<?xml version="1.0" encoding="UTF-8"?><doi_batch version="4.3.7" xmlns="http://www.crossref.org/schema/4.3.7" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.3.7 http://www.crossref.org/schema/deposit/crossref4.3.7.xsd">
		<head>
		<doi_batch_id>cirpublications.com-6Tq5-1790874882-s417811021</doi_batch_id>
		<timestamp>1790874882</timestamp>
		<depositor>
			<depositor_name>Clinical Intelligence Research Press</depositor_name>
			<email_address>info@cirpublications.com</email_address>
		</depositor>
		<registrant>Clinical Intelligence Research Press</registrant>
	</head>
	<body>
		<journal>
			<journal_metadata>
				<full_title>Journal of Artificial Intelligence for Healthcare Systems</full_title>
				<abbrev_title>J. Artif. Intell. Healthc. Syst.</abbrev_title>
				<issn>3149-8981</issn>
			</journal_metadata>
			<journal_issue>
				<publication_date>
					<year>2026</year>
				</publication_date>
				<journal_volume>
					<volume>5</volume>
				</journal_volume>
				<issue>2</issue>
			</journal_issue>
			<journal_article publication_type="full_text">
				<titles>
					<title>Federated Continual Learning Framework for Adaptive Maintenance of Fall Risk Prediction Models across Aging Populations Using Wearable Accelerometer Data from Smartwatches</title>
				</titles>
								<contributors>
          					<person_name sequence="first" contributor_role="author">
            <given_name>Igor</given_name>
            <surname>Petrov</surname>
					</person_name>
          					<person_name sequence="additional" contributor_role="author">
            <given_name>Alexei</given_name>
            <surname>Smirnov</surname>
					</person_name>
          				</contributors>
								<publication_date>
					<year>2026</year>
				</publication_date>
				<doi_data>
					<doi>10.68159/s417811021</doi>
					<resource>https://cirpublications.com/pub/journal/1/article/s417811021</resource>
				</doi_data>
				<citation_list>
          					<citation key="rk-10.68159/s417811021-874b650c-684a-4add-b65f-d72e8d8510e4">
					  <unstructured_citation>Howcroft J, Kofman J, Lemaire ED. Prospective fall-risk prediction models for older adults based on wearable sensors. IEEE Trans Neural Syst Rehabil Eng. 2017;25(10):1812-20.</unstructured_citation>
						 <doi>10.1109/TNSRE.2016.2639794</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-5e4923f0-04a5-40ef-b105-5c3a7b06ae36">
					  <unstructured_citation>Howcroft J, Kofman J, Lemaire ED. Feature selection for elderly faller classification based on wearable sensors. J Neuroeng Rehabil. 2017;14(1):47.</unstructured_citation>
						 <doi>10.1186/s12984-017-0255-9</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-c29b2862-6fd3-4d9c-b781-d4e17b66cdf4">
					  <unstructured_citation>Bet P, Castro PC, Ponti MA. Fall detection and fall risk assessment in older person using wearable sensors: a systematic review. Int J Med Inform. 2019;130:103946.</unstructured_citation>
						 <doi>10.1016/j.ijmedinf.2019.103946</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-c6149a41-e229-4e9d-bc2d-c0d294d15399">
					  <unstructured_citation>Tunca C, Salur G, Ersoy C. Deep learning for fall risk assessment with inertial sensors: utilizing domain knowledge in spatio-temporal gait parameters. IEEE J Biomed Health Inform. 2020;24(7):1994-2005.</unstructured_citation>
						 <doi>10.1109/JBHI.2019.2948879</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-8cb3787e-d8c4-4196-bef2-79b8b89debd6">
					  <unstructured_citation>Wang B, Liu Y, Lu A, Wang C. Application of wearable sensors in constructing a fall risk prediction model for community-dwelling older adults: a scoping review. Arch Gerontol Geriatr. 2025;129:105689.</unstructured_citation>
						 <doi>10.1016/j.archger.2024.105689</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-d99416f1-2f9f-443c-847d-b3de867b1504">
					  <unstructured_citation>Chen Y, Qin X, Wang J, Yu C, Gao W. FedHealth: a federated transfer learning framework for wearable healthcare. IEEE Intell Syst. 2020;35(4):83-93.</unstructured_citation>
						 <doi>10.1109/MIS.2020.2988604</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-fe91ee72-4e69-460b-a229-b77c4ee9274f">
					  <unstructured_citation>Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119.</unstructured_citation>
						 <doi>10.1038/s41746-020-00323-1</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-3f5d50c6-8b17-4a95-a646-e5dfe7df728f">
					  <unstructured_citation>Lee CS, Lee AY. Clinical applications of continual learning machine learning. Lancet Digit Health. 2020;2(6):e279-e281.</unstructured_citation>
						 <doi>10.1016/S2589-7500(20)30102-3</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-35064be1-6f13-4b7a-b6d1-74ad074126f0">
