Optimized Vision Transformer for Dementia Diagnosis Using Micro-Doppler Radar

In the aging society, the number of dementia patients continues to increase, and early detection of dementia is required. However, going to a hospital and being diagnosed by a doctor is burdensome for elderly people. This paper designs a Vision Transformer(ViT)-based gait diagnosis with micro-Doppler radar to diagnose dementia without burden for elderly people. The ViT is optimized by proposed Vertical Rectangle Patching and Adaptive Thresholding, which improve the Attention of Vi'I. The micro-Doppler radar collects the signal of elderly people, and the signal is transformed into two kinds of signal-analyzed images by two signal-analysis methods (Short-term Fourier Transform: STFT, and Continuous Wavelet Transform: CWT) for diagnosing by optimized ViI. Experiments compare eight kinds of CNN models, and current ViTs with the optimized ViT to evaluate the proposal's performance. The experimental results show that STFT is suitable for analyzing micro-Doppler radar signals, and ViT-56x4s+th, which uses Vertical Rectangle Patching and Adaptive Thresholding, achieves high scores such as an accuracy of 88.9%. Another proposed model ViT-224xl allows for faster learning convergence for time series data and improved accuracy without fine-tuning. In summary, the possibility of diagnosing dementia by optimized ViT has been provided. The experimentation of Adaptive thresholding also proves there are some unimportant patches and brings an idea for reducing the unimportant computations to achieve a compact ViT as future work.