Validity Analysis of Monocular Human Pose Estimation Models Interfaced with a Mobile Application for Assessing Upper Limb Range of Motion

Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photo...

Full description

Saved in:
Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-12, Vol.24 (24), p.7983
Main Authors: Moreira, Rayele, Teixeira, Silmar, Fialho, Renan, Miranda, Aline, Lima, Lucas Daniel Batista, Carvalho, Maria Beatriz, Alves, Ana Beatriz, Bastos, Victor Hugo Vale, Teles, Ariel Soares
Format: Article
Language:English
Subjects:
Computer vision
Elbow
goniometry
Human performance
human pose estimation
Machine vision
Medical research
Medicine, Experimental
Performance evaluation
Posture
Range of motion
Sensors
Smartphones
upper limbs
Wireless telephone software
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Human Pose Estimation (HPE) is a computer vision application that utilizes deep learning techniques to precisely locate Key Joint Points (KJPs), enabling the accurate description of a person’s pose. HPE models can be extended to facilitate Range of Motion (ROM) assessment by leveraging patient photographs. This study aims to evaluate and compare the performance of HPE models for assessing upper limbs ROM. A physiotherapist evaluated the degrees of ROM in shoulders (flexion, extension, and abduction) and elbows (flexion and extension) for fifty-two participants using both Universal Goniometer (UG) and five HPE models. Participants were instructed to repeat each movement three times to obtain measurements with the UG, then positioned while photos were captured using the NLMeasurer mobile application. The paired t-test, bias, and error measures were employed to evaluate the difference and agreement between measurement methods. Results indicated that the MoveNet Thunder INT16 model exhibited superior performance. Root Mean Square Errors obtained through this model were
ISSN:1424-8220
1424-8220
DOI:10.3390/s24247983