Wi-Fi Channel-State-Information-Based Clustering of Freezing-of-Gait Episodes Caused by Parkinson's Disease Using...

Because Parkinson's disease (PD) symptoms progress and worsen slowly over time, it is difficult to assess the states and the effects of medications based on short-term clinical diagnoses. Therefore, monitoring of patients at home plays an important role in improving the medical care of PD patients. This paper proposes and constructs a non-contact, unsupervised normal-gait/freezing-of-gait periods clustering system using Wi-Fi channel state information (CSI) based on short-time autocorrelation function (ST-ACF) as a method to continuously monitor symptoms of gait freezing, one of the main symptoms of PD. We use ST-ACF to extract robust periodic signals, followed by short-time complex-frequency principal component analysis (ST-CFPCA) to extract human motion features from the time-series ST-ACF, which is high-dimensional due to the CSI nature. Experiments demonstrate that, with unsupervised clustering, the system identifies the freezing-of-gait episodes with an accuracy of 95.3 %.Clinical relevance This proposed system using short-time autocorrelation function (ST-ACF) and short-time complex-frequency principal component analysis (ST-CFPCA) will realize continuous monitoring of Parkinson's disease at home by using widely available Wi-Fi radio waves, leading to daily checking of the progression of symptoms and the effectiveness of medication.