Systematic Comparison of OWAS, RULA, and REBA Based on a Literature Review

This study aimed to systematically compare three representative observational methods for assessing musculoskeletal loadings and their association with musculoskeletal disorders (MSDs): Ovako Working Posture Analysis System (OWAS), Rapid Upper Limb Assessment (RULA), and Rapid Entire Body Assessment (REBA). The comparison was based on a literature review without time limitations and was conducted on various factors related to observational methods. The comparisons showed that although it has a significant limitation of comprising only two classifications for the leg postures, (1) the RULA is the most frequently used method among the three techniques; (2) many studies adopted the RULA even in evaluation of unstable lower limb postures; (3) the RULA assessed postural loads as higher risk levels in most studies reviewed in this research; (4) the intra- and inter-reliabilities for the RULA were not low; and (5) the risk levels assessed by the RULA were more significantly associated with postural load criteria such as discomfort, MHTs and % capable at the trunk, and MSDs.

Feasibility of the basic movements of Azeri dance in the balance and posture of a person with Parkinson's disease: ABA single-subject design

Background/aims Basic movements of Azeri dance as an intervention may have an impact on the physical and mental status of people with Parkinson's disease. The aim of this study was to assess the feasibility and efficacy of basic movements of Azeri dance on the balance and static posture of a person with Parkinson's disease. The effects of basic movements of Azeri dance on quality of life, quality of sleep and the performance in activities of daily living were reported before and after the intervention. Methods In this single-subject experimental ABA design, a 64-year-old man with a 4-year history of Parkinson's disease was selected. The outcome measures consisted of the Functional Reach Test, Timed Up and Go Test, qualitative static posture analysis, Short Form Health Survey, Modified Barthel Index and Pittsburgh Sleep Quality Index. Results An increasing trend in balance from the initial baseline stage through to the end of the intervention stage was found. Static posture analysis showed that the patient's posture improved qualitatively. His quality of life score changed after the intervention; however, his sleep quality and activites of daily living scores did not change. Conclusions Basic movements of Azeri dance are useful in the rehabilitation of patients with Parkinson's disease who have balance issues and postural instability, but further research is warranted.

A study on musculoskeletal complaints and working postures in pathology specialists in Iran

Background Musculoskeletal disorders (MSDs) are one of the most common causes of occupational injuries and disabilities among health care workers. This study investigates the relationship between musculoskeletal complaints and pathologist postures in laboratories. Method In this cross-sectional study, 40 pathologists were evaluated. MSDs in different body segments of the participants were evaluated by Nordic questionnaire. For postural analysis, 20 min film was recorded while using a microscope by subjects. Posture analysis was done by the Rapid Upper Limb Assessment (RULA) method and their repetitive movements were scored. The data was analyzed by SPSS Version 11.5. Results The mean age and duration of employment of subjects was 36.57 ± 7.54 years and 6.50 ± 6.30 years, respectively. Most MSDs were found in neck (65%), wrist (57.5%), upper back (50%) and lower back (47.5%). The mean RULA grand score was higher in participants with upper back and shoulder pain. A statistically significant relationship was found between the mean RULA grand, the upper back pain (P = 0.02) and the wrist pain (P = 0.003), as well as between the mean RULA B, the neck pain (P = 0.02) and the lower back pain (P = 0.05). The results showed a significant relationship between mean weekly working hours and tight (P < 0.001), wrist (p = 0.01) and ankle (P = 0.008). Conclusion This study revealed high prevalence of MSDs among the pathologists. Therefore, performing ergonomic corrective actions is essential in order to improve their physical conditions at work.

Innovative Research on the Development of Online Education Mode of Internet Thinking Based on the Discrimination of Learning Attention under the Analysis of Head Posture

With the rapid development of Internet technology and the popularity of 5G and broadband, online education in China, especially mobile online education, is in full swing. Based on the development status of online education in China, this paper analyzes the innovative application of learning attention discrimination based on head posture analysis in the development of online education mode of Internet thinking. Learning attention is an important factor of students’ learning efficiency, which directly affects students’ learning effect. In order to effectively monitor students’ learning attention in online teaching, a method of distinguishing students’ learning attention based on head posture recognition is proposed. In the tracking process, as long as the head angle of the current frame is close to the head angle of the key frame in a certain scale model, the visual angle apparent model can reduce the error accumulation in large-scale tracking. A Dynamic Bayesian Network (DBN) model is used to reason students’ Learning Attention Goal (LAG), which combines the relationships among multiple LAGs, multiple students’ positions, multicamera face images, and so on. We measure the head posture through the similarity vector between the face image and multiple face categories without explicitly calculating the specific head posture value. The test results show that the proposed model can effectively detect students’ learning attention and has a good application prospect.

Syntactic model-based human body 3D reconstruction and event classification via association based features mining and deep learning

The study of human posture analysis and gait event detection from various types of inputs is a key contribution to the human life log. With the help of this research and technologies humans can save costs in terms of time and utility resources. In this paper we present a robust approach to human posture analysis and gait event detection from complex video-based data. For this, initially posture information, landmark information are extracted, and human 2D skeleton mesh are extracted, using this information set we reconstruct the human 2D to 3D model. Contextual features, namely, degrees of freedom over detected body parts, joint angle information, periodic and non-periodic motion, and human motion direction flow, are extracted. For features mining, we applied the rule-based features mining technique and, for gait event detection and classification, the deep learning-based CNN technique is applied over the mpii-video pose, the COCO, and the pose track datasets. For the mpii-video pose dataset, we achieved a human landmark detection mean accuracy of 87.09% and a gait event recognition mean accuracy of 90.90%. For the COCO dataset, we achieved a human landmark detection mean accuracy of 87.36% and a gait event recognition mean accuracy of 89.09%. For the pose track dataset, we achieved a human landmark detection mean accuracy of 87.72% and a gait event recognition mean accuracy of 88.18%. The proposed system performance shows a significant improvement compared to existing state-of-the-art frameworks.

Behavioral and postural analyses establish sleep-like states for mosquitoes that can impact interactions with hosts

Sleep is an evolutionarily conserved process that has been described in different animal systems. For insects, sleep characterization has been primarily achieved using behavioral and electrophysiological correlates in a few systems. Sleep in mosquitoes, which are important vectors of disease-causing pathogens, has not been directly examined. This is surprising as circadian rhythms, which have been well studied in mosquitoes, influence sleep in other systems. In this study, we characterized sleep in mosquitoes using body posture analysis and behavioral correlates, and quantified the effect of sleep deprivation on sleep rebound and host landing. Body and appendage position metrics revealed a clear distinction between the posture of mosquitoes in their putative sleep and awake states for multiple species, which correlates with a reduction in responsiveness to host cues. Sleep assessment informed by these posture analyses indicated significantly more sleep during periods of low activity. Nighttime and daytime sleep deprivation resulting from the delivery of vibration stimuli induced sleep rebound in the subsequent phase in day and night active mosquitoes, respectively. Lastly, sleep deprivation suppressed host landing in both laboratory and field settings when mosquitoes would normally be active. These results suggest that quantifiable sleep states occur in mosquitoes, and highlight the potential epidemiological importance of mosquito sleep.