Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-oneperson-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features. Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring. These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.
Background Evidence about the impact of the COVID-19 pandemic on existing health inequalities is emerging. This study explored differences in mental health, sense of coherence (SOC), sense of community coherence (SOCC), sense of national coherence (SONC), and social support between low and high socioeconomic (SES) groups, and the predictive value of these predictors for mental health. participants and procedure A cross-sectional study was conducted using an online survey in the Netherlands in October 2021, comprising a total of 91 respondents (n = 41, low SES; n = 50, high SES). results There were no differences in mental health, SOC, SOCC, SONC, and social support between the groups. SOC was a predictor for mental health in both groups and SOCC for the low SES group. conclusions We found that both SOC and SOCC predict mental health during the pandemic. In the article we reflect on possible pathways for strengthening these resources for mental health.
