{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T16:59:27Z","timestamp":1783616367300,"version":"3.55.0"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"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>Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.<\/jats:p>","DOI":"10.3390\/s23020849","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:47:01Z","timestamp":1673495221000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":61,"title":["Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2638-9643","authenticated-orcid":false,"given":"Ling-Feng","family":"Shi","sequence":"first","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong-Ye","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ke-Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Materials Engineering, Queen\u2019s University, 130 Stuart Street, Kingston, ON K7L 3N6, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiao","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITB.2005.856864","article-title":"Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring","volume":"10","author":"Karantonis","year":"2006","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1109\/TIM.2018.2869262","article-title":"A robust pedestrian dead reckoning system using low-cost magnetic and inertial sensors","volume":"68","author":"Shi","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1109\/RBME.2018.2807182","article-title":"A review on accelerometry-based gait analysis and emerging clinical applications","volume":"11","author":"Jarchi","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1109\/JSEN.2021.3131582","article-title":"Wearable gait recognition systems based on MEMS pressure and inertial sensors: A review","volume":"22","author":"Li","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Liu, G.X., Shi, L.F., Xun, J.H., Chen, S., Liu, H., and Shi, Y.F. (2018, January 22\u201323). Hierarchical calibration architecture based on inertial\/magnetic sensors for indoor positioning. Proceedings of the 2018 Ubiquitous Positioning, Indoor Navigation and Location-Based Services (UPINLBS), Wuhan, China.","DOI":"10.1109\/UPINLBS.2018.8559914"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jung, J., Choi, Y.C., and Choi, S.I. (2021, January 23\u201325). Ensemble learning using pressure sensor for gait recognition. Proceedings of the 2021 IEEE Region 10 Symposium (TENSYMP), Jeju, Republic of Korea.","DOI":"10.1109\/TENSYMP52854.2021.9550860"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Meng, M., She, Q., Gao, Y., and Luo, Z. (2010, January 20\u201323). EMG signals based gait phases recognition using hidden Markov models. Proceedings of the 2010 IEEE International Conference on Information and Automation, Harbin, China.","DOI":"10.1109\/ICINFA.2010.5512456"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1109\/TNNLS.2019.2919764","article-title":"Redundancy and attention in convolutional LSTM for gesture recognition","volume":"31","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/TNNLS.2017.2669522","article-title":"Computational model based on neural network of visual cortex for human action recognition","volume":"29","author":"Liu","year":"2018","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3176","DOI":"10.1109\/TNNLS.2015.2411287","article-title":"Learning a tracking and estimation integrated graphical model for human pose tracking","volume":"26","author":"Zhao","year":"2015","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Sikder, N., Chowdhury, M.S., Arif, A.S.M., and Nahid, A.A. (2019, January 26\u201328). Human activity recognition using multichannel convolutional neural network. Proceedings of the 2019 5th International Conference on Advances in Electrical Engineering (ICAEE), Dhaka, Bangladesh.","DOI":"10.1109\/ICAEE48663.2019.8975649"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Viswambaran, R.A., Chen, G., Xue, B., and Nekooei, M. (2019, January 10\u201313). Evolutionary design of recurrent neural network architecture for human activity recognition. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand.","DOI":"10.1109\/CEC.2019.8790050"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, S., Wei, H., and Zhang, K. (2022, January 14\u201316). Deep bidirectional GRU network for human activity recognition using wearable inertial sensors. Proceedings of the 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Zhuhai, China.","DOI":"10.1109\/IWECAI55315.2022.00054"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, T., Chen, J., Yan, N., and Liu, X. (2018, January 18\u201320). A Multi-Layer Parallel LSTM Network for Human Activity Recognition with Smartphone Sensors. Proceedings of the 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, China.","DOI":"10.1109\/WCSP.2018.8555945"},{"key":"ref_16","unstructured":"Anguita, D., Ghio, A., Oneto, L., Parra, X., and Reyes-Ortiz, J.L. (2013, January 24\u201326). A public domain dataset for human activity recognition using smartphones. Proceedings of the 21th International European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Garcia-Gonzalez, D., Rivero, D., Fernandez-Blanco, E., and Luaces, M.R. (2020). A public domain dataset for real-life human activity recognition using smartphone sensors. Sensors, 20.","DOI":"10.3390\/s20082200"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chereshnev, R., and Kert\u00e9sz-Farkas, A. (2017, January 27\u201329). Hugadb: Human gait database for activity recognition from wearable inertial sensor networks. Proceedings of the International Conference on Analysis of Images, Social Networks and Texts, Moscow, Russia.","DOI":"10.1007\/978-3-319-73013-4_12"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1145\/1964897.1964918","article-title":"Activity recognition using cell phone accelerometers","volume":"12","author":"Kwapisz","year":"2011","journal-title":"ACM SigKDD Explor. Newsl."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"56855","DOI":"10.1109\/ACCESS.2020.2982225","article-title":"LSTM-CNN architecture for human activity recognition","volume":"8","author":"Xia","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3101","DOI":"10.1109\/JSEN.2019.2956901","article-title":"Human action recognition using deep learning methods on limited sensory data","volume":"20","author":"Tufek","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"174105","DOI":"10.1109\/ACCESS.2020.3025938","article-title":"Human daily activity recognition performed using wearable inertial sensors combined with deep learning algorithms","volume":"8","author":"Yen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"27015","DOI":"10.1109\/JSEN.2021.3122258","article-title":"A novel attention-based convolution neural network for human activity recognition","volume":"21","author":"Zheng","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4095","DOI":"10.1007\/s00371-021-02283-3","article-title":"A multibranch CNN-BiLSTM model for human activity recognition using wearable sensor data","volume":"38","author":"Challa","year":"2022","journal-title":"Vis. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"6164","DOI":"10.1109\/JSEN.2022.3148431","article-title":"A novel deep learning Bi-GRU-I model for real-time human activity recognition using inertial sensors","volume":"22","author":"Tong","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yi, M.-K., and Hwang, S.O. (2022, January 6\u20139). Smartphone based human activity recognition using 1D lightweight convolutional neural network. Proceedings of the 2022 International Conference on Electronics, Information, and Communication (ICEIC), Jeju-si, Republic of Korea.","DOI":"10.1109\/ICEIC54506.2022.9748312"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kumari, G., Chakraborty, J., and Nandy, A. (2020, January 1\u20133). Effect of reduced dimensionality on deep learning for human activity recognition. Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT49239.2020.9225419"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"70556","DOI":"10.1109\/ACCESS.2021.3078513","article-title":"Monitoring real-time personal locomotion behaviors over smart indoor-outdoor environments via body-worn sensors","volume":"9","author":"Gochoo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_29","first-page":"667","article-title":"Real-time human activity recognition system based on capsule and LoRa","volume":"21","author":"Shi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1109\/JSEN.2020.3015521","article-title":"Layer-wise training convolutional neural networks with smaller filters for human activity recognition using wearable sensors","volume":"21","author":"Tang","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_31","first-page":"1","article-title":"Deep neural networks for sensor-based human activity recognition using selective kernel convolution","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:03:10Z","timestamp":1760119390000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,11]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020849"],"URL":"https:\/\/doi.org\/10.3390\/s23020849","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,11]]}}}