Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
🤖 AI Summary
This study addresses the challenge of real-time monitoring of emotional states in older adults by developing an emotion prediction system based on smart wearable devices. The system captures physiological signals via a wristband and integrates ecological momentary assessment (EMA) data collected through smartphones to train machine learning models capable of automatically and accurately identifying key emotional states—such as happiness and activeness—using only wearable-derived inputs. Experimental results demonstrate that the proposed approach achieves state-of-the-art accuracy on critical affective dimensions, substantially reducing reliance on subjective self-reports. This work thus offers a practical and unobtrusive technological pathway for continuous, passive emotional monitoring in aging populations.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
mood monitoring
elderly people
daily life activities
emotion prediction
wearable system
Innovation
Methods, ideas, or system contributions that make the work stand out.
wearable system
mood prediction
machine learning
ecological momentary assessment
elderly monitoring
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