Apathy Classification Based on Doppler Radar Image for the Elderly Person - Nojiri, Naoto; Meng, Zelin; Saho, Kenshi; Duan, Yucong; Uemura, Kazuki; Aravinda, C. V.; Prabhu, G. Amar; Shimakawa, Hiromitsu; Meng, Lin (Frontiers in Bioengineering and Biotechnology, 2020)
Apathy is a disease, including diminished motivation not attributable to diminished level of consciousness, cognitive impairment, or emotional distress. Also, Apathy is a serial problem in the elderly society. For Apathy diagnosis, the elderly person should go to clinic which is not convenient and especially hard to the elderly. This paper want to prove the possibility of using Doppler radar image for Apathy classification for the elderly. In this research, we ask for the help of 178 the elderly for creating dataset by questionnaire and taking Walking action by Doppler radar. The reason of selecting the Walking action is it is a most normal action in daily life, and lot of health information are hidden in this action. And radar image is a good method on privacy protection than the RGB camera. Seven machine learning methods, including proposed Neural Network, are used for the Apathy classification by using the walking Doppler radar images of the elderly person. Before classification, simple image pre-processing is proposed for feature extraction. The pre-processing separates the every Walking Doppler radar image into four parts in Vertical axis and Horizontal axis, respectively. Then feature points number are counted in the every separated part after binarization for creating the eight features. In binarization, optimized threshold are obtained by sliding the threshold in experimentation. The experimental results show the proposed Neural Network achieved more 75% in Apathy classification, which is better than others machine learning methods. The accuracy is not so high than the current object classification research. However, as a first research in this area, the possibility of Apathy classification using Doppler radar image for the elderly person is proved. Centrally, the accuracy should be increased as future work.