{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T03:42:06Z","timestamp":1784000526609,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"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>Recent technological developments, such as the Internet of Things (IoT), artificial intelligence, edge, and cloud computing, have paved the way in transforming traditional healthcare systems into smart healthcare (SHC) systems. SHC escalates healthcare management with increased efficiency, convenience, and personalization, via use of wearable devices and connectivity, to access information with rapid responses. Wearable devices are equipped with multiple sensors to identify a person\u2019s movements. The unlabeled data acquired from these sensors are directly trained in the cloud servers, which require vast memory and high computational costs. To overcome this limitation in SHC, we propose a federated learning-based person movement identification (FL-PMI). The deep reinforcement learning (DRL) framework is leveraged in FL-PMI for auto-labeling the unlabeled data. The data are then trained using federated learning (FL), in which the edge servers allow the parameters alone to pass on the cloud, rather than passing vast amounts of sensor data. Finally, the bidirectional long short-term memory (BiLSTM) in FL-PMI classifies the data for various processes associated with the SHC. The simulation results proved the efficiency of FL-PMI, with 99.67% accuracy scores, minimized memory usage and computational costs, and reduced transmission data by 36.73%.<\/jats:p>","DOI":"10.3390\/s22041377","type":"journal-article","created":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T05:14:43Z","timestamp":1644556483000},"page":"1377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":158,"title":["FL-PMI: Federated Learning-Based Person Movement Identification through Wearable Devices in Smart Healthcare Systems"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9522-2105","authenticated-orcid":false,"given":"K. S.","family":"Arikumar","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, St. Joseph\u2019s Institute of Technology, Chennai 600119, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sahaya Beni","family":"Prathiba","sequence":"additional","affiliation":[{"name":"Department of Computer Technology, Anna University, Chennai 600025, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-3704","authenticated-orcid":false,"given":"Mamoun","family":"Alazab","sequence":"additional","affiliation":[{"name":"College of Engineering, IT and Environment, Charles Darwin University, Casuarina, NT 0815, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-801X","authenticated-orcid":false,"given":"Thippa Reddy","family":"Gadekallu","sequence":"additional","affiliation":[{"name":"School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4507-1844","authenticated-orcid":false,"given":"Sharnil","family":"Pandya","sequence":"additional","affiliation":[{"name":"Symbiosis Institute of India, Symbiosis International (Deemed) University, Pune 411042, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Javed Masood","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Food Science and Nutrition, Faculty of Food and Agricultural Sciences, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajalakshmi Shenbaga","family":"Moorthy","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6632599","DOI":"10.1155\/2021\/6632599","article-title":"IoT-Based Applications in Healthcare Devices","volume":"2021","author":"Pradhan","year":"2021","journal-title":"J. Healthc. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Arikumar, K., and Natarajan, V. (2021). FIoT: A QoS-Aware Fog-IoT Framework to Minimize Latency in IoT Applications via Fog Offloading. Evolution in Computational Intelligence, Springer.","DOI":"10.1007\/978-981-15-5788-0_53"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"McGrath, M.J., and Scanaill, C.N. (2013). Sensing and sensor fundamentals. Sensor Technologies, Springer.","DOI":"10.1007\/978-1-4302-6014-1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10245","DOI":"10.1007\/s13369-020-04616-1","article-title":"EELTM: An energy efficient LifeTime maximization approach for WSN by PSO and fuzzy-based unequal clustering","volume":"45","author":"Arikumar","year":"2020","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3090343","DOI":"10.1155\/2017\/3090343","article-title":"A review on human activity recognition using vision-based method","volume":"2017","author":"Zhang","year":"2017","journal-title":"J. Healthc. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sztyler, T., and Stuckenschmidt, H. (2016, January 14\u201319). On-body Localization of Wearable Devices: An Investigation of Position-Aware Activity Recognition. Proceedings of the 2016 IEEE International Conference on Pervasive Computing and Communications (PerCom), Sydney, NSW, Australia.","DOI":"10.1109\/PERCOM.2016.7456521"},{"key":"ref_7","unstructured":"Wu, M., and Luo, J. (2019). Wearable technology applications in healthcare: A literature review. Online J. Nurs. Inform., 23."