{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:53:32Z","timestamp":1781621612627,"version":"3.54.5"},"reference-count":130,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11872316"],"award-info":[{"award-number":["11872316"]}],"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":["12272317"],"award-info":[{"award-number":["12272317"]}],"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":["G2022KY0601"],"award-info":[{"award-number":["G2022KY0601"]}],"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":["GK2021KY0604"],"award-info":[{"award-number":["GK2021KY0604"]}],"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":["HYZHXM01003"],"award-info":[{"award-number":["HYZHXM01003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["11872316"],"award-info":[{"award-number":["11872316"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["12272317"],"award-info":[{"award-number":["12272317"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["G2022KY0601"],"award-info":[{"award-number":["G2022KY0601"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["GK2021KY0604"],"award-info":[{"award-number":["GK2021KY0604"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["HYZHXM01003"],"award-info":[{"award-number":["HYZHXM01003"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Space Medical Experiment Project of China Manned Space Project","award":["11872316"],"award-info":[{"award-number":["11872316"]}]},{"name":"Space Medical Experiment Project of China Manned Space Project","award":["12272317"],"award-info":[{"award-number":["12272317"]}]},{"name":"Space Medical Experiment Project of China Manned Space Project","award":["G2022KY0601"],"award-info":[{"award-number":["G2022KY0601"]}]},{"name":"Space Medical Experiment Project of China Manned Space Project","award":["GK2021KY0604"],"award-info":[{"award-number":["GK2021KY0604"]}]},{"name":"Space Medical Experiment Project of China Manned Space Project","award":["HYZHXM01003"],"award-info":[{"award-number":["HYZHXM01003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Abnormal posture or movement is generally the indicator of musculoskeletal injuries or diseases. Mechanical forces dominate the injury and recovery processes of musculoskeletal tissue. Using kinematic data collected from wearable sensors (notably IMUs) as input, activity recognition and musculoskeletal force (typically represented by ground reaction force, joint force\/torque, and muscle activity\/force) estimation approaches based on machine learning models have demonstrated their superior accuracy. The purpose of the present study is to summarize recent achievements in the application of IMUs in biomechanics, with an emphasis on activity recognition and mechanical force estimation. The methodology adopted in such applications, including data pre-processing, noise suppression, classification models, force\/torque estimation models, and the corresponding application effects, are reviewed. The extent of the applications of IMUs in daily activity assessment, posture assessment, disease diagnosis, rehabilitation, and exoskeleton control strategy development are illustrated and discussed. More importantly, the technical feasibility and application opportunities of musculoskeletal force prediction using IMU-based wearable devices are indicated and highlighted. With the development and application of novel adaptive networks and deep learning models, the accurate estimation of musculoskeletal forces can become a research field worthy of further attention.<\/jats:p>","DOI":"10.3390\/s23094229","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T03:04:08Z","timestamp":1682305448000},"page":"4229","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":37,"title":["Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation"],"prefix":"10.3390","volume":"23","author":[{"given":"Wenqi","family":"Liang","sequence":"first","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fanjie","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ao","family":"Fan","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenrui","family":"Zhao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yao","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3681-763X","authenticated-orcid":false,"given":"Pengfei","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Space Bioscience and Biotechnology, School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.inffus.2018.06.002","article-title":"Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions","volume":"46","author":"Nweke","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, T., Zhou, Y., and Zhu, J. (2018). New Advances and Challenges of Fall Detection Systems: A Survey. Appl. Sci., 8.","DOI":"10.3390\/app8030418"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6514520","DOI":"10.1155\/2019\/6514520","article-title":"Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts","volume":"2019","author":"Masoud","year":"2019","journal-title":"J. Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.3389\/fbioe.2020.00009","article-title":"A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks","volume":"8","author":"Stetter","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"320","DOI":"10.3389\/fbioe.2020.00320","article-title":"A Machine Learning Approach to Estimate Hip and Knee Joint Loading Using a Mobile Phone-Embedded IMU","volume":"8","author":"Emmerzaal","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, M., Deng, J., Zha, F., Qiu, S., Wang, X., and Chen, F. (2018). Towards Online Estimation of Human Joint Muscular Torque with a Lower Limb Exoskeleton Robot. Appl. Sci., 8.","DOI":"10.3390\/app8091610"},{"key":"ref_7","unstructured":"Yi, C., Zhang, S., Jiang, F., Liu, J., Ding, Z., Yang, C., and Zhou, H. (2021). IEEE Transactions on Cognitive and Developmental Systems, IEEE."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/TBME.2020.3006158","article-title":"Multidimensional Ground Reaction Forces and Moments from Wearable Sensor Accelerations via Deep Learning","volume":"68","author":"Johnson","year":"2021","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lee, M., and Park, S. (2020). Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum. Sensors, 20.","DOI":"10.3390\/s20216277"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"604","DOI":"10.3389\/fbioe.2020.00604","article-title":"CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics from Measured and Simulated Inertial Sensor Data","volume":"8","author":"Dorschky","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3389\/fbioe.2020.00041","article-title":"Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network","volume":"8","author":"Mundt","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83041","DOI":"10.1109\/ACCESS.2021.3085660","article-title":"Deep Learning Techniques in Estimating Ankle Joint Power Using Wearable IMUs","volume":"9","author":"Barua","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mundt, M., Johnson, W.R., Potthast, W., Markert, B., Mian, A., and Alderson, J. (2021). A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units. Sensors, 21.","DOI":"10.3390\/s21134535"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e12752","DOI":"10.7717\/peerj.12752","article-title":"Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: A recurrent neural network solution","volume":"10","author":"Alcantara","year":"2022","journal-title":"PeerJ"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1080\/17434440.2016.1198694","article-title":"Wearable inertial sensors for human movement analysis","volume":"13","author":"Iosa","year":"2016","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lim, H., Kim, B., and Park, S. (2019). Prediction of Lower Limb Kinetics and Kinematics during Walking by a Single IMU on the Lower Back Using Machine Learning. Sensors, 20.","DOI":"10.3390\/s20010130"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"817","DOI":"10.3389\/fpsyg.2017.00817","article-title":"Inertial Sensors to Assess Gait Quality in Patients with Neurological Disorders: A Systematic Review of Technical and Analytical Challenges","volume":"8","author":"Barrois","year":"2017","journal-title":"Front. Psychol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Clemente, F.M., Akyildiz, Z., Pino-Ortega, J., and Rico-Gonz\u00e1lez, M. (2021). Validity and Reliability of the Inertial Measurement Unit for Barbell Velocity Assessments: A Systematic Review. Sensors, 21.","DOI":"10.3390\/s21072511"},{"key":"ref_19","unstructured":"Yang, J.B., Nguyen, M.N., San, P.P., Li, X.L., and Krishnaswamy, S. Deep convolutional neural networks on multichannel time series for human activity recognition. Proceedings of the 24th International Conference on Artificial Intelligence."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/JBHI.2019.2909688","article-title":"TSE-CNN: A Two-Stage End-to-End CNN for Human Activity Recognition","volume":"24","author":"Huang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Alemayoh, T.T., Lee, J.H., and Okamoto, S. (2021). New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition. Sensors, 21.","DOI":"10.3390\/s21082814"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Steven Eyobu, O., and Han, D.S. (2018). Feature Representation and Data Augmentation for Human Activity Classification Based on Wearable IMU Sensor Data Using a Deep LSTM Neural Network. Sensors, 18.","DOI":"10.3390\/s18092892"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ullah, M., Ullah, H., Khan, S.D., and Cheikh, F.A. (2019, January 28\u201331). Stacked Lstm Network for Human Activity Recognition Using Smartphone Data. Proceedings of the 2019 8th European Workshop on Visual Information Processing (EUVIP), Rome, Italy.","DOI":"10.1109\/EUVIP47703.2019.8946180"},{"key":"ref_24","unstructured":"Mahmud, S., Tonmoy, M.T.H., Bhaumik, K., Rahman, A., Amin, M.A., Shoyaib, M., Khan, M., and Ali, A. (2020). Human Activity Recognition from Wearable Sensor Data Using Self-Attention. