Apathy Classification Based on Doppler Radar Image for the Elderly Person

Naoto Nojiri; Zelin Meng; Kenshi Saho; Yucong Duan; Kazuki Uemura; C V Aravinda; G Amar Prabhu; Hiromitsu Shimakawa; Lin Meng

doi:10.3389/fbioe.2020.553847

Apathy Classification Based on Doppler Radar Image for the Elderly Person

Front Bioeng Biotechnol. 2020 Nov 3:8:553847. doi: 10.3389/fbioe.2020.553847. eCollection 2020.

Authors

Naoto Nojiri¹, Zelin Meng², Kenshi Saho³, Yucong Duan⁴, Kazuki Uemura³, C V Aravinda⁵, G Amar Prabhu⁵, Hiromitsu Shimakawa¹, Lin Meng²

Affiliations

¹ College of Information Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
² College of Science and Engineering, Ritsumeikan University, Kusatsu, Japan.
³ Faculty of Engineering, Toyama Prefectural University, Imizu, Japan.
⁴ Data Science and Technology Department, Hainan University, Haikou, China.
⁵ Department of Computer Science and Engineering, NMAM Institute of Technology, NITTE, Karkala, India.

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.