How Is Grandma Doing? Predicting Functional Health Status from Binary Ambient Sensor Data
Saskia Robben, Gwenn Englebienne, Margriet Pol, Ben Kröse, Cook, Diane J., Krishnan, Narayanan C., Rashidi, Parisa, Skubic, Marjorie, Mihailidis, Alex · UvA-DARE (University of Amsterdam) · 2012
Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring. The prob-lem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of pre-dicting 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 in-dependently living elderly people. Based on expert knowl-edge, 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-one-person-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 au-tomated functional health assessment is possible, but some challenges lie ahead. The most important challenge is elic-iting expert knowledge and translating that into quantifiable features.