Towards a Visualizable, De-identified Synthetic Biomarker of Human Movement Disorders

Abstract

Human motion analysis has been a common thread across modern and early medicine. While medicine evolves, analysis of movement disorders is mostly based on clinical presentation and trained observers making subjective assessments using clinical rating scales. Currently, the field of computer vision has seen exponential growth and successful medical applications. While this has been the case, neurology, for the most part, has not embraced digital movement analysis. There are many reasons for this including: the limited size of labeled datasets, accuracy and nontransparent nature of neural networks, and potential legal and ethical concerns. We hypothesize that a number of opportunities are made available by advancements in computer vision that will enable digitization of human form, movements, and will represent them synthetically in 3D. Representing human movements within synthetic body models will potentially pave the way towards objective standardized digital movement disorder diagnosis and building sharable open-source datasets from such processed videos. We provide a perspective of this emerging field and describe how clinicians and computer scientists can navigate this new space. Such digital movement capturing methods will be important for both machine learning-based diagnosis and computer vision-aided clinical assessment. It would also supplement face-to-face clinical visits and be used for longitudinal monitoring and remote diagnosis.

Emergence of 3D body model-based infant health evaluation: a review

“…The use of 3D body model-based technologies has been driven by the need for more accurate and efficient ways to capture, analyze, and simulate human motion and shape [4][5][6] . These technologies use computer algorithms to create 3D digital models of the human body, which can then be used for a wide range of applications, including film and video game production, sports training, biomechanical research and medical diagnosis [7][8][9][10] . The availability of affordable and powerful computer hardware, along with advances in computer vision and machine learning, have enabled researchers to create algorithms that can accurately estimate body shape and motion from 2D images or video 11,12 .…”