Prediction accuracy comparison between deep learning and classification algorithms in the context of human activity recognition

Abstract

In this paper, we compare the prediction accuracy of a deep learning model and three classification algorithms on very similar motions in the field of dementia diagnostics.The basic aim is to gain insights into the retrieval, provision and classification of interaction and health data in the course of the disease of dementia patients. This work shows how the smartwatch "Apple Watch Series 7" can be used to record interaction data from dementia patients and recognise corresponding movement sequences.A data transfer platform was developed that enables communication with a watchOS application on the smartwatch via a Node.js WebSocket. This data transfer platform can be used to control smartwatches and retrieve data from sensors in different frequencies (1 to 100 Hz) in real time. The sensor technology used includes accelerometers, position sensors, gyroscopes, magnetometers and heart rate monitors.In this work, the main focus is on the recognition of motion sequences. For this purpose, two different approaches of supervised learning are compared: recurrent neural network versus classification algorithms. The recurrent neural network is a special form of neural network in which neurons of the same layer or different layers are fed back. Through these feedbacks, temporally coded information can be extracted from data. Typical areas of application are handwriting recognition, translations or speech recognition. A recurrent neural network processes data with a memory called Long Short-Term Memory (LSTM). LSTMs represent the state of the art in human activity recognition and are ideal for analysing sequential streams of sensor data. An LSTM is a memory-based, powerful model that can dynamically capture and analyse contextual information whose timing is relevant.This approach of recognising motion sequences is contrasted with the classification algorithms Logistic Regression, Support Vector Machine and Decision Tree. The classification takes place under consideration of features of the respective class. An algorithm tries to work out a dividing line between combinations of features of data and to group them.Records of the activities of dementia patients by the nursing staff from two dementia care communities are available. Consultation with various care teams who work with dementia patients on a daily basis revealed that many patients wear smartwatches. Such watches keep the adjustment effort for sensor positioning low.The records of the activities of dementia patients serve as a template in this work; the movement patterns of the activities eating, drinking and writing are classified. These activities are very similar in their movement patterns, which makes classification challenging.The contribution of this work is the comparison of two possibilities for the recognition of similar movements of patients by means of smartwatches with regard to their correctness. We evaluate our prototype in a test series with five test subjects. In doing so, we demonstrate the accuracy of the memory-based classification network and classification in interaction with the latest wearable sensor technologies and discuss future directions and possibilities in the wearable IoT field of dementia

Keywords: Health Informatics, Human Motion Analysis, Machine Learning

DOI: 10.54941/ahfe1002747