Apathy Classification Based on Doppler Radar Image for the Elderly Person

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.

Keywords: apathy classification; deep learning; doppler radar image; machine learning; the elderly person.

Figure 1

Doppler radar image and experimental environment. (A) Original image, (B) Walk process, (C) Experimental environment.

Figure 2

Overview of proposed method. (A) Classification flow, (B) Doppler radar image separation, (C) Classification by NN.

Figure 3

Experimental results: using red channel. (A) SVM, (B) KNN, (C) Naive Bayes, (D) decision tree, (E) random forest, (F) neural network, (G) ensemble.

Figure 4

Experimental results: using green channel. (A) SVM, (B) KNN, (C) Naive Bayes, (D) decision tree, (E) random forest, (F) neural network, (G) ensemble.

Figure 5

Experimental results: using blue channel. (A) SVM, (B) KNN, (C) Naive Bayes, (D) decision tree, (E) random forest, (F) neural network, (G) ensemble.

Figure 6

Experimental results: using Y of YUV. (A) SVM, (B) KNN, (C) Naive Bayes, (D) decision tree, (E) random forest, (F) neural network, (G) ensemble.

Figure 7

Channels discussion about NN.

Figure 8

Discussion about Ensemble model.

Figure 9

Discussion on experimental results of NN (validation = 0.2).

Figure 10

Experimental results of the 5 × 5 image separation.

Figure 11

Experimental results of the 4 × 5 image separation.

Figure 12

Experimental results of deep learning models.