Wearable, Ambient and Smart-Home IoT Sensing for Dementia Care in the Elderly A Comparative Review and Integrated Monitoring Framework

This paper presents a comparative review of 15 studies published between 2016 and 2022 on Internet of Things (IoT)-based sensing technologies for dementia care and mild cognitive impairment. The reviewed approaches are categorized into wearable, ambient, and smart-home sensing systems. Their sensing modalities, monitoring capabilities, reported performance, and limitations are examined. Wearable devices capture physiological and movement-related signals, while ambient sensors monitor daily activities and smart-home systems support safety and caregiving. The findings reveal challenges related to small sample sizes, privacy, usability, and inconsistent evaluation methods. Since reported performance metrics vary across studies, direct comparisons are limited. To address these challenges, a conceptual four-layer integrated monitoring framework is proposed. It combines multiple sensing technologies with edge processing, personalized analytics, and caregiver interfaces. The framework highlights opportunities for continuous monitoring and improved dementia care but requires future implementation and clinical validation.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-11-11
DOI
https://doi.org/10.5281/zenodo.24142077
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
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article

Wearable, Ambient and Smart-Home IoT Sensing for Dementia Care in the Elderly A Comparative Review and Integrated Monitoring Framework

JAYANTH KUMAR CHIPPADA
Zenodo (CERN European Organization for Nuclear Research)
Context-Aware Activity Recognition Systems
article

Wearable, Ambient and Smart-Home IoT Sensing for Dementia Care in the Elderly A Comparative Review and Integrated Monitoring Framework

JAYANTH KUMAR CHIPPADA
article en

Abstract

This paper presents a comparative review of 15 studies published between 2016 and 2022 on Internet of Things (IoT)-based sensing technologies for dementia care and mild cognitive impairment. The reviewed approaches are categorized into wearable, ambient, and smart-home sensing systems. Their sensing modalities, monitoring capabilities, reported performance, and limitations are examined. Wearable devices capture physiological and movement-related signals, while ambient sensors monitor daily activities and smart-home systems support safety and caregiving. The findings reveal challenges related to small sample sizes, privacy, usability, and inconsistent evaluation methods. Since reported performance metrics vary across studies, direct comparisons are limited. To address these challenges, a conceptual four-layer integrated monitoring framework is proposed. It combines multiple sensing technologies with edge processing, personalized analytics, and caregiver interfaces. The framework highlights opportunities for continuous monitoring and improved dementia care but requires future implementation and clinical validation.

Zenodo (CERN European Organization for Nuclear Research)
Context-Aware Activity Recognition Systems
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