Title: Detecting long-term deviation in Activities of Daily Living based on sensor data
Speaker: Leo Kravtchin
Abstract:

Activities of Daily Living behaviour patterns and daily routines, which include any activities performed on a daily basis, can give many insights into a person’s mental and physical state. This project is based on the already existing CASAS datasets and their developed supervised human activity recognition algorithm to label raw time series sensor data with the performed activity, based on many ambient sensors’ readings in a participant’s home. However, as one might expect, not all activities are classified correctly by the developed algorithm. This talk discusses analysis of the existing data, as well as already conducted and planned experiments to improve the algorithm’s performance by combining hierarchical classification and deep learning techniques. Also, some of the next planned steps are explored, such as the detection of correlations between external weather factors and a person’s daily routine deviations to draw conclusions about the impact of such events, with an emphasis on mobility-related ADL patterns. Examples of this can be wandering around at unusual times or a change in the duration of outside activities, depending on seasonality and weather changes. Finally, the realism of the original aim of this project to detect long-term deviations and health deterioration based on the CASAS data is discussed.