Recognition Model for Activity Classification in Everyday Movements in the Context of Dementia Diagnostics – Cooking

Abstract

By monitoring movements and activities, the progression of neurological diseases can be detected. The documentation required for this is associated with a high level of effort, which is hardly possible in view of the increasing shortage of nursing staff. In order to gradually relieve the nursing staff, we are developing an approach to automate documentation in cooperation with two dementia residential communities. The aim of this work is to facilitate everyday life of caregivers. Previous research results from this working group show that everyday activities of dementia patients can be recognized well by combining smartwatch sensor technology and machine learning. However, the state of research has gaps when it comes to recognize activities consisting of a variety of movement patterns. In this paper, we present an approach to classify the activity of cooking. We divide this activity into several sub-activities each consisting of a distinct motion pattern that a recurrent network recognizes. This is followed by a model for calculating the probability that cooking actually occurred based on the different sub-activities recognized. We show the advantages of different smartwatch sensor combinations and compare the different approaches of our model with the prediction accuracy of the classification. This model can later be integrated into the care documentation of the residential communities in addition to the activities that are easier to recognize.

Keywords: Human Motion Analysis, Machine Learning, Health Documentation

DOI: 10.54941/ahfe1002859