H., Li, G., Zhao, X., and Li, F. (2020). Prediction of Limb Joint Angles Based on Multi-Source Signals by GS-GRNN for Exoskeleton Wearer. Sensors, 20.","DOI":"10.3390\/s20041104"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rabe, K.G., and Fey, N.P. (2022). Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression. Front. Robot. AI.","DOI":"10.3389\/frobt.2022.716545"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TNSRE.2011.2163529","article-title":"Resolving the Limb Position Effect in Myoelectric Pattern Recognition","volume":"19","author":"Fougner","year":"2011","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_47","first-page":"252","article-title":"Human-robot interactive information sensing system for gait rehabilitation training robot","volume":"26","author":"Guo","year":"2019","journal-title":"Chin. J. Eng. Des."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2050004","DOI":"10.1142\/S0219843620500048","article-title":"BPNN-Based Real-Time Recognition of Locomotion Modes for an Active Pelvis Orthosis with Different Assistive Strategies","volume":"17","author":"Gong","year":"2020","journal-title":"Int. J. Hum. Robot."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhu, L., Wang, Z., Ning, Z., Zhang, Y., Liu, Y., Cao, W., Wu, X., and Chen, C. (2020). A Novel Motion Intention Recognition Approach for Soft Exoskeleton via IMU. Electronics, 9.","DOI":"10.3390\/electronics9122176"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Pinheiro, C., Figueiredo, J., Magalh\u00e3es, N., and Santos, C.P. (2020). Wearable Biofeedback Improves Human-Robot Compliance during Ankle-Foot Exoskeleton-Assisted Gait Training: A Pre-Post Controlled Study in Healthy Participants. Sensors, 20.","DOI":"10.3390\/s20205876"},{"key":"ref_51","first-page":"1859","article-title":"Flexible and Safe Robot Movement Control Research","volume":"42","author":"Xu","year":"2016","journal-title":"J. Autom."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.1109\/TLA.2020.9381791","article-title":"Robotic Knee Exoskeleton Prototype to Assist Patients in Gait Rehabilitation","volume":"18","author":"Minchala","year":"2020","journal-title":"IEEE Lat. Am. Trans."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1109\/TSMC.2017.2771227","article-title":"Development of a Minimal-Intervention-Based Admittance Control Strategy for Upper Extremity Rehabilitation Exoskeleton","volume":"48","author":"Wu","year":"2018","journal-title":"IEEE Trans. Syst. Man Cybern Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1093\/ptj\/63.10.1606","article-title":"Reliability of the Fugl-Meyer assessment of sensorimotor recovery following cerebrovascular accident","volume":"10","author":"Duncan","year":"1983","journal-title":"Phys. Therapy"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1109\/TBME.2004.827933","article-title":"Gait assessment in Parkinson\u2019s disease: Toward an ambulatory system for long-term monitoring","volume":"51","author":"Salarian","year":"2004","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Anaya-Reyes, F., Cheng, H., Thangavel, P., and Yu, H. (2018, January 8\u201320). The Shared Effects of Active Body Weight Support and Robot-Applied Resistance\/Assistance on Temporal Gait Parameters and Gait Related Muscle Activity. Proceedings of the 2018 3rd International Conference on Advanced Robotics and Mechatronics (ICARM), Singapore.","DOI":"10.1109\/ICARM.2018.8610877"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.nmd.2019.10.007","article-title":"Evaluation of gait in Duchenne Muscular Dystrophy: Relation of 3D gait analysis to clinical assessment","volume":"29","author":"Alberto","year":"2019","journal-title":"Neuromuscul. Disord."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ma, Y., Wu, X., Wang, C., Yi, Z., and Liang, G. (2019). Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method. Sensors, 19.","DOI":"10.3390\/s19245449"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chomiak, T., Sidhu, A.S., Watts, A., Su, L., Graham, B., Wu, J., Classen, S., Falter, B., and Hu, B. (2019). Development and Validation of Ambulosono: A Wearable Sensor for Bio-Feedback Rehabilitation Training. Sensors, 19.","DOI":"10.3390\/s19030686"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Tran, V.-T., Sasaki, K., and Yamamoto, S.-I. (2020). Influence of Body Weight Support Systems on the Abnormal Gait Kinematic. Appl. Sci., 10.","DOI":"10.3390\/app10134685"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"22823","DOI":"10.1038\/s41598-021-01959-z","article-title":"Abnormal synergistic gait mitigation in acute stroke using an innovative ankle\u2013knee\u2013hip interlimb humanoid robot: A preliminary randomized controlled trial","volume":"11","author":"Park","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sconza, C., Negrini, F., Di Matteo, B., Borboni, A., Boccia, G., Petrikonis, I., Stankevi\u010dius, E., and Casale, R. (2021). Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial. Medicina, 57.","DOI":"10.3390\/medicina57070713"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Choi, M., Ahn, N., Park, J., and Kim, K. (2021). 12-Week Exercise Training of Knee Joint and Squat Movement Improves Gait Ability in Older Women. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18041515"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.apmr.2018.06.020","article-title":"Effects of Electromechanical Exoskeleton-Assisted Gait Training on Walking Ability of Stroke Patients: A Randomized Controlled Trial","volume":"100","author":"Jin","year":"2019","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"42","DOI":"10.3389\/fnbot.2020.00042","article-title":"Persistent Effect of Gait Exercise Assist Robot Training on Gait Ability and Lower Limb Function of Patients With Subacute Stroke: A Matched Case\u2013Control Study With Three-Dimensional Gait Analysis","volume":"14","author":"Wang","year":"2020","journal-title":"Front. Neurorobot."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1109\/TNSRE.2020.2984790","article-title":"Neuromuscular Controller Embedded in a Powered Ankle Exoskeleton: Effects on Gait, Clinical Features and Subjective Perspective of Incomplete Spinal Cord Injured Subjects","volume":"28","author":"Tamburella","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1109\/JBHI.2015.2450232","article-title":"Classification of Parkinson\u2019s Disease Gait Using Spatial-Temporal Gait Features","volume":"19","author":"Wahid","year":"2015","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"17269","DOI":"10.1038\/s41598-019-53656-7","article-title":"Selecting Clinically Relevant Gait Characteristics for Classification of Early Parkinson\u2019s Disease: A Comprehensive Machine Learning Approach","volume":"9","author":"Rehman","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.parkreldis.2015.01.016","article-title":"Gait velocity and step length at baseline predict outcome of Nordic walking training in patients with Parkinson\u2019s disease","volume":"21","author":"Herfurth","year":"2015","journal-title":"Parkinsonism Relat. Disord."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1765","DOI":"10.1109\/JBHI.2018.2865218","article-title":"IMU-Based Classification of Parkinson\u2019s Disease From Gait: A Sensitivity Analysis on Sensor Location and Feature Selection","volume":"22","author":"Carlotta","year":"2018","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_71","first-page":"1976","article-title":"Two Shank-Mounted IMUs-Based Gait Analysis and Classification for Neurological Disease Patients","volume":"5","author":"Wang","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Skvortsov, D.V., Kaurkin, S.N., and Ivanova, G.E. (2021). A Study of Biofeedback Gait Training in Cerebral Stroke Patients in the Early Recovery Phase with Stance Phase as Target Parameter. Sensors, 21.","DOI":"10.3390\/s21217217"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Saito, H., Yokoyama, H., Sasaki, A., Kato, T., and Nakazawa, K. (2021). Flexible Recruitments of Fundamental Muscle Synergies in the Trunk and Lower Limbs for Highly Variable Movements and Postures. Sensors, 21.","DOI":"10.1101\/2021.08.03.455001"},{"key":"ref_74","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":"Turolla","year":"2012","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1310\/sci1701-16","article-title":"Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement","volume":"17","author":"Safavynia","year":"2011","journal-title":"Top. Spinal Cord Inj. Rehabil."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1109\/TNSRE.2020.3017128","article-title":"Adapting to the Mechanical Properties and Active Force of an Exoskeleton by Altering Muscle Synergies in Chronic Stroke Survivors","volume":"28","author":"Rinaldi","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"733738","DOI":"10.3389\/fnbot.2021.733738","article-title":"Robotic Exoskeleton Gait Training in Stroke: An Electromyography-Based Evaluation","volume":"15","author":"Longatelli","year":"2021","journal-title":"Front. Neurorobot."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Seo, J.-W., and Kim, H.-S. (2021). Biomechanical Analysis in Five Bar Linkage Prototype Machine of Gait Training and Rehabilitation by IMU Sensor and Electromyography. Sensors, 21.","DOI":"10.3390\/s21051726"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jbiomech.2019.04.028","article-title":"An actuated dissipative spring-mass walking model: Predicting human-like ground reaction forces and the effects of model parameters","volume":"90","author":"Li","year":"2019","journal-title":"J. Biomech."