{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T04:40:17Z","timestamp":1773117617842,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T00:00:00Z","timestamp":1687737600000},"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>Gait phase recognition is of great importance in the development of rehabilitation devices. The advantages of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) are combined (LSTM-CNN) in this paper, then a gait phase recognition method based on LSTM-CNN neural network model is proposed. In the LSTM-CNN model, the LSTM layer is used to process temporal sequences and the CNN layer is used to extract features A wireless sensor system including six inertial measurement units (IMU) fixed on the six positions of the lower limbs was developed. The difference in the gait recognition performance of the LSTM-CNN model was estimated using different groups of input data collected by seven different IMU grouping methods. Four phases in a complete gait were considered in this paper including the supporting phase with the right hill strike (SU-RHS), left leg swimming phase (SW-L), the supporting phase with the left hill strike (SU-LHS), and right leg swimming phase (SW-R). The results show that the best performance of the model in gait recognition appeared based on the group of data from all the six IMUs, with the recognition precision and macro-F1 unto 95.03% and 95.29%, respectively. At the same time, the best phase recognition accuracy for SU-RHS and SW-R appeared and up to 96.49% and 95.64%, respectively. The results also showed the best phase recognition accuracy (97.22%) for SW-L was acquired based on the group of data from four IMUs located at the left and right thighs and shanks. Comparably, the best phase recognition accuracy (97.86%) for SU-LHS was acquired based on the group of data from four IMUs located at left and right shanks and feet. Ulteriorly, a novel gait recognition method based on Data Pre-Filtering Long Short-Term Memory and Convolutional Neural Network (DPF-LSTM-CNN) model was proposed and its performance for gait phase recognition was evaluated. The experiment results showed that the recognition accuracy reached 97.21%, which was the highest compared to Deep convolutional neural networks (DCNN) and CNN-LSTM.<\/jats:p>","DOI":"10.3390\/s23135905","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T05:28:02Z","timestamp":1687757282000},"page":"5905","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Novel Gait Phase Recognition Method Based on DPF-LSTM-CNN Using Wearable Inertial Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1869-9369","authenticated-orcid":false,"given":"Kun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Shuo","family":"Ji","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Chi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Shizhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]},{"given":"Jun","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,26]]},"reference":[{"key":"ref_1","first-page":"26","article-title":"Recent developments and challenges of lower extremity exoskeletons","volume":"5","author":"Chen","year":"2016","journal-title":"J. Orthop. Transl."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s42235-019-0010-y","article-title":"Locomotion Stability Analysis of Lower Extremity Augmentation Device","volume":"16","author":"Wang","year":"2019","journal-title":"J. Bionic Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"169107","DOI":"10.1109\/ACCESS.2019.2953302","article-title":"Development and Hybrid Control of an Electrically Actuated Lower Limb Exoskeleton for Motion Assistance","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/TRO.2018.2883819","article-title":"A Velocity-Field-Based Controller for Assisting Leg Movement During Walking with a Bilateral Hip and Knee Lower Limb Exoskeleton","volume":"35","author":"Martinez","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1007\/s42235-018-0082-0","article-title":"Parametric Gait Online Generation of a Lower-limb Exoskeleton for Individuals with Paraplegia","volume":"15","author":"Zheng","year":"2018","journal-title":"J. Bionic Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TMRB.2019.2930352","article-title":"Knee Exoskeleton Assistive Torque Control Based on Real-Time Gait Event Detection","volume":"1","author":"Xu","year":"2019","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"57","DOI":"10.3389\/fnbot.2019.00057","article-title":"Stance and Swing Detection Based on the Angular Velocity of Lower Limb Segments During Walking","volume":"13","author":"Grimmer","year":"2019","journal-title":"Front. Neurorobot."