Detecting leisure activities with dense motif discovery
Eugen Berlin, Kristof Van Laerhoven · 2012
This paper proposes an activity inference system that has been designed for deployment in mood disorder research, which aims at accurately and efficiently recognizing selected leisure activities in week-long continuous data. The approach to achieve this relies on an unobtrusive and wrist-worn data logger, in combination with a custom data mining tool that performs early data abstraction and dense motif discovery to collect evidence for activities. After presenting the system design, a feasibility study on weeks of continuous inertial data from 6 participants investigates both accuracy and execution speed of each of the abstraction and detection steps. Results show that our method is able to detect target activities in a large data set with a comparable precision and recall to more conventional approaches, in approximately the time it takes to download and visualize the logs from the sensor.