					  <unstructured_citation>Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, et al. Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare. NPJ Digit Med. 2022;5(1):66.</unstructured_citation>
						 <doi>10.1038/s41746-022-00584-6</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-92ba96f7-8da6-43fc-8f91-8d85553e9a0d">
					  <unstructured_citation>Criado MF, Casado FE, Iglesias R, Regueiro CV, Barro S. Non-IID data and continual learning processes in federated learning: a long road ahead. Inf Fusion. 2022;88:263-80.</unstructured_citation>
						 <doi>10.1016/j.inffus.2022.07.003</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-60190962-60b2-4612-87da-2fb70b87e9b9">
					  <unstructured_citation>Wu Q, Chen X, Zhou Z, Zhang J. FedHome: cloud-edge based personalized federated learning for in-home health monitoring. IEEE Trans Mob Comput. 2022;21(8):2818-32.</unstructured_citation>
						 <doi>10.1109/TMC.2020.3047205</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-3d315c93-68fd-475d-ba9e-2e1df1ac1675">
					  <unstructured_citation>Pfitzner B, Steckhan N, Arnrich B. Federated learning in a medical context: a systematic literature review. ACM Trans Internet Technol. 2021;21(2):1-31.</unstructured_citation>
						 <doi>10.1145/3418292</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-b4738d79-8330-4452-9ed4-b3fa5d7951d7">
					  <unstructured_citation>Lien WC, Ching CT, Lai ZW, Wang HM, Lin JS, Huang YC, et al. Intelligent fall-risk assessment based on gait stability and symmetry among older adults using tri-axial accelerometry. Front Bioeng Biotechnol. 2022;10:887269.</unstructured_citation>
						 <doi>10.3389/fbioe.2022.887269</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-c1675bf6-44a7-4757-91b2-92959084290b">
					  <unstructured_citation>González-Castro A, Benítez-Andrades JA, González-González R, Prada-García C, Leirós-Rodríguez R. Predicting fall risk in older adults: a machine learning comparison of accelerometric and non-accelerometric factors. Digit Health. 2025;11:20552076251331752.</unstructured_citation>
						 <doi>10.1177/20552076251331752</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-3d183b06-6dda-45b8-b500-2b461b069254">
					  <unstructured_citation>Wang T, Du Y, Gong Y, Choo KKR, Guo Y. Applications of federated learning in mobile health: scoping review. J Med Internet Res. 2023;25:e43006.</unstructured_citation>
						 <doi>10.2196/43006</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-d377ba32-b64a-421b-b497-4d2567a12d26">
					  <unstructured_citation>Pati S, Baid U, Edwards B, Sheller M, Wang SH, Reina GA, et al. Federated learning enables big data for rare cancer boundary detection. Nat Commun. 2022;13(1):7346.</unstructured_citation>
						 <doi>10.1038/s41467-022-33407-5</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-1625cc91-bc58-44bb-b074-4efa57009d33">
					  <unstructured_citation>Bruno P, Quarta A, Calimeri F. Continual learning in medicine: a systematic literature review. Neural Process Lett. 2025;57(1):2.</unstructured_citation>
						 <doi>10.1007/s11063-024-11634-4</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-5f92c80a-a73b-43c5-a289-efc39ddb223a">
					  <unstructured_citation>Wu X, Xu Z, Tong RK. Continual learning in medical image analysis: a survey. Comput Biol Med. 2024;182:109206.</unstructured_citation>
						 <doi>10.1016/j.compbiomed.2024.109206</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-a9e0ee00-45a2-4c66-b69b-a2e0b0d3576c">
					  <unstructured_citation>Yu H, Chen Z, Zhang X, Chen X, Zhuang F, Xiong H, et al. FedHAR: semi-supervised online learning for personalized federated human activity recognition. IEEE Trans Mob Comput. 2023;22(6):3318-32.</unstructured_citation>
						 <doi>10.1109/TMC.2021.3131778</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-8dc8f6e2-3fd0-408f-aa7d-7e71c4e21788">
					  <unstructured_citation>Presotto R, Civitarese G, Bettini C. Semi-supervised and personalized federated activity recognition based on active learning and label propagation. Pers Ubiquit Comput. 2022;26(5):1281-98.</unstructured_citation>
						 <doi>10.1007/s00779-021-01574-3</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-64b3f2b7-8e9f-497a-8e48-e90cc1e6c2d5">
					  <unstructured_citation>Cheng D, Zhang L, Bu C, Wang X, Wu H, Song A. ProtoHAR: prototype guided personalized federated learning for human activity recognition. IEEE J Biomed Health Inform. 2023;27(8):3900-11.</unstructured_citation>
						 <doi>10.1109/JBHI.2023.3268172</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-cea7c4f4-8ea1-4a1c-94e2-7b7876b54356">
					  <unstructured_citation>Chai Y, Liu H, Zhu H, Pan Y, Zhou A, Liu H, et al. A profile similarity-based personalized federated learning method for wearable sensor-based human activity recognition. Inf Manage. 2024;61(7):103922.</unstructured_citation>
