Version 1
: Received: 9 May 2024 / Approved: 10 May 2024 / Online: 10 May 2024 (11:38:28 CEST)
How to cite:
Markova, V.; Markov, M.; Petrova, Z.; Filkova, S. A Photogrammetric Assessment of Incorrect Sitting Posture Leading to Low Back Pain. Preprints2024, 2024050664. https://doi.org/10.20944/preprints202405.0664.v1
Markova, V.; Markov, M.; Petrova, Z.; Filkova, S. A Photogrammetric Assessment of Incorrect Sitting Posture Leading to Low Back Pain. Preprints 2024, 2024050664. https://doi.org/10.20944/preprints202405.0664.v1
Markova, V.; Markov, M.; Petrova, Z.; Filkova, S. A Photogrammetric Assessment of Incorrect Sitting Posture Leading to Low Back Pain. Preprints2024, 2024050664. https://doi.org/10.20944/preprints202405.0664.v1
APA Style
Markova, V., Markov, M., Petrova, Z., & Filkova, S. (2024). A Photogrammetric Assessment of Incorrect Sitting Posture Leading to Low Back Pain. Preprints. https://doi.org/10.20944/preprints202405.0664.v1
Chicago/Turabian Style
Markova, V., Zornica Petrova and Silviya Filkova. 2024 "A Photogrammetric Assessment of Incorrect Sitting Posture Leading to Low Back Pain" Preprints. https://doi.org/10.20944/preprints202405.0664.v1
Abstract
The prolonged static sitting at the workplace is considered as one of the main risks for development of musculoskeletal disorders (MSDs) and adverse health effects. Factors such as poor posture and extended sitting are perceived to be a reason for conditions such as lumbar hyperlordosis and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. This publication proposes the low back pain assessment (LBPA) dataset collected through experiment with 100 participants and consisting of photogrammetric images with intentionally placed body markers, calculated postural angles and tags correct and incorrect posture assessed by habilitated rehabilitator, as well as questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed for recognizing incorrect body postures. Two scenarios have been elaborated for modeling purposes: scenario 1, based on natural body posture tagged as correct and incorrect, and scenario 2, based on incorrect body postures, corrected additionally by the rehabilitator. The achieved accuracies of respectively 75.3% and 85% for both scenarios reveal the potential for future research in enhancing awareness and actively managing posture-related issues that elevate the likelihood of developing lower back pain symptoms.
Keywords
photogrammetry; MSDs; low back pain; ergonomics; incorrect sitting posture; postural angles, body markers, self-assessment; ML models
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