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4969","DOI":"10.1109\/JIOT.2020.3033430","article-title":"ST-DeepHAR: Deep Learning Model for Human Activity Recognition in IoHT Applications","volume":"8","author":"Hawash","year":"2021","journal-title":"IEEE Internet Things"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Haresamudram, H.K., Beedu, A., Agrawal, V., Grady, P., Essa, I., Hoffman, J., and Pl\u00f6tz, T. (2020, January 12\u201316). Masked reconstruction based self-supervision for human activity recognition. Proceedings of the 2020 ACM International Symposium on Wearable Computers, Virtual.","DOI":"10.1145\/3410531.3414306"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1109\/TMECH.2019.2948650","article-title":"Wireless Ground Reaction Force Sensing System Using a Mechanically Decoupled Two-Dimensional Force Sensor","volume":"25","author":"Kim","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1016\/j.jbiomech.2008.06.001","article-title":"Whole body inverse dynamics over a complete gait cycle based only on measured kinematics","volume":"41","author":"Ren","year":"2008","journal-title":"J. Biomech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1109\/TBME.2017.2704085","article-title":"Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms","volume":"65","author":"Durandau","year":"2018","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ancillao, A., Tedesco, S., Barton, J., and O\u2019Flynn, B. (2018). Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review. Sensors, 18.","DOI":"10.3390\/s18082564"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gurchiek, R.D., Cheney, N., and McGinnis, R.S. (2019). Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques. Sensors, 19.","DOI":"10.20944\/preprints201911.0006.v1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lee, C.J., and Lee, J.K. (2022). Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review. Sensors, 22.","DOI":"10.3390\/s22072507"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/JSEN.2017.2720725","article-title":"An Adaptive Algorithm to Improve Energy Efficiency in Wearable Activity Recognition Systems","volume":"17","author":"Rezaie","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Safi, K., Attal, F., Mohammed, S., Khalil, M., and Amirat, Y. (2015, January 16\u201318). Physical Activity Recognition Using Inertial Wearable Sensors\u2014A Review of Supervised Classification Algorithms. Proceedings of the 2015 International Conference on Advances in Biomedical Engineering (ICABME), Beirut, Lebanon.","DOI":"10.1109\/ICABME.2015.7323315"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.neunet.2018.02.017","article-title":"Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors","volume":"102","year":"2018","journal-title":"Neural Netw."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1649","DOI":"10.1093\/comjnl\/bxt075","article-title":"Recognizing Daily and Sports Activities in Two Open Source Machine Learning Environments Using Body-Worn Sensor Units","volume":"57","author":"Barshan","year":"2014","journal-title":"Comput. J."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/3380999","article-title":"GIobalFusion: A Global Attentional Deep Learning Framework for Multisensor Information Fusion","volume":"4","author":"Liu","year":"2020","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Yao, S., Hu, S., Zhao, Y., Zhang, A., and Abdelzaher, T. (2017, January 3\u20137). DeepSense: A Unified Deep Learning Framework for Time-Series Mobile Sensing Data Processing. Proceedings of the 26th International Conference on World Wide Web, International World Wide Web, Perth, Australia.","DOI":"10.1145\/3038912.3052577"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.3390\/s130201402","article-title":"Human Behavior Cognition Using Smartphone Sensors","volume":"13","author":"Pei","year":"2013","journal-title":"Sensors"},{"key":"ref_40","unstructured":"Liu, Y., Zhao, F., Shao, W., and Luo, H. (2016, January 2\u20134). In An Hidden Markov Model Based Complex Walking Pattern Recognition Algorithm. Proceedings of the 2016 Fourth International Conference on Ubiquitous Positioning, Indoor Navigation and Location Based Services (UPINLBS), Shanghai, China."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1016\/j.asoc.2017.09.027","article-title":"Real-time human activity recognition from accelerometer data using Convolutional Neural Networks","volume":"62","author":"Ignatov","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhong, K., Zhang, J., Sun, Q., and Zhao, X. (2016, January 24\u201325). LSTM Networks for Mobile Human Activity Recognition. Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications, Bangkok, Thailand.","DOI":"10.2991\/icaita-16.2016.13"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7316954","DOI":"10.1155\/2018\/7316954","article-title":"Deep Residual Bidir-LSTM for Human Activity Recognition Using Wearable Sensors","volume":"2018","author":"Zhao","year":"2018","journal-title":"Math. Probl. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1016\/j.neucom.2015.07.085","article-title":"Transition-Aware Human Activity Recognition Using Smartphones","volume":"171","author":"Oneto","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mekruksavanich, S., and Jitpattanakul, A. (2021). Deep Convolutional Neural Network with RNNs for Complex Activity Recognition Using Wrist-Worn Wearable Sensor Data. Electronics, 10.","DOI":"10.3390\/electronics10141685"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Ma, H., Li, W., Zhang, X., Gao, S., and Lu, S. (2019, January 10\u201316). AttnSense: Multi-level attention mechanism for multimodal human activity recognition. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2019\/431"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ord\u00f3\u00f1ez, F.J., and Roggen, D. (2016). Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition. Sensors, 16.","DOI":"10.3390\/s16010115"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3448083","article-title":"Attend and Discriminate: Beyond the State-of-the-Art for Human Activity Recognition Using Wearable Sensors","volume":"5","author":"Abedin","year":"2021","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5290","DOI":"10.1109\/JSEN.2017.2722105","article-title":"Recognizing Human Activity in Free-Living Using Multiple Body-Worn Accelerometers","volume":"17","author":"Fullerton","year":"2017","journal-title":"IEEE Sens. J."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3090076","article-title":"Ensembles of Deep LSTM Learners for Activity Recognition using Wearables","volume":"1","author":"Guan","year":"2017","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zeng, M., Gao, H., Yu, T., Mengshoel, O.J., Langseth, H., Lane, I., and Liu, X. (2018, January 8\u201312). Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention. Proceedings of the 2018 ACM International Symposium on Wearable Computers, Singapore.","DOI":"10.1145\/3267242.3267286"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Staab, S., Krissel, S., Luderschmidt, J., and Martin, L. (2022, January 10\u201312). Recognition Models for Distribution and Out-of-Distribution of Human Activities. Proceedings of the 2022 18th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Thessaloniki, Greece.","DOI":"10.1109\/WiMob55322.2022.9941671"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"100333","DOI":"10.1016\/j.smhl.2022.100333","article-title":"Automated documentation of almost identical movements in the context of dementia diagnostics","volume":"26","author":"Staab","year":"2022","journal-title":"Smart Health"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Arai, K. (2023). Intelligent Systems and Applications, Springer International Publishing.","DOI":"10.1007\/978-3-031-16072-1"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Staab, S., Luderschmidt, J., and Martin, L. (2021, January 17\u201319). Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning. Proceedings of the 2021 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China.","DOI":"10.1109\/PIC53636.2021.9687025"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"218","DOI":"10.3389\/fphys.2018.00218","article-title":"Estimation of Vertical Ground Reaction Forces and Sagittal Knee Kinematics During Running Using Three Inertial Sensors","volume":"9","author":"Wouda","year":"2018","journal-title":"Front. Physiol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Zhu, Y., Xia, D., and Zhang, H. (2023). Using Wearable Sensors to Estimate Vertical Ground Reaction Force Based on a Transformer. Appl. Sci., 13.","DOI":"10.3390\/app13042136"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Guo, Y., Storm, F., Zhao, Y., Billings, S.A., Pavic, A., Mazz\u00e0, C., and Guo, L.-Z. (2017). A New Proxy Measurement Algorithm with Application to the Estimation of Vertical Ground Reaction Forces Using Wearable Sensors. Sensors, 17.","DOI":"10.3390\/s17102181"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/TNSRE.2018.2830976","article-title":"Real-Life Measurement of Tri-Axial Walking Ground Reaction Forces Using Optimal Network of Wearable Inertial Measurement Units","volume":"26","author":"Shahabpoor","year":"2018","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Chaaban, C.R., Berry, N.T., Armitano-Lago, C., Kiefer, A.W., Mazzoleni, M.J., and Padua, D.A. (2021). Combining Inertial Sensors and Machine Learning to Predict vGRF and Knee Biomechanics during a Double Limb Jump Landing Task. Sensors, 