Activity Recognition over Temporal Distance using Supervised Learning in the Context of Dementia Diagnostics

Abstract

In the case of neurological diseases, the progression of the disease can be detected by monitoring movements and activities. Documenting such monitoring requires time-consuming work, which can hardly be covered in the context of a constantly decreasing availability of nursing staff. In cooperation with two dementia residential communities, we incrementally develop a process that supports the nursing staff by providing an approach for a semiautomated documentation. This paper presents an approach to aggregate individual activities over a care period using smartwatches in combination with supervised learning algorithms. A smartwatch offers the opportunity to integrate sensor technology into a patient’s daily routine without disturbing them, as many patients already wear watches. We are investigating promising combinations of sensor technologies and supervised learning algorithms, collecting data from the accelerometer, heart rate sensor, gyroscope, gravity and position sensor at 20 Hz and sending it to a web server. The activities are then classified multiple times using Fast Forest, Logistic Regression and Support Vector Machines over a maintenance layer. We present an activity classification prototype over time distance for automated activity recognition, which, after a number of classifications and the likelihood of these, suggests to the nurse a statement of activities over the respective time period of a nursing shift, in the form of a completed documentation. In addition, the work provides an interpretation of how the knowledge gained can be used to recognise motor skills in the course of caring for patients with neurological diseases.