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Description

Dystonia is a movement disorder that causes unusual movements and involuntary muscle contractions affecting some parts of the whole body. Selecting drugs and doses is a highly personalized process for dystonia, requiring frequent visits to the clinic, pointing toward the need for more systematic and objective methods of collecting patient data. A deep learning-based pose estimation algorithm can be a good candidate for aiding independent clinical assessment of dystonia as it has outperformed the classical approach to human pose estimation. The deep learning-based model can help patients and physicians assess the first symptoms of neurological diseases and build low-cost solutions not only for dystonia score prediction but also to monitor the progress of the disease. Pose estimation algorithms with convolution networks have already been shown to extract relevant information about the motor signals of Parkinson’s disease from video assessments, and the calculated score correlates well with the clinical score. OpenPose algorithm was used for human pose estimation in videos of dystonia patients being clinically assessed to annotate body key points in the videos. This project explored the basic pipeline steps required to process the clinical videos, including spatiotemporal key points normalization. CNN successfully predicted neck dystonia scores to around the scores obtained from standard clinical assessment, leaving space for further validations and research with more data and methods.