Abstract
Biophotogrammetric methods for postural analysis have shown effectiveness in the clinical practice because they do not expose individuals to radiation. Furthermore, valid statements can be made about postural weaknesses. Usually, such measurements are collected via markers attached to the subject’s body, which can provide conclusions about the current posture. The craniovertebral angle (CVA) is one of the recognized measurements used for the analysis of human head–neck postures. This study presents a novel method to automate the detection of the landmarks that are required to determine the CVA in RGBs. Different image processing methods are applied together with a neuronal network Openpose to find significant landmarks in a photograph. A prominent key body point is the spinous process of the cervical vertebra C7, which is often visible on the skin. Another visual landmark needed for the calculation of the CVA is the ear tragus. The methods proposed for the automated detection of the C7 spinous process and ear tragus are described and evaluated using a custom dataset. The results indicate the reliability of the proposed detection approach, particularly head postures.
Subject
Management Science and Operations Research,Mechanical Engineering,Energy Engineering and Power Technology
Reference35 articles.
1. Providing Information through Smart Platforms: An Applied Study on Academic Libraries in Saudi Universities
2. Ericsson Mobility Report. Report, Ericsson
https://www.ericsson.com/en/reports-and-papers/mobility-report/reports/november-2021
3. Number of Smartphone Users from 2016 to 2021. Website
https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
4. Gravitational demand on the neck musculature during tablet computer use
5. Computer-related posture and musculoskeletal discomfort in middle school students