Subtle Motion Detection Using Wi-Fi for Hand Rest Tremor in Parkinson's Disease

Parkinson's disease (PD) affects 1% of the population over the age of 60, and its prevalence increases with age. The disease progresses over time, and the condition can vary significantly in a day, which makes it difficult for precise diagnosis and medication based on short clinical sessions. Therefore, home health monitoring can play an important role in improving the healthcare of the PD patients. In this study, we proposed a method to detect, classify, and quantify daily movements and motor symptoms of PD by using the wireless sensing technology. With the presence of human movements in a space with the Wi-Fi coverage, the channel state information (CSI) of the wireless signal was transformed into images. The images were used to train a deep learning model to distinguish between different daily movements and simulated tremor. The results showed that our method obtained 99.59% and 100% accuracy of recognizing the tremor with modified VGG19 and modified Resnet152, respectively. In addition, the tremor movement was then successfully segmented out and quantified for the frequency and duration.