SmartLife Guardian: An AI-Driven Multimodal Health Monitoring System for Elderly Care

DOI

10.2991/978-94-6239-713-2_6How to use a DOI?

Keywords

Edge AI; TinyML; IoT-based elderly monitoring; Wearable health-care systems; Embedded sensors; Medical Reminder System; Real-time health prediction; Intelligent alerting

Abstract

Many elderly people require constant monitoring of their vital signs. Every day, going to hospitals for checkups or being admitted for monitoring of vital signs is tiring; therefore, there is a high demand for smart health monitoring systems. To support elderly healthcare, this paper introduces the SmartLife Guardian, a glove-based wearable system that helps monitor vital signs. This glove integrates multiple sensors, Edge AI (artificial intelligence that processes data on edge devices), and cloud-assisted monitoring. Heart rate, SpO₂, body temperature, respiration, and activity patterns are vital parameters monitored by this wearable system. Tiny ML models are integrated with the hardware that helps in spotting odd patterns of the vital signs and customized health assessments. TinyML enables low-latency and also brings data privacy into play, as evidenced by an alert response time of less than three seconds in our experiments, and it reduces network dependency since all necessary pathology analysis was performed on the ESP32 microcontroller, without the cloud being present for inference. The overview of the health insights and alerts is sent to the cloud so that the caregiver and family members can access and visualize the patient records. It employs a voice assistant to remind the patient to take medication, drink water or rest if they show early signs of instability in vital signs. The proposed system has been tested against experimental dataset, and the obtained results indicate that our system was able to achieve 87%-93% accuracy for vital sign monitoring, a TinyML anomaly classification success rate of 75–85%, an alert response time in below three seconds and less false alarm rates due to personalized baseline learning approach; thus making the system suitable for prolonged continuous health tracking of aged population.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - G. Hemaa Shri
AU  - A. R. Fouzia Banu
AU  - L. Ganesh Raja
AU  - Divya Muralitharan
PY  - 2026
DA  - 2026/06/25
TI  - SmartLife Guardian: An AI-Driven Multimodal Health Monitoring System for Elderly Care
BT  - Proceedings of the International Conference on Advances in Computing Technology and Artificial Intelligence (COMPUTATIA 2026)
PB  - Atlantis Press
SP  - 85
EP  - 99
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6239-713-2_6
DO  - 10.2991/978-94-6239-713-2_6
ID  - Shri2026
ER  -