{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T19:11:02Z","timestamp":1778094662145,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T00:00:00Z","timestamp":1610496000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFC2001300"],"award-info":[{"award-number":["2018YFC2001300"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91848204"],"award-info":[{"award-number":["91848204"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["91948302"],"award-info":[{"award-number":["91948302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52075216"],"award-info":[{"award-number":["52075216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man\u2013machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent\/descent, stair ascent\/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.<\/jats:p>","DOI":"10.3390\/s21020526","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T21:50:54Z","timestamp":1610574654000},"page":"526","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3870-6730","authenticated-orcid":false,"given":"Yang","family":"Han","sequence":"first","affiliation":[{"name":"The School of Mechanical Science and Aerospace Engineering, Jilin University, Changchun 130000, China"},{"name":"Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130000, China"}]},{"given":"Chunbao","family":"Liu","sequence":"additional","affiliation":[{"name":"The School of Mechanical Science and Aerospace Engineering, Jilin University, Changchun 130000, China"},{"name":"Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130000, China"}]},{"given":"Lingyun","family":"Yan","sequence":"additional","affiliation":[{"name":"The School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK"}]},{"given":"Lei","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130000, China"},{"name":"The School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,13]]},"reference":[{"key":"ref_1","first-page":"661","article-title":"Human Motion Intent Recognition Based on Kernel Principal Component Analysis and Relevance Vector Machine","volume":"39","author":"Liu","year":"2017","journal-title":"Robot"},{"key":"ref_2","first-page":"512","article-title":"sEMG Pattern Recognition Based on Multi Feature Fusion of Wavelet Transform","volume":"29","author":"Yu","year":"2016","journal-title":"Chin. J. Sens. Actuators"},{"key":"ref_3","first-page":"433","article-title":"Research on Classification Algorithm of Reduced Support Vector Machine for Low Limb Movement Recognition","volume":"4","author":"Wu","year":"2011","journal-title":"China Mech. Eng."},{"key":"ref_4","first-page":"810","article-title":"Multi-channel sEMG Time Series Analysis Based Human Motion Recognition Method","volume":"40","author":"Tong","year":"2014","journal-title":"Acta Autom. Sin."},{"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":"671","DOI":"10.1109\/TNSRE.2013.2285101","article-title":"A training method for locomotion mode prediction using powered lower limb prostheses","volume":"22","author":"Young","year":"2014","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1109\/JAS.2017.7510619","article-title":"Intent pattern recognition of lower-limb motion based on mechanical sensors","volume":"4","author":"Liu","year":"2017","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.1109\/TNSRE.2019.2909585","article-title":"A CNN-based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis","volume":"27","author":"Su","year":"2019","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.3390\/s17092020","article-title":"An adaptive classification strategy for reliable locomotion mode recognition","volume":"17","author":"Liu","year":"2017","journal-title":"Sensors"},{"key":"ref_11","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_12","first-page":"310","article-title":"Lower limb locomotion modes recognition based on multiple-source information and general regression neural network","volume":"37","author":"Liu","year":"2015","journal-title":"Robot"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"434","DOI":"10.1109\/TNSRE.2015.2420539","article-title":"Development of an environment-aware locomotion mode recognition system for powered lower limb prostheses","volume":"24","author":"Liu","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2716","DOI":"10.1109\/TBME.2012.2208641","article-title":"Toward design of an environment-aware adaptive locomotion-mode-recognition system","volume":"59","author":"Du","year":"2012","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","first-page":"99","article-title":"Toward Minimal-Sensing Locomotion Mode Recognition for a Powered Knee-Ankle Prosthesis","volume":"1","author":"Khademi","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/978-3-319-46669-9_168","article-title":"Probabilistic Locomotion Mode Recognition withWearable Sensors","volume":"Volume 15","author":"Mahmood","year":"2017","journal-title":"Converging Clinical and Engineering Research on Neurorehabilitation II"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1080\/01691864.2018.1563500","article-title":"A strain gauge based locomotion mode recognition method using convolutional neural network","volume":"33","author":"Feng","year":"2019","journal-title":"Adv. Robot."},{"key":"ref_18","first-page":"337","article-title":"Gait Pre-recognition of active Lower Limb Prosthesis Based on Hidden Markov Model","volume":"36","author":"Zhao","year":"2014","journal-title":"Robot"},{"key":"ref_19","first-page":"1517","article-title":"An Improved Motion Intent Recognition Method for Intelligent Lower Limb Prosthesis Driven by Inertial Motion Capture Data","volume":"46","author":"Su","year":"2020","journal-title":"Acta Autom. Sin."},{"key":"ref_20","first-page":"96","article-title":"Motion intent Recognition Of Intelligent Lower Limb Prosthetics Based On LSTM Deep Learning Model","volume":"36","author":"Liu","year":"2019","journal-title":"J. Hefei Univ. (Compr. Ed.)"