Recognition of Freezing of Gait in Parkinson’s Disease Based on Machine Vision

Abstract

BackgroundFreezing of gait (FOG) is a common clinical manifestation of Parkinson’s disease (PD), mostly occurring in the intermediate and advanced stages. FOG is likely to cause patients to fall, resulting in fractures, disabilities and even death. Currently, the pathogenesis of FOG is unclear, and FOG detection and screening methods have various defects, including subjectivity, inconvenience, and high cost. Due to limited public healthcare and transportation resources during the COVID-19 pandemic, there are greater inconveniences for PD patients who need diagnosis and treatment.ObjectiveA method was established to automatically recognize FOG in PD patients through videos taken by mobile phone, which is time-saving, labor-saving, and low-cost for daily use, which may overcome the above defects. In the future, PD patients can undergo FOG assessment at any time in the home rather than in the hospital.MethodsIn this study, motion features were extracted from timed up and go (TUG) test and the narrow TUG (Narrow) test videos of 50 FOG-PD subjects through a machine learning method; then a motion recognition model to distinguish between walking and turning stages and a model to recognize FOG in these stages were constructed using the XGBoost algorithm. Finally, we combined these three models to form a multi-stage FOG recognition model.ResultsWe adopted the leave-one-subject-out (LOSO) method to evaluate model performance, and the multi-stage FOG recognition model achieved a sensitivity of 87.5% sensitivity and a specificity of 79.82%.ConclusionA method to realize remote PD patient FOG recognition based on mobile phone video is presented in this paper. This method is convenient with high recognition accuracy and can be used to rapidly evaluate FOG in the home environment and remotely manage FOG-PD, or screen patients in large-scale communities.

Development of a Tremor Detection Algorithm for use in an Academic Movement Disorders Center

“…Despite this, technologies for identifying tremor have progressed towards standardization and clinical uptake very slowly [1]. In the research domain, various technologies measure human motion, including body-worn sensors [3,7,8], 3D motion capture [9,10], and -most recently -pose recognition from monocular video [11][12][13]. Digitizing tablets are often used for assessing tremor during tasks like spiral drawing [14,15] and for discriminating tremor from bradykinesia during finger tapping [16].…”