AI Article Synopsis

  • Occupational therapists assess various aspects of a client's performance, particularly focusing on postural control as a key skill.
  • New deep-learning techniques have been developed to evaluate postural control more accurately through video analysis, offering precise and detailed quantitative measures.
  • This study compares three deep-learning pose estimators to determine their accuracy and speed while also identifying which quantitative measures align best with therapists' clinical evaluations, potentially enhancing therapy practices.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9729024PMC
http://dx.doi.org/10.1155/2022/6952999DOI Listing

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