An architectural framework of elderly healthcare monitoring and tracking through wearable sensor technologies

Author:

Alsadoon AbeerORCID,Al-Naymat Ghazi,Jerew Oday D.

Abstract

AbstractThe growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.

Funder

Charles Sturt University

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Reference104 articles.

1. Zang W, Miao F, Gravina R, Sun F, Fortino G, Li Y (2019) CMDP-based intelligent transmission for wireless body area network in remote health monitoring. Neural Comput Appl 10(2):1–9

2. Yang X, Tian Y (2017) Super normal vector for human activity recognition with depth cameras. IEEE Trans Pattern Anal Mach Intell 39(5):1028–1039

3. Yang X, Shah SA, Ren A, Fan D, Zhao N, Zheng S, Zhao W, Wang W, Soh PJ, Abbasi QH (2018) S-band sensing-based motion assessment framework for cerebellar dysfunction patients. IEEE Sens J 7(1):1–15

4. Yacchirema D, de Puga JS, Palau C, Esteve M (2019) Fall detection system for elderly people using IoT and ensemble machine learning algorithm. Pers Ubiquit Comput 10(5):1–17

5. Yacchirema DC, Sarabia-Jácome D, Palau CE, Esteve M (2018) A smart system for sleep monitoring by integrating IoT with big data analytics. IEEE Access 6(2):35988–36001

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