STM Journals Peer-reviewed research from Consortium e-Learning Network

Current Trends in Information Technology Original Research

Elderly Healthcare Using Federated Learning Approach

  1. Arul Antran Vijay S. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore
  2. Jebson J. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore
  3. Subairkhan A. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore
  4. Vasanthakumar R. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore
  5. Vinith Kumar V. Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore

Abstract

The healthcare system for elderly people faces several challenges, which can be addressed using advanced machine learning models. These models can help monitor chronic diseases, detect falls, and provide personalized health recommendations. The study uses comprehensive datasets like MIMIC-III/IV, WESAD, and UCIHAR to explore human movements, device limitations, and the differences in fall occurrences. A detailed review of existing literature discusses current technologies for activity monitoring and fall detection, focusing on deep learning methods like convolutional neural networks (CNNs) for detecting unusual patterns, recurrent neural networks (RNNs) and long short-term memory (LSTM) architectures are employed to analyze sequential data, while deep reinforcement learning (DRL) is utilized to enhance the personalization of treatment strategies. The approach integrates federated learning to maintain patient data confidentiality, which is crucial in healthcare settings. The effectiveness of the models is assessed using evaluation metrics including accuracy, precision, recall, and F1-score, offering insights into their performance advantages and limitations. A comparison of different models provides valuable insights into their performance and relevance in clinical settings. The findings highlight how these technologies could improve outcomes for patients in critical care, with future research aimed at making the models more accurate and widely applicable for elderly health management.

Keywords

References (23)

  1. Paul A, Nayyar A. A context-sensitive multi-tier deep learning framework for multimodal sentiment analysis. Multimed Tools Appl. 2024;83(18):54249–54278.
  2. V. JP, S. AAV, P. GK, N.K. K. A novel attention-based cross-modal transfer learning framework for predicting cardiovascular disease. Computers in Biology and Medicine. 2024;170:107977. doi:10.1016/j.compbiomed.2024.107977
  3. Maheswari BU, Nithya P, Vijay S, Tamilarasi K, G A, Muthukumaran N. An Automated Technology for IoT based Rail-Track Inspection to Locate Surface Flaws by Robotics and Neural Networks. 2022 International Conference on Inventive Computation Technologies (ICICT). 2022:873-878. doi:10.1109/icict54344.2022.9850453
  4. Subramanian AAV, Venugopal JP. A deep ensemble network model for classifying and predicting breast cancer. Computational Intelligence. 2022;39(2):258-282. doi:10.1111/coin.12563
  5. Vijay SAA, GaneshKumar P. Fuzzy Expert System based on a Novel Hybrid Stem Cell (HSC) Algorithm for Classification of Micro Array Data. Journal of Medical Systems. 2018;42(4). doi:10.1007/s10916-018-0910-0
  6. Saraswat D, Bhattacharya P, Verma A, Prasad VK, Tanwar S, Sharma G, et al. Explainable AI for Healthcare 5.0: Opportunities and Challenges. IEEE Access. 2022;10:84486-84517. doi:10.1109/access.2022.3197671
  7. Qadir QM, Rashid TA, Al-Salihi NK, Ismael B, Kist AA, Zhang Z. Low Power Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability Needs. IEEE Access. 2018;6:77454-77473. doi:10.1109/access.2018.2883151
  8. National University of Sciences and Technology, Islamabad, Pakistan, Joyia GJ, Liaqat RM, Farooq A, Rehman S. Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain. Journal of Communications. 2017. doi:10.12720/jcm.12.4.240-247
  9. Yang L, Yu K, Yang SX, Chakraborty C, Lu Y, Guo T. An Intelligent Trust Cloud Management Method for Secure Clustering in 5G Enabled Internet of Medical Things. IEEE Transactions on Industrial Informatics. 2022;18(12):8864-8875. doi:10.1109/tii.2021.3128954
  10. Das J, Ghosh S, Mukherjee A, Ghosh SK, Buyya R. RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments. Software: Practice and Experience. 2022;52(7):1615-1642. doi:10.1002/spe.3078
  11. soufiene BO, Bahattab AA, Trad A, Youssef H. PEERP: An Priority-Based Energy-Efficient Routing Protocol for Reliable Data Transmission in Healthcare using the IoT. Procedia Computer Science. 2020;175:373-378. doi:10.1016/j.procs.2020.07.053
  12. Mukherjee A, Ghosh S, Behere A, Ghosh SK, Buyya R. Internet of Health Things (IoHT) for personalized health care using integrated edge-fog-cloud network. Journal of Ambient Intelligence and Humanized Computing. 2020;12(1):943-959. doi:10.1007/s12652-020-02113-9
  13. Ghosh S, Mukherjee A, Ghosh SK, Buyya R. STOPPAGE: Spatio‐temporal data driven cloud‐fog‐edge computing framework for pandemic monitoring and management. Software: Practice and Experience. 2022;52(12):2700-2726. doi:10.1002/spe.3144
  14. Rubenstein LZ. Falls in older people: epidemiology, risk factors and strategies for prevention. Age and Ageing. 2006;35(suppl_2):ii37-ii41. doi:10.1093/ageing/afl084
  15. Xu Z, Guo Y, Chakraborty C, Hua Q, Chen S, Yu K. A Simple Federated Learning-Based Scheme for Security Enhancement Over Internet of Medical Things. IEEE Journal of Biomedical and Health Informatics. 2023;27(2):652-663. doi:10.1109/jbhi.2022.3187471
  16. Yuan X, Chen J, Zhang K, Wu Y, Yang T. A Stable AI-Based Binary and Multiple Class Heart Disease Prediction Model for IoMT. IEEE Transactions on Industrial Informatics. 2022;18(3):2032-2040. doi:10.1109/tii.2021.3098306
  17. Chakraborty C, Kishor A. Real-Time Cloud-Based Patient-Centric Monitoring Using Computational Health Systems. IEEE Transactions on Computational Social Systems. 2022;9(6):1613-1623. doi:10.1109/tcss.2022.3170375
  18. Nath RK, Thapliyal H. Smart Wristband-Based Stress Detection Framework for Older Adults With Cortisol as Stress Biomarker. IEEE Transactions on Consumer Electronics. 2021;67(1):30-39. doi:10.1109/tce.2021.3057806
  19. Musci M, De Martini D, Blago N, Facchinetti T, Piastra M. Online Fall Detection Using Recurrent Neural Networks on Smart Wearable Devices. IEEE Transactions on Emerging Topics in Computing. 2021;9(3):1276-1289. doi:10.1109/tetc.2020.3027454
  20. Mauldin T, Ngu AH, Metsis V, Canby ME. Ensemble Deep Learning on Wearables Using Small Datasets. ACM Transactions on Computing for Healthcare. 2020;2(1):1-30. doi:10.1145/3428666
  21. Wu Q, Chen X, Zhou Z, Zhang J. FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring. IEEE Transactions on Mobile Computing. 2022;21(8):2818-2832. doi:10.1109/tmc.2020.3045266
  22. Alzubi JA, Alzubi OA, Singh A, Ramachandran M, Cloud I. IoT-based electronic health record privacy-preserving by CNN and blockchain-enabled federated learning. IEEE Trans Ind Inform. 2023;19(1):1080–1087.
  23. Lian Z, Yang Q, Wang W, Zeng Q, Alazab M, Zhao H, et al. DEEP-FEL: Decentralized, Efficient and Privacy-Enhanced Federated Edge Learning for Healthcare Cyber Physical Systems. IEEE Transactions on Network Science and Engineering. 2022;9(5):3558-3569. doi:10.1109/tnse.2022.3175945
Support