Prolonged static sitting at the workplace is considered one of the main risks for the 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 discomfort and lower back pain (LBP), even though the scientific explanation of this relationship is still unclear and raises disputes in the scientific community. The current study focused on evaluating the relationship between LBP and prolonged sitting in poor posture using photogrammetric images, postural angle calculation, machine learning models, and questionnaire-based self-reports regarding the occurrence of LBP and similar symptoms among the participants. Machine learning models trained with this data are employed to recognize poor 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.

This study investigates the risk of developing musculoskeletal disorders (MSDs) in individuals performing standing tasks, with a focus on real-time posture assessment using motion capture technology. Improper body posture and repetitive movements during daily work activities can impose strain on the musculoskeletal system, increasing the likelihood of discomfort and long-term injury. Data were collected from five male and female participants using the Perception Neuron motion capture system, with body-mounted sensors tracking posture and movement. Joint angles were calculated to distinguish between correct and incorrect postures based on ISO 11226:2000 ergonomic guidelines. Key physical risk factors identified included prolonged forward trunk inclination, elevated arm positions, and repetitive actions. The analysis revealed that participants frequently adopted moderate- to high-risk postures, especially when working at non-ergonomic desk heights, suggesting a heightened risk of MSDs such as back and upper limb pain. These findings underscore the importance of real-time ergonomic monitoring and adaptive workstation design to reduce musculoskeletal risks in standing work environments.

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.