},{"key":"ref_21","first-page":"169","article-title":"Motion intent recognition of intelligent lower limb prosthesis based on GMM-HMM","volume":"40","author":"Sheng","year":"2019","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/TNSRE.2016.2529581","article-title":"Noncontact capacitive sensing-based locomotion transition recognition for amputees with robotic transtibial prostheses","volume":"25","author":"Zheng","year":"2017","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1109\/TITB.2011.2165320","article-title":"Detection and Analysis of Transitional Activity in Manifold Space","volume":"16","author":"Ali","year":"2012","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2480","DOI":"10.1109\/TMECH.2017.2755048","article-title":"Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots","volume":"22","author":"Parri","year":"2017","journal-title":"IEEE ASME Trans. Mechatron."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1109\/TNSRE.2015.2412461","article-title":"A classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses","volume":"24","author":"Young","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1109\/TBME.2017.2718528","article-title":"Translational motion tracking of leg joints for enhanced prediction of walking tasks","volume":"65","author":"Stolyarov","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1330","DOI":"10.1109\/TBME.2017.2750139","article-title":"A phase variable approach for IMU-based locomotion activity recognition","volume":"65","author":"Bartlett","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1334","DOI":"10.1109\/TNSRE.2020.2987155","article-title":"IMU-Based locomotion mode identification for transtibial prostheses, orthoses, and exoskeletons","volume":"28","author":"Gao","year":"2020","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_29","first-page":"851","article-title":"Deep learning in bioinformatics","volume":"18","author":"Min","year":"2017","journal-title":"Brief. Bioinform."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1111\/jcpt.13172","article-title":"Efficacy Assessment of Ticagrelor Versus Clopidogrel in Chinese Patients with Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention by Data Mining and Machine-Learning Decision Tree Approaches","volume":"45","author":"Xue","year":"2020","journal-title":"J. Clin. Pharm. Ther."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Billah, Q.M., Rahman, L., Adan, J., Kamal, A.H.M.M., Islam, M.K., Shahnaz, C., and Subhana, A. (2019, January 17\u201320). Design of Intent Recognition System in a Prosthetic Leg for Automatic Switching of Locomotion Modes. Proceedings of the TENCON 2019\u20142019 IEEE Region 10 Conference (TENCON), Kerala, India.","DOI":"10.1109\/TENCON.2019.8929624"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.mechatronics.2015.09.002","article-title":"A foot-wearable interface for locomotion mode recognition based on discrete contact force distribution","volume":"32","author":"Chen","year":"2015","journal-title":"Mechatronics"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Xu, D.F., and Wang, Q.N. (2019, January 3\u20138). BP Neural Network Based On-board Training for Real-time Locomotion Mode Recognition in Robotic Transtibial Prostheses. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macao, China.","DOI":"10.1109\/IROS40897.2019.8968298"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mai, J., Chen, W., Zhang, S., Xu, D., and Wang, Q. (2018, January 25\u201327). Performance analysis of hardware acceleration for locomotion mode recognition in robotic prosthetic control. Proceedings of the IEEE International Conference on Cyborg and Bionic Systems, Shenzhen, China.","DOI":"10.1109\/CBS.2018.8612257"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1142\/S0219843620500048","article-title":"BPNN-based real-time locomotion mode recognition for an active pelvis orthosis with different assistive strategies","volume":"17","author":"Gong","year":"2020","journal-title":"Int. J. Hum. Robot."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112921","DOI":"10.1016\/j.eswa.2019.112921","article-title":"A multi-strategy fusion artificial bee colony algorithm with small population","volume":"142","author":"Song","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, C.S., Huang, J.F., Huang, N.C., and Chen, K.S. (2020). MS Location Estimation Based on the Artificial Bee Colony Algorithm. Sensors, 20.","DOI":"10.3390\/s20195597"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4473","DOI":"10.1007\/s00521-018-3568-0","article-title":"High-quality-guided artificial bee colony algorithm for designing loudspeaker","volume":"32","author":"Gao","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TEVC.2002.802450","article-title":"Ant colony optimization for resource-constrained project scheduling","volume":"6","author":"Merkle","year":"2002","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.epsr.2003.12.009","article-title":"Application of particle swarm optimization technique and its variants to generation expansion planning problem","volume":"70","author":"Kannan","year":"2004","journal-title":"Electr. Power Syst. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1109\/TMECH.2019.2952084","article-title":"Design of a Semipowered Stance-Control Swing-Assist Transfemoral Prosthesis","volume":"25","author":"Lee","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/526\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:10:39Z","timestamp":1760159439000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/526"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,13]]},"references-count":42,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020526"],"URL":"https:\/\/doi.org\/10.3390\/s21020526","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,13]]}}}