Automated documentation of almost identical movements in the context of dementia diagnostics

When monitoring the health state of people with neurological diseases, it is crucial to detect disease progression and disease-related changes as soon as possible. Such changes can be detected in the performance of everyday activities like eating, drinking, nose blowing, reading, looking at photos, knitting, telephoning, or brushing hair.

In order to supervise such everyday activities, this work proposes an approach employing smartwatches in combination with a Recurrent neural network. A smartwatch offers the possibility of integrating sensor technology into a patient’s daily routines in a unobtrusive way.

We have developed a machine learning web tool to track motion data using smartwatch sensors. Data from the accelerometer, heart rate sensor, gyroscope, gravity sensor and position sensor are tracked at 20 Hz. Our tool allows a systematic comparison of the performance between a Long Short-Term Memory (LSTM) model and the different sensor systems in classifying twelve nearly identical daily activities.

In this paper, we address the problem of caregiver effort in creating daily care documentation for dementia patients. We present an activity classification system that enables automatic classification of daily activities of patients with dementia across a nursing shift. To assist caregivers, we provide an idea for integrating our system into a nursing documentation.