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

TL;DR

This paper introduces a wearable system that uses physiological data and machine learning to monitor and predict mood states in elderly individuals during daily activities, achieving promising accuracy.

Contribution

It presents a novel integrated wearable and mobile app system for real-time mood monitoring and prediction in elderly people using machine learning techniques.

Findings

Achieved mood prediction accuracy comparable to state-of-the-art methods.

Successfully classified happiness and activeness in elderly users.

Demonstrated feasibility of real-time mood monitoring with wearable devices.

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.