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Smyrli, A., and Papadopoulos, E. (August, January 31). A methodology for the incorporation of arbitrarily-shaped feet in passive bipedal walking dynamics. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196617"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1109\/JSYST.2014.2318698","article-title":"Multimodal Human\u2013Robot Interaction for Walker-Assisted Gait","volume":"10","author":"Cifuentes","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"113788","DOI":"10.1109\/ACCESS.2021.3104464","article-title":"Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems","volume":"9","author":"Kolaghassi","year":"2021","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1838","DOI":"10.1109\/TNSRE.2021.3109729","article-title":"A Soft Robotic Intervention for Gait Enhancement in Older Adults","volume":"29","author":"Hu","year":"2021","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1023\/A:1024488717009","article-title":"An Adaptive Shared Control System for an Intelligent Mobility Aid for the Elderly","volume":"15","author":"Yu","year":"2003","journal-title":"Auton. Robot."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"025701","DOI":"10.1088\/0957-0233\/27\/2\/025701","article-title":"A wearable sensor system for lower-limb rehabilitation evaluation using the GRF and CoP distributions","volume":"27","author":"Tao","year":"2015","journal-title":"Meas. Sci. Technol."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ye, J., Chen, G., and Liu, Q. (2018, January 24\u201327). An Adaptive Shared Control of a Novel Robotic Walker for Gait Rehabilitation of Stroke Patients. Proceedings of the 2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR), Shenyang, China.","DOI":"10.1109\/IISR.2018.8535892"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Miyake, T., Kobayashi, Y., Fujie, M.G., and Sugano, S. (2018, January 12\u201315). Intermittent Force Application of Wire-Driven Gait Training Robot to Encourage User to Learn an Induced Gait. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8664811"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"76741","DOI":"10.1109\/ACCESS.2019.2922258","article-title":"Walking Assist Robot: A Novel Non-Contact Abnormal Gait Recognition Approach Based on Extended Set Membership Filter","volume":"7","author":"Zhao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Mu, Z., Fang, J., and Zhang, Q. (2019, January 24\u201328). Admittance Control of the Ankle Mechanism in a Rotational Orthosis for Walking with Arm Swing. Proceedings of the 2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR), Toronto, ON, Canada.","DOI":"10.1109\/ICORR.2019.8779408"},{"key":"ref_90","first-page":"1729881419839584","article-title":"Human-robot interactive control based on reinforcement learning for gait rehabilitation training robot","volume":"16","author":"Guo","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1007\/s12541-019-00185-y","article-title":"Gait Training Algorithm of an End-Effector Typed Hybrid Walking Rehabilitation Robot","volume":"20","author":"Kim","year":"2019","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/fnbot.2020.00017","article-title":"Development of an Improved Rotational Orthosis for Walking With Arm Swing and Active Ankle Control","volume":"14","author":"Mu","year":"2020","journal-title":"Front. Neurorobot."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"103326","DOI":"10.1016\/j.robot.2019.103326","article-title":"Admittance control based robotic clinical gait training with physiological cost evaluation","volume":"123","author":"Shunki","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, S., Nolan, K.J., and Zanotto, D. (December, January 29). Reinforcement Learning Assist-as-needed Control for Robot Assisted Gait Training. Proceedings of the 2020 8th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), New York, NY, USA.","DOI":"10.1109\/BioRob49111.2020.9224392"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"103642","DOI":"10.1016\/j.robot.2020.103642","article-title":"Fuzzy logic compliance adaptation for an assist-as-needed controller on the Gait Rehabilitation Exoskeleton (GAREX)","volume":"133","author":"Zhong","year":"2020","journal-title":"Robot. Auton. Syst."