Depressed Mood Prediction of Elderly People with a Wearable Band

Depression in the elderly is an important social issue considering the population aging of the world. In particular, elderly living alone who has narrowed social relationship due to bereavement and retirement are more prone to be depressed. Long-term depressed mood can be a precursor to eventual depression as a disease. Our goal is how to predict the depressed mood of single household elderly from unobtrusive monitoring of their daily life. We have selected a wearable band with multiple sensors for monitoring elderly people. Depression questionnaire has been surveyed periodically to be used as the labels. Instead of working with depression patients, we recruited 14 single household elderly people from a nearby community. The wearable band provided daily activity and biometric data for 71 days. From the data, we generate a depressed mood prediction model. Multiple features from the collected sensor data are exploited for model generation. One general model is generated to be used as the baseline for the initial model deployment. Personal models are also generated for model refinement. The general model has a high recall of 80% in an MLP model. Individual models achieved an average recall of 82.7%. In this study, we have demonstrated that we can generate depressed mood prediction models with data collected from real daily living. Our work has shown the feasibility of using a wearable band as an unobtrusive depression monitoring sensor even for elderly people.

Automated summary

Depression in the elderly is a significant social issue related to the aging population. Elderly individuals who live alone and have limited social connections due to bereavement and retirement are more susceptible to depression. The study aims to predict the depressed mood of single household elderly people through unobtrusive monitoring of their daily life. A wearable band with multiple sensors was utilized to monitor the elderly participants, and depression questionnaires were used as labels. The study demonstrated the feasibility of generating depressed mood prediction models using data collected from real daily living, even for elderly individuals.