21.","DOI":"10.3390\/s21134383"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1109\/TMRB.2022.3144025","article-title":"Subject-Independent, Biological Hip Moment Estimation During Multimodal Overground Ambulation Using Deep Learning","volume":"4","author":"Molinaro","year":"2022","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Stetter, B.J., Ringhof, S., Krafft, F.C., Sell, S., and Stein, T. (2019). Estimation of Knee Joint Forces in Sport Movements Using Wearable Sensors and Machine Learning. Sensors, 19.","DOI":"10.3390\/s19173690"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hossain, M.S.B., Guo, Z., and Choi, H. (2023). Estimation of Lower Extremity Joint Moments and 3D Ground Reaction Forces Using IMU Sensors in Multiple Walking Conditions: A Deep Learning Approach. IEEE J. Biomed. Health Inform., 1\u201312.","DOI":"10.1109\/JBHI.2023.3262164"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Tedesco, S., Alfieri, D., Perez-Valero, E., Komaris, D.-S., Jordan, L., Belcastro, M., Barton, J., Hennessy, L., and O\u2019Flynn, B. (2021). A Wearable System for the Estimation of Performance-Related Metrics during Running and Jumping Tasks. Appl. Sci., 11.","DOI":"10.3390\/app11115258"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Jiang, X., Gholami, M., Khoshnam, M., Eng, J.J., and Menon, C. (2019). Estimation of Ankle Joint Power during Walking Using Two Inertial Sensors. Sensors, 19.","DOI":"10.3390\/s19122796"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3389\/fbioe.2020.00033","article-title":"Tibial Acceleration-Based Prediction of Maximal Vertical Loading Rate During Overground Running: A Machine Learning Approach","volume":"8","author":"Derie","year":"2020","journal-title":"Front. Bioeng. Biotechnol."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hu, W., Yang, Y., Hou, J., and Wang, Z. (2014, January 28\u201330). A method to calibrate installation orientation errors of inertial sensors for gait analysis. Proceedings of the 2014 IEEE International Conference on Information and Automation (ICIA), Hailar, China.","DOI":"10.1109\/ICInfA.2014.6932724"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1215","DOI":"10.1109\/JBHI.2020.3014963","article-title":"Accurate Impact Loading Rate Estimation During Running via a Subject-Independent Convolutional Neural Network Model and Optimal IMU Placement","volume":"25","author":"Tan","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1111\/sms.13396","article-title":"Shoe-mounted accelerometers should be used with caution in gait retraining","volume":"29","author":"Cheung","year":"2019","journal-title":"Scand. J. Med. Sci. Sport."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"9995","DOI":"10.3390\/s140609995","article-title":"Dealing with the Effects of Sensor Displacement in Wearable Activity Recognition","volume":"14","author":"Banos","year":"2014","journal-title":"Sensors"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/MPRV.2014.73","article-title":"Sensor Placement Variations in Wearable Activity Recognition","volume":"13","author":"Kunze","year":"2014","journal-title":"IEEE Pervasive Comput."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Yurtman, A., and Barshan, B. (2017). Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors. Sensors, 17.","DOI":"10.3390\/s17081838"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"109416","DOI":"10.1016\/j.jbiomech.2019.109416","article-title":"Influence of IMU position and orientation placement errors on ground reaction force estimation","volume":"97","author":"Tan","year":"2019","journal-title":"J. Biomech."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Qiu, S., Liu, L., Zhao, H., Wang, Z., and Jiang, Y. (2018). MEMS Inertial Sensors Based Gait Analysis for Rehabilitation Assessment via Multi-Sensor Fusion. Micromachines, 9.","DOI":"10.3390\/mi9090442"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Vargas-Valencia, L.S., Elias, A., Rocon, E., Bastos-Filho, T., and Frizera, A. (2016). An IMU-to-Body Alignment Method Applied to Human Gait Analysis. Sensors, 16.","DOI":"10.3390\/s16122090"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3389\/frobt.2018.00127","article-title":"Corrigendum: Benchmark Datasets for Bilateral Lower-Limb Neuromechanical Signals from Wearable Sensors during Unassisted Locomotion in Able-Bodied Individuals","volume":"5","author":"Hu","year":"2018","journal-title":"Front. Robot. AI"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"110320","DOI":"10.1016\/j.jbiomech.2021.110320","article-title":"A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions","volume":"119","author":"Camargo","year":"2021","journal-title":"J. Biomech."