Development of Human Posture Simulation Method for Assessing Posture Angles and Spinal Loads

Posture monitoring in healthcare: a systematic mapping study and taxonomy

Palliative treatments for back pain usually include exercise, analgesics, physiotherapy, prostheses, and surgery in severe cases. Technologies for postural monitoring are growing, and they are important in preventing back pain and mitigating permanent damage. Remote work, especially after the COVID-19 pandemic, made people spend more time than usual in chairs and environments not certified by the health aspects of work. This research investigated through a Systematic Mapping Study (SMS) contributions in posture monitoring for healthcare in smart environments, including the different methods to obtain the posture, the limitations, and the target audience of the proposed models. The SMS was conducted in eight databases, including articles from January 2012 to March 2022. The initial search yielded 3161 articles, of which 34 were selected after applying the filtering criteria. Moreover, this study presents the challenges related to posture behavior monitoring, identifying studies and implementations that apply assistive technology for postural monitoring and improving the health and life of remote workers. In addition, three commercial postural devices are presented, and what challenges they currently face. Regarding healthcare, results showed a prevalence of using the Internet of Things (IoT) devices such as wireless sensor networks and inertial measurement unit (IMU) sensors. This article also proposes a taxonomy, showing the most used technologies and algorithms for improving posture, besides the posture-monitoring hierarchy classifying into three important branches: (a) Data Collect; (b) Data Transmission; and (c) Data Analysis.

Estimating Trunk Angle Kinematics During Lifting Using a Computationally Efficient Computer Vision Method

OBJECTIVE

A computer vision method was developed for estimating the trunk flexion angle, angular speed, and angular acceleration by extracting simple features from the moving image during lifting.

BACKGROUND

Trunk kinematics is an important risk factor for lower back pain, but is often difficult to measure by practitioners for lifting risk assessments.

METHODS

Mannequins representing a wide range of hand locations for different lifting postures were systematically generated using the University of Michigan 3DSSPP software. A bounding box was drawn tightly around each mannequin and regression models estimated trunk angles. The estimates were validated against human posture data for 216 lifts collected using a laboratory-grade motion capture system and synchronized video recordings. Trunk kinematics, based on bounding box dimensions drawn around the subjects in the video recordings of the lifts, were modeled for consecutive video frames.

RESULTS

The mean absolute difference between predicted and motion capture measured trunk angles was 14.7°, and there was a significant linear relationship between predicted and measured trunk angles (R2 = .80, p < .001). The training error for the kinematics model was 2.3°.

CONCLUSION

Using simple computer vision-extracted features, the bounding box method indirectly estimated trunk angle and associated kinematics, albeit with limited precision.

APPLICATION

This computer vision method may be implemented on handheld devices such as smartphones to facilitate automatic lifting risk assessments in the workplace.

A kinematic posture analysis of neurological assistants in their daily working practice-a pilot study

Background

The aim of this pilot study was to analyze postures during the work of neurologists with respect to their occupational activities.

Methods

A total data material of 64.8 h (3885.74 min) of nine (three m/six f) neurologists (assistant physicians) was collected. Kinematic data were collected using the CUELA system (electro-goniometry). In addition, the occupational tasks performed on-site were subject to a detailed objective activity analysis. All activities were assigned to the categories “Office activities” (I), “Measures on patients” (II) and “Other activities” (III). The angle values of each body region (evaluation parameters) were evaluated according to ergonomic ISO standards.

Results

Only 3.4% of the working hours were spent with (II), while 50.8% of time was spent with (I) and 45.8% with (III). All tasks of category (II) revealed an increased ergonomic risk to the head, neck, trunk and back areas. During category (I) especially neck and back movements in the sagittal plane showed higher ergonomic risk levels.

Conclusion

Despite frequently performed awkward body positions in (II), the ergonomic risk is considered as rather low, since the percentage time share totaled only 3.4%. As a result, “Office activities” have been detected as high predictor to cause stress load on the musculoskeletal system in the daily work of neurologists.

Non-chemical Risk Assessment for Lifting and Low Back Pain Based on Bayesian Threshold Models

Background

Self-reported low back pain (LBP) has been evaluated in relation to material handling lifting tasks, but little research has focused on relating quantifiable stressors to LBP at the individual level. The National Institute for Occupational Safety and Health (NIOSH) Composite Lifting Index (CLI) has been used to quantify stressors for lifting tasks. A chemical exposure can be readily used as an exposure metric or stressor for chemical risk assessment (RA). Defining and quantifying lifting nonchemical stressors and related adverse responses is more difficult. Stressor–response models appropriate for CLI and LBP associations do not easily fit in common chemical RA modeling techniques (e.g., Benchmark Dose methods), so different approaches were tried.

Methods

This work used prospective data from 138 manufacturing workers to consider the linkage of the occupational stressor of material lifting to LBP. The final model used a Bayesian random threshold approach to estimate the probability of an increase in LBP as a threshold step function.

Results

Using maximal and mean CLI values, a significant increase in the probability of LBP for values above 1.5 was found.

Conclusion

A risk of LBP associated with CLI values > 1.5 existed in this worker population. The relevance for other populations requires further study.

Ergonomic Risk Factors and Their Association With Lower Back and Neck Pain Among Pharmaceutical Employees in Iran

The aim of this cross-sectional study was to explore the ergonomic risk factors for low back pain (LBP) and neck pain in an industry in which only light tasks are performed. These common disorders can be significant work-related musculoskeletal disorders. This study included 396 employees who worked in packaging units of pharmaceutical companies. The Nordic Musculoskeletal Questionnaire and the rapid upper limb assessment (RULA) were used to generate data. This study showed an association between LBP, RULA scores, and workers' education. For neck pain, an association was found with age, gender, and subjective questions about working posture (mostly sitting/standing or alternating between the two). Absence from work more than 3 days, which could have been associated with pain, was significantly associated with both disorders.

Efficacy of the revised NIOSH lifting equation to predict risk of low-back pain associated with manual lifting: a one-year prospective study

OBJECTIVE

The objective was to evaluate the efficacy of the Revised National Institute for Occupational Safety and Health (NIOSH) lifting equation (RNLE) to predict risk of low-back pain (LBP).

BACKGROUND

In 1993, NIOSH published the RNLE as a risk assessment method for LBP associated with manual lifting. To date, there has been little research evaluating the RNLE as a predictor of the risk of LBP using a prospective design.

METHODS

A total of 78 healthy industrial workers' baseline LBP risk exposures and self-reported LBP at one-year follow-up were investigated. The composite lifting index (CLI), the outcome measure of the RNLE for analyzing multiple lifting tasks, was used as the main risk predictor. The risk was estimated using the mean and maximum CLI variables at baseline and self-reported LBP during the follow-up. Odds ratios (ORs) were calculated using a logistic regression analysis adjusted for covariates that included personal factors, physical activities outside of work, job factors, and work-related psychosocial characteristics.

RESULTS

The one-year self-reported LBP incidence was 32.1%. After controlling for history of prior LBP, supervisory support, and job strain, the categorical mean and maximum CLI above 2 had a significant relationship (OR = 5.1-6.5) with self-reported LBP, as compared with the CLI below or equal to I. The correlation between the continuous CLI variables and LBP was unclear.

CONCLUSIONS

The CLI > 2 threshold may be useful for predicting self-reported LBP. Research with a larger sample size is needed to clarify the exposure-response relationship between the CLI and LBP.