A smart e-health framework for monitoring the health of the elderly and disabled

Date

2023-12-01

Author

Yazıcı, Adnan
Zhumabekova, Dana
Nurakhmetova, Aidana
Yergaliyev, Zhanggir
Yatbaz, Hakan Yekta
Makisheva, Zaida
Lewis, Michael
EVER, ENVER

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The healthcare sector is experiencing a significant transformation due to the widespread adoption of IoT-based systems, especially in the care of elderly and disabled individuals who can now be monitored through portable and wearable devices. The widespread implementation of IoT-based systems in the healthcare sector holds immense potential for improving care and ensuring the well-being of vulnerable populations. In our paper, we introduce an e-health framework that utilizes real-time data from inertial, ECG, and video sensors to monitor the health and activities of these individuals. Our framework employs edge computing for efficient analysis and generates notifications based on data analysis while prioritizing privacy by activating multimedia sensors only when necessary. To achieve accurate results, we evaluated each component of our framework separately. Using the MHEALTH dataset, our proposed machine learning model achieves accuracies of 99.97% for inertial sensors’ human activity recognition (HAR) and 99.14% for ECG sensors. We also evaluated our first-level inconsistency detection-based anomaly proposal mechanism using one user's data, demonstrating its proof-of-concept. Furthermore, our video-based HAR and fall detection module achieve an accuracy of 86.97% on the well-known DMLSmartActions dataset. We successfully deployed our proposed HAR model with inertial sensors in a controlled experimental environment, yielding promising results with an accuracy of 96% and an F1 score of 90.52%. These evaluations validate the effectiveness of our framework in monitoring the health and activities of elderly and disabled individuals.

Subject Keywords

Activity recognition, e-health, Emergency detection, IoT

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85176602085&origin=inward
https://hdl.handle.net/11511/107124

Collections

Department of Computer Engineering, Article