Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation

AbstractPurpose of ReviewRecent advances in the machine learning field, especially in deep learning, provide the opportunity for automated, detailed, and unbiased analysis of motor behavior. Although there has not yet been wide use of these techniques in the motor rehabilitation field, they have great potential. In this review, I describe how the current state of machine learning can be applied to 3D kinematic analysis, and how this will have an impact on neurorehabilitation.Recent FindingsApplications of deep learning methods, in the form of convolutional neural networks, have been revolutionary for image analysis such as face recognition and object detection in images, exceeding human level performance. Recent studies have shown applicability of these deep learning approaches to human posture and movement classification. It is to be expected that portable stereo-camera systems will bring 3D pose estimation into the clinical setting and allow the assessment of movement quality in response to interventions.SummaryAdvances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement.
Source: Current Neurology and Neuroscience Reports - Category: Neuroscience Source Type: research