Authors:
Mariana Carvalho
1
;
Inês Rocha
1
;
Marcelo Arantes
1
;
Ricardo Linhares
1
;
José Soares
1
;
António Moreira
1
;
2
;
João L. Vilaça
1
;
2
;
Demétrio Matos
3
;
Pedro Morais
1
;
2
and
Vítor Carvalho
1
;
2
Affiliations:
1
2Ai−School of Technology, Polytechnic University of Cávado and Ave (IPCA), Campus of IPCA, 4750-810 Barcelos, Portugal
;
2
LASI−Associate Laboratory of Intelligent Systems, 4800-058 Guimarães, Portugal
;
3
Research Institute for Design, Media and Culture (ID+), School of Design, Polytechnic of Cávado and Ave, 4750-810 Barcelos, Portugal
Keyword(s):
Wearable Technology, Activity Recognition, AI, Elderly, Dementia.
Abstract:
Dementia is a progressive neurological condition affecting millions worldwide, posing significant challenges for patients and caregivers. Wearable technologies integrated with artificial intelligence (AI) provide promising solutions for continuous activity monitoring, supporting dementia care. This study evaluates the performance of various AI models, including tree-based methods and deep learning approaches, in recognizing activities relevant to dementia care. While the first excelled in handling class imbalances and recognizing common activities, deep learning models demonstrated superior capabilities in capturing complex temporal and spatial patterns. Additionally, a comprehensive analysis of 30 datasets revealed significant gaps, including limited representation of elderly participants, insufficient activity coverage, short recording durations, and a lack of real-world environmental data. To address these gaps, future work should focus on developing datasets tailored to dementia
care, incorporating long-duration recordings, diverse activities, and realistic contexts. This study highlights the potential of AI-powered wearable systems to transform dementia management, enabling accurate activity recognition, early anomaly detection, and improved quality of life for patients and caregivers.
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