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2132138","DOI":"10.1155\/2020\/2132138","article-title":"Wearable sensor-based human activity recognition using hybrid deep learning techniques","volume":"2020","author":"Wang","year":"2020","journal-title":"Secur. Commun. Netw."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1092","DOI":"10.1016\/j.csbj.2021.01.028","article-title":"Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring","volume":"19","author":"Ghannam","year":"2021","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"68022","DOI":"10.1109\/ACCESS.2019.2917125","article-title":"SmartWall: Novel RFID-enabled ambient human activity recognition using machine learning for unobtrusive health monitoring","volume":"7","author":"Oguntala","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83791","DOI":"10.1109\/ACCESS.2020.2991891","article-title":"Sensing technology for human activity recognition: A comprehensive survey","volume":"8","author":"Fu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"7731","DOI":"10.1109\/ACCESS.2020.2964237","article-title":"C2FHAR: Coarse-to-fine human activity recognition with behavioral context modeling using smart inertial sensors","volume":"8","author":"Azam","year":"2020","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1109\/JIOT.2018.2850664","article-title":"Wearable sensor devices for prevention and rehabilitation in healthcare: Swimming exercise with real-time therapist feedback","volume":"6","author":"Kos","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2749","DOI":"10.1109\/JIOT.2018.2873594","article-title":"Wearable computing for Internet of Things: A discriminant approach for human activity recognition","volume":"6","author":"Lu","year":"2018","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1109\/TMM.2017.2726187","article-title":"Deep temporal multimodal fusion for medical procedure monitoring using wearable sensors","volume":"20","author":"Bernal","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hwang, I., Cha, G., and Oh, S. (2017, January 16\u201318). Multi-modal human action recognition using deep neural networks fusing image and inertial sensor data. Proceedings of the 2017 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI), Daegu, Korea.","DOI":"10.1109\/MFI.2017.8170441"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"164453","DOI":"10.1109\/ACCESS.2020.3022287","article-title":"WiWeHAR: Multimodal Human Activity Recognition Using Wi-Fi and Wearable Sensing Modalities","volume":"8","author":"Muaaz","year":"2020","journal-title":"IEEE Access"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Micucci, D., Mobilio, M., and Napoletano, P. (2017). Unimib shar: A dataset for human activity recognition using acceleration data from smartphones. Appl. Sci., 7.","DOI":"10.20944\/preprints201706.0033.v1"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ghayvat, H., Pandya, S.N., Bhattacharya, P., Zuhair, M., Rashid, M., Hakak, S., and Dev, K. (2021). CP-BDHCA: Blockchain-based Confidentiality-Privacy preserving Big Data scheme for healthcare clouds and applications. IEEE J. Biomed. Health Inform.","DOI":"10.1109\/JBHI.2021.3097237"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ghayvat, H., Awais, M., Pandya, S., Ren, H., Akbarzadeh, S., Chandra Mukhopadhyay, S., Chen, C., Gope, P., Chouhan, A., and Chen, W. (2019). Smart aging system: Uncovering the hidden wellness parameter for well-being monitoring and anomaly detection. Sensors, 19.","DOI":"10.3390\/s19040766"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tahir, B., Jolfaei, A., and Tariq, M. (2021). Experience Driven Attack Design and Federated Learning Based Intrusion Detection in Industry 4.0. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2021.3133384"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"102355","DOI":"10.1016\/j.cose.2021.102355","article-title":"Integration of Blockchain and Federated Learning for Internet of Things: Recent Advances and Future Challenges","volume":"108","author":"Ali","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Prathiba, S.B., Raja, G., Anbalagan, S., Dev, K., Gurumoorthy, S., and Sankaran, A.P. (2021). Federated Learning Empowered Computation Offloading and Resource Management in 6G-V2X. IEEE Trans. Netw. Sci. Eng.","DOI":"10.1109\/TNSE.2021.3103124"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Prathiba, S., Raja, G., Anbalagan, S., Gurumoorthy, S., Kumar, N., and Guizani, M. (2021). Cybertwin-Driven Federated Learning Based Personalized Service Provision for 6G-V2X. IEEE Trans. Veh. Technol., 1.","DOI":"10.1109\/TVT.2021.3133291"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13340","DOI":"10.1109\/TVT.2021.3122257","article-title":"A Hybrid Deep Reinforcement Learning For Autonomous Vehicles Smart-Platooning","volume":"70","author":"Prathiba","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"3501","DOI":"10.1109\/TII.2021.3119038","article-title":"Federated learning for cybersecurity: