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Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments
Published on: March 28, 2018
Machine Learning for 3D Kinematic Analysis of Movements in Neurorehabilitation
1Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 710 Westwood Plaza, Rm 3-232, Los Angeles, CA, 90095, USA. aarac@mednet.ucla.edu.
Purpose Of Review:
Recent 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 Findings:
Applications 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. Advances in machine learning can help automate the process of obtaining 3D kinematics of human movements and to identify/classify patterns of movement.