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ding, Z., Yang, C., Xing, K., Ma, X., Yang, K., Guo, H., Yi, C., and Jiang, F. (2018, January 18\u201321). The Real Time Gait Phase Detection Based on Long Short-Term Memory. Proceedings of the IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, China.","DOI":"10.1109\/DSC.2018.00014"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117730","DOI":"10.1016\/j.eswa.2022.117730","article-title":"Faster R-CNN and recurrent neural network based approach of gait recognition with and without carried objects","volume":"205","author":"Rajib","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"18578","DOI":"10.1007\/s11227-022-04611-3","article-title":"Exploiting vulnerability of convolutional neural network-based gait recognition system","volume":"78","author":"Bukhari","year":"2022","journal-title":"J. Supercomput."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11277-022-09758-z","article-title":"Gait Recognition Analysis for Human Identification Analysis\u2014A Hybrid Deep Learning Process","volume":"126","author":"Mathivanan","year":"2022","journal-title":"Wirel. Pers. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"117306","DOI":"10.1016\/j.eswa.2022.117306","article-title":"Real-time walking gait terrain classification from foot-mounted Inertial Measurement Unit using Convolutional Long Short-Term Memory neural network","volume":"203","author":"Coelho","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1007\/s42235-022-00230-z","article-title":"Integral Real time Locomotion Mode Recognition Based on GA CNN for Lower Limb Exoskeleton","volume":"19","author":"Wang","year":"2022","journal-title":"J. Bionic Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103693","DOI":"10.1016\/j.bspc.2022.103693","article-title":"Gait phase recognition of lower limb exoskeleton system based on the integrated network model","volume":"76","author":"Zhang","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Su, B., Smith, C., and Gutierrez Farewik, E. (2020). Gait Phase Recognition Using Deep Convolutional Neural Network with Inertial Measurement Units. Biosensors, 10.","DOI":"10.3390\/bios10090109"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"23826","DOI":"10.1109\/ACCESS.2021.3056880","article-title":"Multi-Model Long Short-Term Memory Network for Gait Recognition Using Window-Based Data Segment","volume":"9","author":"Tran","year":"2021","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4999","DOI":"10.1109\/TIE.2021.3082067","article-title":"Gait Phase Classification for a Lower Limb Exoskeleton System Based on a Graph Convolutional Network Model","volume":"69","author":"Wu","year":"2022","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1007\/s42235-021-00083-y","article-title":"A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton","volume":"18","author":"Chen","year":"2021","journal-title":"J. Bionic Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kreuzer, D., and Munz, M. (2021). Deep Convolutional and LSTM Networks on Multi-Channel Time Series Data for Gait Phase Recognition. Sensors, 21.","DOI":"10.3390\/s21030789"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1016\/j.gaitpost.2007.07.007","article-title":"Two simple methods for determining gait events during treadmill and overground walking using kinematic data","volume":"27","author":"Zeni","year":"2008","journal-title":"Gait Posture"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.gaitpost.2014.10.019","article-title":"Adaptive method for real-time gait phase detection based on ground contact forces","volume":"41","author":"Yu","year":"2015","journal-title":"Gait Posture"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zahradka, N., Verma, K., Behboodi, A., Bodt, B., Wright, H., and Lee, S.C.K. (2020). An Evaluation of Three Kinematic Methods for Gait Event Detection Compared to the Kinetic-Based \u2018Gold Standard\u2019. Sensors, 20.","DOI":"10.3390\/s20185272"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1049\/ccs2.12007","article-title":"Personal-specific gait recognition based on latent orthogonal feature space","volume":"3","author":"Zhou","year":"2021","journal-title":"Cogn. Comput. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102279","DOI":"10.1016\/j.bspc.2020.102279","article-title":"Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models","volume":"64","author":"Lu","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"102781","DOI":"10.1016\/j.bspc.2021.102781","article-title":"sEMG-based consecutive estimation of human lower limb movement by using multi-branch neural network","volume":"68","author":"Wang","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"5712108","DOI":"10.1155\/2018\/5712108","article-title":"A Flexible Lower Extremity Exoskeleton Robot with Deep Locomotion Mode Identification","volume":"2018","author":"Wang","year":"2018","journal-title":"Complexity"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5905\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:00:44Z","timestamp":1760126444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/5905"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,26]]},"references-count":27,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23135905"],"URL":"https:\/\/doi.org\/10.3390\/s23135905","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,26]]}}}