Development and Verification of Postural Control Assessment Using Deep-Learning-Based Pose Estimators: Towards Clinical Applications.

Occupational therapists evaluate various aspects of a client's occupational performance. Among these, postural control is one of the fundamental skills that need assessment. Recently, several methods have been proposed to estimate postural control abilities using deep-learning-based approaches. Such techniques allow for the potential to provide automated, precise, fine-grained quantitative indices simply by evaluating videos of a client engaging in a postural control task. However, the clinical applicability of these assessment tools requires further investigation. In the current study, we compared three deep-learning-based pose estimators to assess their clinical applicability in terms of accuracy of pose estimations and processing speed. In addition, we verified which of the proposed quantitative indices for postural controls best reflected the clinical evaluations of occupational therapists. A framework using deep-learning techniques broadens the possibility of quantifying clients' postural control in a more fine-grained way compared with conventional coarse indices, which can lead to improved occupational therapy practice.

Computer Vision-Based Approach for Quantifying Occupational Therapists' Qualitative Evaluations of Postural Control.

This study aimed to leverage computer vision (CV) technology to develop a technique for quantifying postural control. A conventional quantitative index, occupational therapists' qualitative clinical evaluations, and CV-based quantitative indices using an image analysis algorithm were applied to evaluate the postural control of 34 typically developed preschoolers. The effectiveness of the CV-based indices was investigated relative to current methods to explore the clinical applicability of the proposed method. The capacity of the CV-based indices to reflect therapists' qualitative evaluations was confirmed. Furthermore, compared to the conventional quantitative index, the CV-based indices provided more detailed quantitative information with lower costs. CV-based evaluations enable therapists to quantify details of motor performance that are currently observed qualitatively. The development of such precise quantification methods will improve the science and practice of occupational therapy and allow therapists to perform to their full potential.