Validity of Deep Learning-Based Motion Capture Using DeepLabCut to Assess Proprioception in Children

Abstract

Proprioceptive deficits can lead to impaired motor performance. Therefore, accurately measuring proprioceptive function in order to identify deficits as soon as possible is important. Techniques based on deep learning to track body landmarks in simple video recordings are promising to assess proprioception (joint position sense) during joint position reproduction (JPR) tests in clinical settings, outside the laboratory and without the need to attach markers. Fifteen typically developing children participated in 90 knee JPR trials and 21 typically developing children participated in 126 hip JPR trials. Concurrent validity of two-dimensional deep-learning-based motion capture (DeepLabCut) to measure the Joint Reproduction Error (JRE) with respect to laboratory-based optoelectronic three-dimensional motion capture (Vicon motion capture system, gold standard) was assessed. There was no significant difference in the hip and knee JRE measured with DeepLabCut and Vicon. Two-dimensional deep-learning-based motion capture (DeepLabCut) is valid to assess proprioception with respect to the gold standard in typically developing children. Tools based on deep learning, such as DeepLabCut, make it possible to accurately measure joint angles in order to assess proprioception without the need of a laboratory and to attach markers, with a high level of automatization.