Elderly Healthcare Using Federated Learning Approach

Year : 2026 | Volume : 16 | Issue : 01 | Page : 13 23
    By

    Arul Antran Vijay S.,

  • Jebson J.,

  • Subairkhan A.,

  • Vasanthakumar R.,

  • Vinith Kumar V.,

  1. Associate Professor, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  2. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  3. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  4. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India
  5. Student, Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu, India

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: Long short-term memory, recurrent neural network, convolutional neural network, deep reinforcement learning, WESAD, UCI HAR, MIMIC III, Internet of Medical Things

[This article belongs to Current Trends in Information Technology ]

How to cite this article:
Arul Antran Vijay S., Jebson J., Subairkhan A., Vasanthakumar R., Vinith Kumar V.. Elderly Healthcare Using Federated Learning Approach. Current Trends in Information Technology. 2026; 16(01):13-23.
How to cite this URL:
Arul Antran Vijay S., Jebson J., Subairkhan A., Vasanthakumar R., Vinith Kumar V.. Elderly Healthcare Using Federated Learning Approach. Current Trends in Information Technology. 2026; 16(01):13-23. Available from: https://journals.stmjournals.com/ctit/article=2026/view=236619


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Regular Issue Subscription Original Research
Volume 16
Issue 01
Received 10/03/2025
Accepted 08/10/2025
Published 07/02/2026
Publication Time 334 Days
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