{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T16:19:25Z","timestamp":1783700365755,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T00:00:00Z","timestamp":1472774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Locomotion mode identification is essential for the control of a robotic rehabilitation exoskeletons. This paper proposes an online support vector machine (SVM) optimized by particle swarm optimization (PSO) to identify different locomotion modes to realize a smooth and automatic locomotion transition. A PSO algorithm is used to obtain the optimal parameters of SVM for a better overall performance. Signals measured by the foot pressure sensors integrated in the insoles of wearable shoes and the MEMS-based attitude and heading reference systems (AHRS) attached on the shoes and shanks of leg segments are fused together as the input information of SVM. Based on the chosen window whose size is 200 ms (with sampling frequency of 40 Hz), a three-layer wavelet packet analysis (WPA) is used for feature extraction, after which, the kernel principal component analysis (kPCA) is utilized to reduce the dimension of the feature set to reduce computation cost of the SVM. Since the signals are from two types of different sensors, the normalization is conducted to scale the input into the interval of [0, 1]. Five-fold cross validation is adapted to train the classifier, which prevents the classifier over-fitting. Based on the SVM model obtained offline in MATLAB, an online SVM algorithm is constructed for locomotion mode identification. Experiments are performed for different locomotion modes and experimental results show the effectiveness of the proposed algorithm with an accuracy of 96.00% \u00b1 2.45%. To improve its accuracy, majority vote algorithm (MVA) is used for post-processing, with which the identification accuracy is better than 98.35% \u00b1 1.65%. The proposed algorithm can be extended and employed in the field of robotic rehabilitation and assistance.<\/jats:p>","DOI":"10.3390\/s16091408","type":"journal-article","created":{"date-parts":[[2016,9,2]],"date-time":"2016-09-02T10:01:56Z","timestamp":1472810516000},"page":"1408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["PSO-SVM-Based Online Locomotion Mode Identification for Rehabilitation Robotic Exoskeletons"],"prefix":"10.3390","volume":"16","author":[{"given":"Yi","family":"Long","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Jiang","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1236-8350","authenticated-orcid":false,"given":"Wei-Dong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang-Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guo-Qiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"He","sequence":"additional","affiliation":[{"name":"Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xi-Wang","family":"Mao","sequence":"additional","affiliation":[{"name":"Weapon Equipment Research Institute, China Ordnance Industries Group, Beijing 102202, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/TRO.2008.2008747","article-title":"Powered ankle-foot prosthesis improves walking metabolic economy","volume":"25","author":"Au","year":"2009","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1109\/TMECH.2009.2032688","article-title":"Preliminary evaluations of a self-contained anthropomorphic transfemoral prosthesis","volume":"14","author":"Sup","year":"2009","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1108\/01439910910980169","article-title":"Robotic transtibial prosthesis with biomechanical energy regeneration","volume":"36","author":"Hitt","year":"2009","journal-title":"Ind. Robot"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.medengphy.2015.03.001","article-title":"High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification","volume":"37","author":"Joshi","year":"2015","journal-title":"Med. Eng. Phys."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"99","DOI":"10.4218\/etrij.14.0113.0064","article-title":"Real-Time Locomotion Mode Recognition Employing Correlation Feature Analysis Using EMG Pattern","volume":"36","author":"Kim","year":"2014","journal-title":"ETRI J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.neunet.2008.03.006","article-title":"Powered ankle-foot prosthesis to assist level-ground and stair-descent gaits","volume":"21","author":"Au","year":"2008","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1109\/TBME.2008.2003293","article-title":"A strategy for identifying locomotion modes using surface electromyography","volume":"56","author":"Huang","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2867","DOI":"10.1109\/TBME.2011.2161671","article-title":"Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion","volume":"58","author":"Huang","year":"2011","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"056021","DOI":"10.1088\/1741-2560\/11\/5\/056021","article-title":"Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses","volume":"11","author":"Young","year":"2014","journal-title":"J. Neural Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1275","DOI":"10.1007\/s10439-015-1407-3","article-title":"Terrain and direction classification of locomotion transitions using neuromuscular and mechanical input","volume":"44","author":"Joshi","year":"2016","journal-title":"Ann. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1109\/TBME.2014.2334316","article-title":"A noncontact capacitive sensing system for recognizing locomotion modes of transtibial amputees","volume":"61","author":"Enhao","year":"2014","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/978-3-642-33932-5_26","article-title":"A wearable plantar pressure measurement system: Design specifications and first experiments with an amputee","volume":"194","author":"Wang","year":"2013","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yuan, K., Sun, S., and Wang, Z. (2013, January 6\u201310). A fuzzy logic based terrain identification approach to prosthesis control using multi-sensor fusion. Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631048"},{"key":"ref_14","unstructured":"David, L.Y., and Hsiao-Wecksler, E.T. (2013, January 24\u201326). Gait mode recognition and control for a portable-powered ankle-foot orthosis. Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Seattle, WA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s10439-013-0909-0","article-title":"Intent recognition in a powered lower limb prosthesis using time history information","volume":"42","author":"Young","year":"2014","journal-title":"Ann. Biomed. Eng."