Monitoring and Prediction of Mood in Elderly People during Daily Life Activities
This addresses mood monitoring for elderly individuals, but it is incremental as it builds on existing methods in the domain.
The researchers tackled the problem of monitoring and predicting mood states in elderly people during daily activities by developing a wearable system with a wristband and mobile app, achieving results comparable to state-of-the-art methods for detecting happiness and activeness.
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.