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Hamdi, M.M., Awad, M.I., Abdelhameed, M.M., and Tolbah, F.A. (2014, January 11\u201313). Lower limb motion tracking using IMU sensor network. Proceedings of the 2014 Cairo International Biomedical Engineering Conference (CIBEC), Giza, Egypt.","DOI":"10.1109\/CIBEC.2014.7020957"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TIM.2016.2642658","article-title":"Improving the Accuracy of Human Body Orientation Estimation With Wearable IMU Sensors","volume":"66","author":"Ahmed","year":"2017","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Faber, H., van Soest, A.J., and Kistemaker, D.A. (2018). Inverse dynamics of mechanical multibody systems: An improved algorithm that ensures consistency between kinematics and external forces. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0204575"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Hwang, S., Choi, S., Lee, Y.S., and Kim, J. (2021). A Novel Simplified System to Estimate Lower-Limb Joint Moments during Sit-to-Stand. Sensors, 21.","DOI":"10.3390\/s21020521"},{"key":"ref_82","first-page":"125","article-title":"Measurement of Joint Moments using Wearable Sensors","volume":"9","author":"Fukutoku","year":"2020","journal-title":"IEEJ J. Ind. Appl."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Madgwick, S.O.H., Harrison, A.J.L., and Vaidyanathan, R. (July, January 29). Estimation of IMU and MARG Orientation Using a Gradient Descent Algorithm. Proceedings of the 2011 IEEE International Conference on Rehabilitation Robotics, Zurich, Switzerland.","DOI":"10.1109\/ICORR.2011.5975346"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1109\/TRO.2006.886270","article-title":"Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking","volume":"22","author":"Yun","year":"2006","journal-title":"IEEE Trans. Robot."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"3170","DOI":"10.1109\/TMECH.2015.2430357","article-title":"Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System","volume":"20","author":"Wang","year":"2015","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.inffus.2020.10.018","article-title":"40 years of sensor fusion for orientation tracking via magnetic and inertial measurement units: Methods, lessons learned, and future challenges","volume":"68","author":"Nazarahari","year":"2021","journal-title":"Inf. Fusion"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"He, Y., and Pi, D. (2016, January 15\u201318). In Anomaly Detection Algorithm for Helicopter Rotor Based on STFT and SVDD. Proceedings of the International Conference on Communication, Computing & Security, Kauai, HI, USA.","DOI":"10.1007\/978-3-319-48674-1_34"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Lin, J., Keogh, E., Lonardi, S., and Chiu, B. (2003, January 13). A symbolic representation of time series, with implications for streaming algorithms. Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, San Diego, CA, USA.","DOI":"10.1145\/882082.882086"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Huynh, T., and Schiele, B. (2005, January 12\u201314). Analyzing features for activity recognition. Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context-Aware Services: Usages and Technologies, Grenoble, France.","DOI":"10.1145\/1107548.1107591"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2499621","article-title":"A Tutorial on Human Activity Recognition Using Body-Worn Inertial Sensors","volume":"46","author":"Bulling","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1109\/TITB.2009.2036165","article-title":"Wearable Assistant for Parkinson\u2019s Disease Patients with the Freezing of Gait Symptom","volume":"14","author":"Bachlin","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"101963","DOI":"10.1016\/j.bspc.2020.101963","article-title":"Accuracy comparison of dimensionality reduction techniques to determine significant features from IMU sensor-based data to diagnose vestibular system disorders","volume":"61","author":"Heydarov","year":"2020","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"31715","DOI":"10.1109\/ACCESS.2018.2839766","article-title":"Human Daily and Sport Activity Recognition Using a Wearable Inertial Sensor Network","volume":"6","author":"Hsu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.future.2017.11.029","article-title":"A robust human activity recognition system using smartphone sensors and deep learning","volume":"81","author":"Hassan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Zeng, M., Nguyen, L.T., Yu, B., Mengshoel, O.J., Zhu, J., Wu, P., and Zhang, J. (2014, January 6\u20137). Convolutional Neural Networks for human activity recognition using mobile sensors. Proceedings of the 6th International Conference on Mobile Computing, Applications and Services, Austin, TX, USA.","DOI":"10.4108\/icst.mobicase.2014.257786"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Chen, Y., and Xue, Y. (2015, January 9\u201312). A Deep Learning Approach to Human Activity Recognition Based on Single Accelerometer. