Measurement of users’ well-being through domotic sensors and machine learning algorithms

This paper proposes a specific domotic sensor network to measure the well-being of elderly people in private home environments through Machine Learning (ML) algorithms trained with daily surveys. The tests have been conducted in 5 apartments lived by 8 older people where the non-obtrusive sensor network is installed. Two ML algorithms are compared, Random Forest (RF) and Regression Tree (RT), such that to verify whether the users’ well-being is encoded in behavioural patterns obtained from the domotic data. These data are used to measure users’ well-being and compared with three reference indices obtained through a daily survey: a physical (Phy), a mental (Mind) and a general health index (Avg). The extracted indices from the daily survey are used to train ML algorithms in the estimation of user’s well-being for users that live alone (single-resident) or with others (multi-resident). Single-house and multi-house procedures are tested, both to extract a user-specific behaviour, and assess whether the model is able to generalise across different users and environments. Results show that the RF algorithm provides better performance than the RT algorithm in predicting the level of well-being with a Mean Absolute Error in the multi-house procedure of 32%, 13% and 17% for the Avg, Mind and Phy indices, respectively.