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

arXiv cs.LG / 3/13/2026

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Key Points

  • The paper proposes an intelligent wearable system consisting of a wristband to record physiological signals and a mobile app for ecological momentary assessment to monitor and predict mood states in elderly individuals during daily activities.
  • It trains a classifier using machine learning to predict different mood states based solely on data from the smart band, achieving accuracy comparable to state-of-the-art in detecting happiness and activeness.
  • The approach enables mood monitoring in real-life settings, potentially supporting personalized care and well-being for elderly users.
  • The results are described as promising, indicating feasibility of mood prediction from wearable sensors outside clinical contexts.

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.

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