						 <doi>10.1016/j.im.2024.103922</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-be0d4eb8-c40b-4ff0-a8eb-0cc796da5648">
					  <unstructured_citation>Yu Z, Liu J, Yang M, Cheng Y, Hu J, Li X. An elderly fall detection method based on federated learning and extreme learning machine (Fed-ELM). IEEE Access. 2022;10:130816-24.</unstructured_citation>
						 <doi>10.1109/ACCESS.2022.3227088</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-6d9125ad-9285-41e0-a636-fce208b1ea38">
					  <unstructured_citation>Ghosh S, Ghosh SK. FEEL: federated learning framework for elderly healthcare using edge-IoMT. IEEE Trans Comput Soc Syst. 2023;10(4):1800-9.</unstructured_citation>
						 <doi>10.1109/TCSS.2022.3211504</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-689cc5da-65f3-404a-b828-9b283ccec15d">
					  <unstructured_citation>Qi P, Chiaro D, Piccialli F. FL-FD: federated learning-based fall detection with multimodal data fusion. Inf Fusion. 2023;99:101890.</unstructured_citation>
						 <doi>10.1016/j.inffus.2023.101890</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-56ed3af7-83f4-4a22-b187-7a8c4fecc807">
					  <unstructured_citation>Aminifar A, Shokri M, Aminifar A. Privacy-preserving edge federated learning for intelligent mobile-health systems. Future Gener Comput Syst. 2024;161:625-37.</unstructured_citation>
						 <doi>10.1016/j.future.2024.07.019</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-74f073a7-61ac-4071-b8d1-86f2e471504f">
					  <unstructured_citation>Xiao Z, Xu X, Xing H, Song F, Wang X, Zhao B. A federated learning system with enhanced feature extraction for human activity recognition. Knowl Based Syst. 2021;229:107338.</unstructured_citation>
						 <doi>10.1016/j.knosys.2021.107338</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-43853ca5-2ade-4f1c-a9dd-35af8ba5da02">
					  <unstructured_citation>Ouyang X, Xie Z, Zhou J, Huang J, Xing G. ClusterFL: a similarity-aware federated learning system for human activity recognition. In: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys). 2021. p. 54-66.</unstructured_citation>
						 <doi>10.1145/3458864.3466628</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-f615d875-564c-426f-b918-b83feae338b1">
					  <unstructured_citation>Tu L, Ouyang X, Zhou J, He Y, Xing G. FedDL: federated learning via dynamic layer sharing for human activity recognition. In: Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems (SenSys). 2021. p. 15-28.</unstructured_citation>
						 <doi>10.1145/3485730.3485946</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-9678b3c8-ad97-4fb7-99cd-3ede84f35166">
					  <unstructured_citation>Kumari P, Chauhan J, Bozorgpour A, Huang B, Azad R, Merhof D. Continual learning in medical image analysis: a comprehensive review of recent advancements and future prospects. Med Image Anal. 2025;103730.</unstructured_citation>
						 <doi>10.1016/j.media.2025.103730</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-1f28c154-695f-479f-ae48-74a01c8e7076">
					  <unstructured_citation>Qazi MA, Hashmi AU, Sanjeev S, Almakky I, Saeed N, Gonzalez C, et al. Continual learning in medical imaging: a survey and practical analysis. ACM Comput Surv. 2026;58(8):1-25.</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s417811021-a8818004-9f8f-43fb-a6f1-5e0d27590904">
					  <unstructured_citation>Verma T, Jin L, Zhou J, Huang J, Tan M, Choong BC, et al. Privacy-preserving continual learning methods for medical image classification: a comparative analysis. Front Med (Lausanne). 2023;10:1227515.</unstructured_citation>
						 <doi>10.3389/fmed.2023.1227515</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-458ef5e0-3a49-4316-88da-94086709f88e">
					  <unstructured_citation>Jagdeesh K, Kanimozhi N, Sardar TH, Naveenkumar N, Mahalakshmi B, Chandrasekar A, et al. Federated learning with continual update for privacy-preserving clinical event prediction across distributed hospitals using MCN-GNN. Sci Rep. 2026;13(1).</unstructured_citation>
											</citation>
          					<citation key="rk-10.68159/s417811021-b4aa3efa-6d5d-4fb7-ab21-3ba21613bbaf">
					  <unstructured_citation>Zhao X, Liu Z, Ji B, Xi P, Peng S. TransEHR: alignment-free electronic health records continual learning across feature spaces. Expert Syst Appl. 2025;129020.</unstructured_citation>
						 <doi>10.1016/j.eswa.2025.129020</doi> 					</citation>
          					<citation key="rk-10.68159/s417811021-7c04d460-c6e6-40a5-93e5-0c8c126589d0">
					  <unstructured_citation>Haescher M, Chodan W, Höpfner F, Bieber G, Aehnelt M, Srinivasan K, et al. Automated fall risk assessment of elderly using wearable devices. J Rehabil Assist Technol Eng. 2020;7:2055668320946209.</unstructured_citation>
						 <doi>10.1177/2055668320946209</doi> 					</citation>
          				</citation_list>
			</journal_article>
		</journal>
	</body>
</doi_batch>