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China.","DOI":"10.1109\/SMC.2015.263"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Jiang, X., Napier, C., Hannigan, B., Eng, J.J., and Menon, C. (2020). Estimating Vertical Ground Reaction Force during Walking Using a Single Inertial Sensor. Sensors, 20.","DOI":"10.3390\/s20154345"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Soviany, S., S\u0103ndulescu, V., and Pu\u015fcoci, S. (2017, January 22\u201324). In The hierarchical classification model using Support Vector Machine with multiple kernels in human behavioral pattern recognition. Proceedings of the 2017 E-Health and Bioengineering Conference (EHB), Sinaia, Romania.","DOI":"10.1109\/EHB.2017.7995516"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/JSTSP.2020.2987728","article-title":"Multimodal Intelligence: Representation Learning, Information Fusion, and Applications","volume":"14","author":"Zhang","year":"2020","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Schlachetzki, J., Barth, J., Marxreiter, F., Gossler, J., Kohl, Z., Reinfelder, S., Ga\u00dfner, H., Aminian, K., Eskofier, B., and Winkler, J. (2017). Wearable sensors objectively measure gait parameters in Parkinson\u2019s disease. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183989"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Amin, J., and Ruthiraphong, P. (2021, January 19\u201322). In Cloud-based Gait Analysis Using a Single IMU for Parkinson Disease. Proceedings of the 2021 18th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), Chiang Mai, Thailand.","DOI":"10.1109\/ECTI-CON51831.2021.9454716"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1970","DOI":"10.1109\/LRA.2020.2970656","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_104","doi-asserted-by":"crossref","unstructured":"Alcaraz, J.C., Moghaddamnia, S., Penner, M., and Peissig, J. (2021, January 18\u201321). Monitoring the Rehabilitation Progress Using a DCNN and Kinematic Data for Digital Healthcare. Proceedings of the 2020 28th European Signal Processing Conference (EUSIPCO), Amsterdam, The Netherlands.","DOI":"10.23919\/Eusipco47968.2020.9287324"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1849","DOI":"10.1109\/JSEN.2013.2245231","article-title":"HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer","volume":"13","author":"Tong","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Alesin, A., Osanlou, A., and Maw, S.W. (February, January 29). In A low budget multifunctional wearable device for motion and falls detection. Proceedings of the 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Moscow, Russia; St. Petersburg, Russia.","DOI":"10.1109\/EIConRus.2018.8317468"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"1","DOI":"10.7575\/aiac.ijkss.v.8n.4p.1","article-title":"Validity and Reliability of StriveTM Sense3 for Muscle Activity Monitoring During the Squat Exercise","volume":"8","author":"Davarzani","year":"2020","journal-title":"Int. J. Kinesiol. Sport. Sci."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"902","DOI":"10.4085\/1062-6050-0540.19","article-title":"\u201cTo Tech or Not to Tech?\u201d A Critical Decision-Making Framework for Implementing Technology in Sport","volume":"55","author":"Windt","year":"2020","journal-title":"J. Athl. Train."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Wittmann, F., Lambercy, O., Gonzenbach, R.R., Raai, M.A.V., H\u00f6ver, R., Held, J., Starkey, M.L., Curt, A., Luft, A., and Gassert, R. (2015, January 11\u201314). Assessment-driven arm therapy at home using an IMU-based virtual reality system. Proceedings of the 2015 IEEE International Conference on Rehabilitation Robotics (ICORR), Singapore.","DOI":"10.1109\/ICORR.2015.7281284"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1088\/0967-3334\/33\/12\/2099","article-title":"Human pose recovery using wireless inertial measurement units","volume":"33","author":"Lin","year":"2012","journal-title":"Physiol. Meas."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Alemayoh, T.T., Lee, J.H., and Okamoto, S. (2022, January 21\u201324). LocoESIS: Deep-Learning-Based Leg-Joint Angle Estimation from a Single Pelvis Inertial Sensor. Proceedings of the 2022 9th IEEE RAS\/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), Seoul, Republic of Korea.","DOI":"10.1109\/BioRob52689.2022.9925420"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Sung, J., Han, S., Park, H., Cho, H.-M., Hwang, S., Park, J.W., and Youn, I. (2022). Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network. Sensors, 22.","DOI":"10.3390\/s22010053"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1701","DOI":"10.1097\/00005768-200110000-00014","article-title":"Overuse injuries in youth sports: Biomechanical considerations","volume":"33","author":"Hawkins","year":"2001","journal-title":"Med. Sci. Sport. Exerc."