Concepts, challenges and future directions","volume":"18","author":"Alazab","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_27","unstructured":"Wang, W., Fida, M.H., Lian, Z., Yin, Z., Pham, Q.V., Gadekallu, T.R., Dev, K., and Su, C. (2021). Secure-enhanced federated learning for ai-empowered electric vehicle energy prediction. IEEE Consum. Electron. Mag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1016\/j.future.2020.10.007","article-title":"A survey on security and privacy of federated learning","volume":"115","author":"Mothukuri","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5844728","DOI":"10.1155\/2021\/5844728","article-title":"Temporal Weighted Averaging for Asynchronous Federated Intrusion Detection Systems","volume":"2021","author":"Agrawal","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"7156420","DOI":"10.1155\/2021\/7156420","article-title":"Genetic CFL: Hyperparameter Optimization in Clustered Federated Learning","volume":"2021","author":"Agrawal","year":"2021","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_31","first-page":"102748","article-title":"Agent architecture of an intelligent medical system based on federated learning and blockchain technology","volume":"58","author":"Srivastava","year":"2021","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_32","unstructured":"Gadekallu, T.R., Pham, Q.V., Huynh-The, T., Bhattacharya, S., Maddikunta, P.K.R., and Liyanage, M. (2021). Federated Learning for Big Data: A Survey on Opportunities, Applications, and Future Directions. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"103663","DOI":"10.1016\/j.scs.2021.103663","article-title":"Federated Learning enabled Digital Twins for smart cities: Concepts, recent advances, and future directions","volume":"79","author":"Ramu","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.jpdc.2018.08.010","article-title":"A wearable sensor-based activity prediction system to facilitate edge computing in smart healthcare system","volume":"123","author":"Uddin","year":"2019","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"101824","DOI":"10.1016\/j.artmed.2020.101824","article-title":"Wearable sensor-based evaluation of psychosocial stress in patients with metabolic syndrome","volume":"104","author":"Akbulut","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Majumder, S., Mondal, T., and Deen, M.J. (2017). Wearable sensors for remote health monitoring. Sensors, 17.","DOI":"10.3390\/s17010130"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"26311","DOI":"10.1109\/JSEN.2021.3058429","article-title":"Flexible and Wearable EMG and PSD Sensors Enabled Locomotion Mode Recognition for IoHT Based In-home Rehabilitation","volume":"21","author":"Zhao","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kumrai, T., Korpela, J., Maekawa, T., Yu, Y., and Kanai, R. (2020, January 23\u201327). Human activity recognition with deep reinforcement learning using the camera of a mobile robot. Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications (PerCom), Austin, TX, USA.","DOI":"10.1109\/PerCom45495.2020.9127376"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/MIS.2020.2988604","article-title":"Fedhealth: A federated transfer learning framework for wearable healthcare","volume":"35","author":"Chen","year":"2020","journal-title":"IEEE Intell. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"209191","DOI":"10.1109\/ACCESS.2020.3038287","article-title":"EdgeFed: Optimized federated learning based on edge computing","volume":"8","author":"Ye","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sozinov, K., Vlassov, V., and Girdzijauskas, S. (2018, January 11\u201313). Human activity recognition using federated learning. Proceedings of the 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA\/IUCC\/BDCloud\/SocialCom\/SustainCom), Melbourne, VIC, Australia.","DOI":"10.1109\/BDCloud.2018.00164"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Casilari, E., Santoyo-Ram\u00f3n, J.A., and Cano-Garc\u00eda, J.M. (2017). Analysis of public datasets for wearable fall detection systems. Sensors, 17.","DOI":"10.3390\/s17071513"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, F., Shirahama, K., Nisar, M.A., K\u00f6ping, L., and Grzegorzek, M. (2018). Comparison of feature learning methods for human activity recognition using wearable sensors. Sensors, 18.","DOI":"10.3390\/s18020679"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"139336","DOI":"10.1109\/ACCESS.2021.3117336","article-title":"Human Activity Recognition Based on Acceleration Data from Smartphones Using HMMs","volume":"9","author":"Iloga","year":"2021","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1377\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:18:10Z","timestamp":1760134690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/4\/1377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":44,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22041377"],"URL":"https:\/\/doi.org\/10.3390\/s22041377","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,11]]}}}