Monitoring and Prediction of Mood in Elderly People during Daily Life Activities

Daniel Bautista-Salinas; Joaquín Roca González; Inmaculada Méndez; Oscar Martinez Mozos

cs.LGMar 11, 2026

Monitoring and Prediction of Mood in Elderly People during Daily Life Activities

Authors: Daniel Bautista-Salinas, Joaquín Roca González, Inmaculada Méndez, Oscar Martinez Mozos

Organizations: Technical University of Cartagena, 30202 Cartagena, Spain · University of Murcia, 30100 Espinardo, Murcia, Spain

Abstract

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.

Affective Computing

Affective computing develops systems that recognize, model, and respond to human emotions and affective states from behavior and physiological signals.

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Flavio Di Martino, Mattia G. Campana, Marcello Magno, Lorenza Pratali+1

IIT-CNR, Pisa, Italy · IFC-CNR, Pisa, Italy

Clinical Outcome Prediction Wearable Sensing Clinical Prediction Wearable Health Monitoring Multimodal Sensor Fusion

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Wearable and mobile sensing technologies enable continuous monitoring of human behavior and health in real-world settings. However, predictive modeling in longitudinal multimodal data remains challenging, particularly when targeting complex or clinically derived outcomes. In this work, we present a longitudinal multimodal study of 66 older adults conducted in real-world conditions and combining wearable sensing, behavioral monitoring, and clinical assessments. This setting provides a rare opportunity to study an underrepresented population in long-term, into-the-wild conditions. Building on this dataset, we investigate how the alignment between sensed signals and target variables affects predictive performance across health-related tasks. We design a unified evaluation framework spanning tasks with increasing levels of observability, including Activity Levels prediction, Sleep Duration estimation, and Sleep Apnea Severity classification. Our results reveal a clear gradient of predictability: highly observable behavioral targets achieve robust performance (macro-F1 65%), while more abstract outcomes remain challenging despite consistent improvements over baseline models. Moreover, through explainability analysis, we show that historical features consistently emerge as the most informative predictors, highlighting the central role of longitudinal information.

Abstract

Affective Computing

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