Abstract
Apathy is a disease characterized by diminished motivation not attributable to a diminished level of consciousness, cognitive impairment, or emotional distress. It is a serious problem facing the elderly in today's society. The diagnosis of apathy needs to be done at a clinic, which is particularly inconvenient and difficult for elderly patients. In this work, we examine the possibility of using doppler radar imaging for the classification of apathy in the elderly. We recruited 178 elderly participants to help create a dataset by having them fill out a questionnaire and submit to doppler radar imaging while performing a walking action. We selected walking because it is one of the most common actions in daily life and potentially contains a variety of useful health information. We used radar imaging rather than an RGB camera due to the greater privacy protection it affords. Seven machine learning models, including our proposed one, which uses a neural network, were applied to apathy classification using the walking doppler radar images of the elderly. Before classification, we perform a simple image pre-processing for feature extraction. This pre-processing separates every walking doppler radar image into four parts on the vertical and horizontal axes and the number of feature points is then counted in every separated part after binarization to create eight features. In this binarization, the optimized threshold is obtained by experimentally sliding the threshold. We found that our proposed neural network achieved an accuracy of more than 75% in apathy classification. This accuracy is not as high as that of other object classification methods in current use, but as an initial research in this area, it demonstrates the potential of apathy classification using doppler radar images for the elderly. We will examine ways of increasing the accuracy in future work.
Highlights
Apathy is a disease characterized by diminished motivation not attributable to a diminished level of consciousness, cognitive impairment, or emotional distress (Marin, 1990, 1991; Marin et al, 1991)
We propose image processing and machine learning for apathy classification of the elderly and describe the optimized threshold of binarization, color channel, and machine learning models
This subsection introduces the seven machine-learning models we examined to determine which one was most suitable for apathy classification: a support vector machine (SVM) (Vapnik, 1998), k-nearest neighbor (KNN), naive Bayes, decision tree, random forest, an ensemble model, and our proposed neural network (NN)
Summary
Apathy is a disease characterized by diminished motivation not attributable to a diminished level of consciousness, cognitive impairment, or emotional distress (Marin, 1990, 1991; Marin et al, 1991) It has a relationship with others diseases such as Parkinson’s, Alzheimer’s, and stroke, all of which tend to befall elderly people and threaten their health and well-being (Landes et al, 2001; Fuh et al, 2005; Caeiro et al, 2013; Pagonabarraga et al, 2015). A computer vision system for assistance with apathy diagnosis in remote operation has been developed (Happy et al, 2019), but since it uses images of the patient’s face, problems related to privacy protection arise Another issue is that patients typically need to exhibit subjective symptoms before seeking a doctor, but apathy rarely has subjective symptoms, among the elderly who often live in solitude. Elderly people may delay getting diagnoses and miss out on the best treatment period
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