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TNSRE.2020.3009317","article-title":"Gait Adaptation Using a Cable-Driven Active Leg Exoskeleton (C-ALEX) With Post-Stroke Participants","volume":"28","author":"Hidayah","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Lopes, J., Pinheiro, C., Figueiredo, J., Reis, L.P., and Santos, C.P. (2020, January 15\u201317). Assist-as-needed Impedance Control Strategy for a Wearable Ankle Robotic Orthosis. Proceedings of the 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Ponta Delgada, Portugal.","DOI":"10.1109\/ICARSC49921.2020.9096186"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yin, Y., Chen, Z., Zhang, Y., Rao, A.K., Guo, Y., and Zanotto, D. (2020). Wearable Biofeedback System to Induce Desired Walking Speed in Overground Gait Training. Sensors, 20.","DOI":"10.3390\/s20144002"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Scheidig, A., Sch\u00fctz, B., Trinh, T.Q., Vorndran, A., Mayfarth, A., Sternitzke, C., R\u00f6hner, E., and Gross, H.-M. (2021). Robot-Assisted Gait Self-Training: Assessing the Level Achieved. Sensors, 21.","DOI":"10.3390\/s21186213"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/TASE.2020.2964807","article-title":"Online Gait Planning of Lower-Limb Exoskeleton Robot for Paraplegic Rehabilitation Considering Weight Transfer Process","volume":"18","author":"Ma","year":"2021","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1186\/s10033-021-00537-8","article-title":"Control and Implementation of 2-DOF Lower Limb Exoskeleton Experiment Platform","volume":"34","author":"Chen","year":"2021","journal-title":"Chin. J. Mech. Eng."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10846-021-01487-y","article-title":"Human-in-the-Loop Control for AGoRA Unilateral Lower-Limb Exoskeleton","volume":"104","author":"Mayag","year":"2022","journal-title":"J. Intell Robot Syst."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"20220001","DOI":"10.2490\/prm.20220001","article-title":"Development of a Gait Rehabilitation Robot Using an Exoskeleton and Functional Electrical Stimulation: Validation in a Pseudo-paraplegic Model","volume":"7","author":"Inoue","year":"2022","journal-title":"Prog. Rehabil. Med."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jbmt.2021.09.019","article-title":"Pelvic floor muscle training and postural balance in elderly women: An exploratory single arm trial","volume":"29","author":"Gianluca","year":"2022","journal-title":"J. Bodyw. Mov. Ther."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.gaitpost.2021.11.025","article-title":"Investigating the underlying biomechanical mechanisms leading to falls in long-term ankle-foot orthosis and functional electrical stimulator users with chronic stroke","volume":"92","author":"Nevisipour","year":"2022","journal-title":"Gait Posture"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"517674","DOI":"10.1260\/2040-2295.1.2.197","article-title":"Locomotor Training in Subjects with Sensori-Motor Deficits: An Overview of the Robotic Gait Orthosis Lokomat","volume":"1","author":"Riener","year":"2010","journal-title":"J. Healthc. Eng."},{"key":"ref_107","unstructured":"Loredana, R., Roberto, P., Flavia, O., Alfredo, M., Francesco, C., and Rocco, S.C. (2020). A multidisciplinary advanced approach in central pontine myelinolysis recovery: Considerations about a case report. Disabil. Rehabil. Assist. Technol."},{"key":"ref_108","first-page":"15","article-title":"A Case Report on Robot-Aided Gait Training in Primary Lateral Sclerosis Rehabilitation: Rationale, Feasibility and Potential Effectiveness of a Novel Rehabilitation Approach","volume":"18","author":"Portaro","year":"2021","journal-title":"Innov Clin. Neurosci."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1007\/s10072-020-04875-8","article-title":"Breaking the ice to improve motor outcomes in patients with chronic stroke: A retrospective clinical study on neuromodulation plus robotics","volume":"42","author":"Naro","year":"2021","journal-title":"Neurol Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"13886","DOI":"10.1109\/JSEN.2021.3070682","article-title":"A Radar Sensor for Automatic Gait Speed Analysis in Walking Tests","volume":"21","author":"Alshamaa","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_111","first-page":"1","article-title":"Measurement of Stride Time by Machine Learning: Sensitivity Analysis for the Simplification of the Experimental Protocol","volume":"71","author":"Cucchiarelli","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Electronics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/10\/1633\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:15:20Z","timestamp":1760138120000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-9292\/11\/10\/1633"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,20]]},"references-count":111,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["electronics11101633"],"URL":"https:\/\/doi.org\/10.3390\/electronics11101633","relation":{},"ISSN":["2079-9292"],"issn-type":[{"type":"electronic","value":"2079-9292"}],"subject":[],"published":{"date-parts":[[2022,5,20]]}}}