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Main Authors: Bautista-Salinas, Daniel, González, Joaquín Roca, Méndez, Inmaculada, Mozos, Oscar Martinez
Format: Preprint
Published: 2026
Subjects:
Online Access:https://arxiv.org/abs/2603.11230
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author Bautista-Salinas, Daniel
González, Joaquín Roca
Méndez, Inmaculada
Mozos, Oscar Martinez
author_facet Bautista-Salinas, Daniel
González, Joaquín Roca
Méndez, Inmaculada
Mozos, Oscar Martinez
contents We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app for ecological momentary assessment (EMA). Machine learning is used to train a classifier to automatically predict different mood states based on the smart band only. Our approach shows promising results on mood accuracy and provides results comparable with the state of the art in the specific detection of happiness and activeness.
format Preprint
id arxiv_https___arxiv_org_abs_2603_11230
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
Bautista-Salinas, Daniel
González, Joaquín Roca
Méndez, Inmaculada
Mozos, Oscar Martinez
Machine Learning
Signal Processing
We present an intelligent wearable system to monitor and predict mood states of elderly people during their daily life activities. Our system is composed of a wristband to record different physiological activities together with a mobile app for ecological momentary assessment (EMA). Machine learning is used to train a classifier to automatically predict different mood states based on the smart band only. Our approach shows promising results on mood accuracy and provides results comparable with the state of the art in the specific detection of happiness and activeness.
title Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
topic Machine Learning
Signal Processing
url https://arxiv.org/abs/2603.11230