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/S0268-0033(02)00072-4","article-title":"Frequency domain characteristics of ground reaction forces during walking of young and elderly females","volume":"17","author":"Stergiou","year":"2002","journal-title":"Clin. Biomech."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Billings, S. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains, John Wiley & Sons.","DOI":"10.1002\/9781118535561"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"11258","DOI":"10.3390\/s150511258","article-title":"Estimation of joint forces and moments for the in-run and take-off in ski jumping based on measurements with wearable inertial sensors","volume":"15","author":"Logar","year":"2015","journal-title":"Sensors"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Liu, T., Inoue, Y., Shibata, K., and Shiojima, K. (2011, January 9\u201313). Three-dimensional lower limb kinematic and kinetic analysis based on a wireless sensor system. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5979856"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.measurement.2015.05.020","article-title":"3D analysis system for estimating intersegmental forces and moments exerted on human lower limbs during walking motion","volume":"73","author":"Yang","year":"2015","journal-title":"Measurement"},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"2164","DOI":"10.1016\/j.jbiomech.2010.03.046","article-title":"Loading of the knee joint during activities of daily living measured in vivo in five subjects","volume":"43","author":"Kutzner","year":"2010","journal-title":"J. Biomech."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Yang, W., Zhang, J., Zhang, S., and Yang, C. (2020). Lower Limb Exoskeleton Gait Planning Based on Crutch and Human-Machine Foot Combined Center of Pressure. Sensors, 20.","DOI":"10.3390\/s20247216"},{"key":"ref_121","first-page":"1266","article-title":"A novel human effort estimation method for knee assistive exoskeletons","volume":"2017","author":"Saccares","year":"2017","journal-title":"IEEE Int. Conf. Rehabil. Robot."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Chen, H.P., Chen, H.C., Liu, K.C., and Chan, C.T. (2016, January 14\u201317). Online segmentation with multi-layer SVM for knee osteoarthritis rehabilitation monitoring. Proceedings of the 2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN), San Francisco, CA, USA.","DOI":"10.1109\/BSN.2016.7516232"},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1186\/s12984-017-0309-z","article-title":"Wearable sensors to predict improvement following an exercise intervention in patients with knee osteoarthritis","volume":"14","author":"Kobsar","year":"2017","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_124","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_125","first-page":"3","article-title":"A public domain dataset for human activity recognition using smartphones","volume":"2013","author":"Anguita","year":"2013","journal-title":"Esann"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2059","DOI":"10.3390\/s150102059","article-title":"A Survey of Online Activity Recognition Using Mobile Phones","volume":"15","author":"Shoaib","year":"2015","journal-title":"Sensors"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"D\u00edez, L.E., Bahillo, A., Masegosa, A.D., Perallos, A., Azpilicueta, L., Falcone, F., Astrain, J.J., and Villadangos, J. (2015, January 7\u201311). Signal processing requirements for step detection using wrist-worn IMU. Proceedings of the 2015 International Conference on Electromagnetics in Advanced Applications (ICEAA), Cartagena des Indias, Colombia.","DOI":"10.1109\/ICEAA.2015.7297271"},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"768","DOI":"10.1016\/j.future.2022.12.024","article-title":"A data balancing approach based on generative adversarial network","volume":"141","author":"Yuan","year":"2023","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"124919","DOI":"10.1016\/j.energy.2022.124919","article-title":"Data augmentation for improving heating load prediction of heating substation based on TimeGAN","volume":"260","author":"Zhang","year":"2022","journal-title":"Energy"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"100957","DOI":"10.1016\/j.trgeo.2023.100957","article-title":"Data-driven analysis on the subbase strain prediction: A deep data augmentation-based study","volume":"40","author":"Yao","year":"2023","journal-title":"Transp. Geotech."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4229\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:22:12Z","timestamp":1760124132000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/9\/4229"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":130,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["s23094229"],"URL":"https:\/\/doi.org\/10.3390\/s23094229","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,24]]}}}