},{"key":"ref_16","unstructured":"Zhang, F., Fang, Z., and Liu, M. (September, January 30). Preliminary design of a terrain recognition system. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1109\/TBME.2009.2034734","article-title":"Multiclass real-time intent recognition of a powered lower limb prosthesis","volume":"57","author":"Varol","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12349","DOI":"10.3390\/s140712349","article-title":"A locomotion intent prediction system based on multi-sensor fusion","volume":"14","author":"Chen","year":"2014","journal-title":"Sensors"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1109\/TMECH.2014.2309708","article-title":"Fuzzy-logic-based terrain identification with multisensor fusion for transtibial amputees","volume":"20","author":"Yuan","year":"2015","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_20","first-page":"110","article-title":"Support vector machines: Theory and applications","volume":"302","author":"Wang","year":"2005","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, T., Ye, X., and Wang, L. (2012, January 23\u201326). Grid Search Optimized SVM Method for Dish-like Underwater Robot Attitude Prediction. Proceedings of the 2012 Fifth International Joint Conference on IEEE Computational Sciences and Optimization (CSO), Harbin, China.","DOI":"10.1109\/CSO.2012.189"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"175","DOI":"10.14257\/ijsh.2015.9.5.17","article-title":"A method for missing data recovery of waste gas monitoring in animal building based on GA-SVM","volume":"9","author":"Liu","year":"2015","journal-title":"Int. J. Smart Home"},{"key":"ref_23","unstructured":"Lu, M.Z., Chen, C.P., and Huo, J.B. (2009, January 12\u201315). Optimization of combined kernel function for SVM by Particle Swarm Optimization. Proceedings of the 2009 International Conference on IEEE Machine Learning and Cybernetics, Baoding, China."},{"key":"ref_24","unstructured":"Olsson, A.E. (2011). Particle Swarm Optimization: Theory, Techniques, and Applications, Nova Science Publishers, Inc.. Engineering Tools Techniques & Tables."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/530251","article-title":"Applications of PCA and SVM-PSO based real-time face recognition system","volume":"2014","author":"Shieh","year":"2014","journal-title":"Math. Probl. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Long, Y., Du, Z., and Wang, W. (July, January 29). A fuzzy logic system tuned with particle swarm optimization for gait segmentation using insole measured ground reaction force. Proceedings of the 2014 IEEE 11th World Congress on Intelligent Control and Automation (WCICA), Shenyang, China.","DOI":"10.1109\/WCICA.2014.7052766"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.robot.2014.08.012","article-title":"A survey of sensor fusion methods in wearable robotics","volume":"73","author":"Novak","year":"2015","journal-title":"Robot. Auton. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Long, Y., Du, Z., and Wang, W. (2014, January 5\u201310). Three gait patterns recognition with ground reaction forceusing support vector machine. Proceedings of the 2014 IEEE International Conference on Robotics and Biomimetics, Bali, Indonesia.","DOI":"10.1109\/ROBIO.2014.7090534"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ipl.2004.01.020","article-title":"Search audio data with the wavelet pyramidal algorithm","volume":"91","author":"Li","year":"2004","journal-title":"Inf. Process. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal dcomposition: The wavelet representation","volume":"11","author":"Mallat","year":"1989","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.aeue.2011.11.010","article-title":"Study on feature extraction method in border monitoring system using optimum wavelet packet decomposition","volume":"66","author":"Wang","year":"2012","journal-title":"AEU Int. J. Electron. Commun."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/LGRS.2012.2218569","article-title":"Wavelet Packet Analysis and Gray Model for Feature Extraction of Hyper spectral Data","volume":"10","author":"Yin","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.compfluid.2014.09.034","article-title":"Experimental study of hydraulic cylinder leakage and fault feature extraction based on wavelet packet analysis","volume":"106","author":"Zhao","year":"2015","journal-title":"Comput. Fluids."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Landgrebe, D.A. (2003). Signal Theory Methods in Multispectral Remote Sensing, John Wiley & Sons.","DOI":"10.1002\/0471723800"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","article-title":"On the mean accuracy of statistical pattern recognizers","volume":"14","author":"Hughes","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sarwinda, D., and Arymurthy, A.M. (2013, January 28\u201329). Feature selection using kernel PCA for Alzheimer\u2019s disease detection with 3D MR Images of brain. Proceedings of the 2013 International Conference on IEEE Advanced Computer Science and Information Systems (ICACSIS), Bali, Indonesia.","DOI":"10.1109\/ICACSIS.2013.6761597"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.neucom.2014.09.102","article-title":"A cloud image detection method based on SVM vector machine","volume":"169","author":"Li","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rencher, A.C. (2012). Methods of Multivariate Analysis, John Wiley & Sons.","DOI":"10.1002\/9781118391686"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1023\/A:1009715923555","article-title":"A tutorial on support vector machines for pattern recognition","volume":"2","author":"Burges","year":"1998","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nocedal, J., and Wright, S. (1999). Numerical Optimization, Springer Science & Business Media.","DOI":"10.1007\/b98874"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Steidl, G. (2015). Supervised learning by support vector machines. Handbook of Mathematical Methods in Imaging, Springer Science & Business Media.","DOI":"10.1007\/978-1-4939-0790-8_22"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:29:55Z","timestamp":1760210995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/16\/9\/1408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,9,2]]},"references-count":41,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2016,9]]}},"alternative-id":["s16091408"],"URL":"https:\/\/doi.org\/10.3390\/s16091408","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,9,2]]}}}