Abstract:Postural assessment can help doctors and therapists identify risk factors for low back pain and determine appropriate follow-up treatment.
Postural alignment is not perfectly symmetrical, and small asymmetries can instead represent norms and criteria for postural evaluation.
It is necessary to comprehensively observe patients’ posture in all directions and analyze the factors related to posture evaluation.
The results of reliability show that in general intra-rater reliability is higher than inter-rater … Show more
“…Extended periods of poor posture, such as slouching or maintaining a bent position, impose biomechanical stress on the lower back, affecting spinal alignment and causing strain on supporting structures. This stress increases the likelihood of conditions like lumbar hyper lordosis, sway-back, round back, flat back, and scoliosis contributing to discomfort and pain [13].…”
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.
“…Extended periods of poor posture, such as slouching or maintaining a bent position, impose biomechanical stress on the lower back, affecting spinal alignment and causing strain on supporting structures. This stress increases the likelihood of conditions like lumbar hyper lordosis, sway-back, round back, flat back, and scoliosis contributing to discomfort and pain [13].…”
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.
Background: Biomechanical analysis of the sagittal alignment of the lumbar spine and pelvis on radiographs is common in clinical practices including chiropractic, physical therapy, scoliosis-related thoraco-lumbo-sacral orthosis (TLSO) management, orthopedics, and neurosurgery. Of specific interest is the assessment of pelvic morphology and the relationship between angle of pelvic incidence, sacral slope, and lumbar lordosis to pain, disability, and clinical treatment of spine conditions. The current state of the literature on the reliability of common methods quantifying these parameters on radiographs is limited. Methods: The objective of this systematic review is to identify and review the available studies on the reliability of different methods of biomechanical analysis of sagittal lumbo-pelvic parameters used in clinical practice. Our review followed the recommendations of the preferred reporting items for systematic reviews and meta-analyses (PRISMA). The design of this systematic review was registered with PROSPERO (CRD42023379873). Results: The search strategy yielded a total of 2387 articles. A total of 1539 articles were screened after deduplication and exclusion by automation tools, leaving 473 full-text articles that were retrieved. After exclusion, 64 articles met the inclusion criteria. The preponderance of the evidence showed good to excellent reliability for biomechanical assessment of sagittal lumbo-pelvic spine alignment. Conclusions: The results of this systematic review of the literature show that sagittal radiographic analysis of spinal biomechanics and alignment of the human lumbo-pelvic spine is a reliable tool for aiding diagnosis and management in clinical settings.
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