Biomechanical aspects of work-related musculoskeletal disorders

sEMG Spectral Analysis and Machine Learning Algorithms Are Able to Discriminate Biomechanical Risk Classes Associated with Manual Material Liftings

Manual material handling and load lifting are activities that can cause work-related musculoskeletal disorders. For this reason, the National Institute for Occupational Safety and Health proposed an equation depending on the following parameters: intensity, duration, frequency, and geometric characteristics associated with the load lifting. In this paper, we explore the feasibility of several Machine Learning (ML) algorithms, fed with frequency-domain features extracted from electromyographic (EMG) signals of back muscles, to discriminate biomechanical risk classes defined by the Revised NIOSH Lifting Equation. The EMG signals of the multifidus and erector spinae muscles were acquired by means of a wearable device for surface EMG and then segmented to extract several frequency-domain features relating to the Total Power Spectrum of the EMG signal. These features were fed to several ML algorithms to assess their prediction power. The ML algorithms produced interesting results in the classification task, with the Support Vector Machine algorithm outperforming the others with accuracy and Area under the Receiver Operating Characteristic Curve values of up to 0.985. Moreover, a correlation between muscular fatigue and risky lifting activities was found. These results showed the feasibility of the proposed methodology-based on wearable sensors and artificial intelligence-to predict the biomechanical risk associated with load lifting. A future investigation on an enriched study population and additional lifting scenarios could confirm the potential of the proposed methodology and its applicability in the field of occupational ergonomics.

Scoping review of the occurrence and characteristics of firefighter exercise and training injuries

OBJECTIVES

To summarize the current research on the occurrence of firefighter exercise and training injuries and to describe the nature of these injuries.

METHODS

Scoping review methods were used to identify articles and extract information relevant to firefighter exercise and training injuries. Relevant articles were identified from MEDLINE, Web of Science, CINAHL, Embase, PubMed, and through hand-searching.

RESULTS

A total of 1053 articles were identified, and 23 met the inclusion criteria. Nine studies were retrospective analyses of injury data, 13 studies used surveys to identify injuries in the past year, and 1 study reviewed U.S. firefighter injury reports. Three studies included both career and volunteer firefighters, 2 studies included career firefighters, 2 studies include volunteer firefighters, 1 study include recruits and 16 studies did not specify the career status. The occurrence of exercise and training injuries from 22 of the 23 studies ranged from 8.1 to 55.3% of reported injuries. One study found that 3 out of 15 fire departments identified exercise and training as the most common cause of their firefighter injuries. The 13 articles that reported the type of injuries identified musculoskeletal disorders as the most common type of injury (32% to 79% of reported injuries). The ankle, knee and leg were identified as the most commonly injured areas of the body.

CONCLUSIONS

Training injuries are common in firefighters and must be prevented. Future research is needed to identify root causes of training injuries to guide prevention strategies.

Wearable inertial sensors for human movement analysis: a five-year update

INTRODUCTION

The aim of the present review is to track the evolution of wearable IMUs from their use in supervised laboratory- and ambulatory-based settings to their application for long-term monitoring of human movement in unsupervised naturalistic settings.

AREAS COVERED

Four main emerging areas of application were identified and synthesized, namely, mobile health solutions (specifically, for the assessment of frailty, risk of falls, chronic neurological diseases, and for the monitoring and promotion of active living), occupational ergonomics, rehabilitation and telerehabilitation, and cognitive assessment. Findings from recent scientific literature in each of these areas was synthesized from an applied and/or clinical perspective with the purpose of providing clinical researchers and practitioners with practical guidance on contemporary uses of inertial sensors in applied clinical settings.

EXPERT OPINION

IMU-based wearable devices have undergone a rapid transition from use in laboratory-based clinical practice to unsupervised, applied settings. Successful use of wearable inertial sensing for assessing mobility, motor performance and movement disorders in applied settings will rely also on machine learning algorithms for managing the vast amounts of data generated by these sensors for extracting information that is both clinically relevant and interpretable by practitioners.

Work-Related Risk Assessment According to the Revised NIOSH Lifting Equation: A Preliminary Study Using a Wearable Inertial Sensor and Machine Learning

Many activities may elicit a biomechanical overload. Among these, lifting loads can cause work-related musculoskeletal disorders. Aspiring to improve risk prevention, the National Institute for Occupational Safety and Health (NIOSH) established a methodology for assessing lifting actions by means of a quantitative method based on intensity, duration, frequency and other geometrical characteristics of lifting. In this paper, we explored the machine learning (ML) feasibility to classify biomechanical risk according to the revised NIOSH lifting equation. Acceleration and angular velocity signals were collected using a wearable sensor during lifting tasks performed by seven subjects and further segmented to extract time-domain features: root mean square, minimum, maximum and standard deviation. The features were fed to several ML algorithms. Interesting results were obtained in terms of evaluation metrics for a binary risk/no-risk classification; specifically, the tree-based algorithms reached accuracies greater than 90% and Area under the Receiver operating curve characteristics curves greater than 0.9. In conclusion, this study indicates the proposed combination of features and algorithms represents a valuable approach to automatically classify work activities in two NIOSH risk groups. These data confirm the potential of this methodology to assess the biomechanical risk to which subjects are exposed during their work activity.

Musculoskeletal Disorder Symptoms among Workers at an Informal Electronic-Waste Recycling Site in Agbogbloshie, Ghana

Informal recycling of electrical and electronic waste (e-waste) has myriad environmental and occupational health consequences, though information about the chronic musculoskeletal health effects on workers is limited. The aim of this study was to examine the prevalence and intensity of self-reported musculoskeletal disorder (MSD) symptoms among e-waste workers at Agbogbloshie in Ghana-the largest informal e-waste dumpsite in West Africa-relative to workers not engaged in e-waste recycling. A standardized musculoskeletal discomfort questionnaire was administered to 176 e-waste workers (73 collectors, 82 dismantlers, and 21 burners) and 41 workers in a reference group. The number of body parts with musculoskeletal discomfort were 1.62 and 1.39 times higher for collectors and dismantlers than burners, respectively. A 1-week discomfort prevalence was highest for collectors (91.8%) followed by dismantlers (89%), burners (81%), and the reference group (70.7%). The discomfort prevalence for e-waste workers was highest in the lower back (65.9%), shoulders (37.5%), and knees (37.5%). Whole-body pain scores (mean ± SE) were higher for collectors (83.7 ± 10.6) than dismantlers (45.5 ± 7.6), burners (34.0 ± 9.1), and the reference group (26.4 ± 5.9). Differences in prevalence, location, and intensity of MSD symptoms by the e-waste job category suggest specific work-related morbidity. Symptom prevalence and intensity call attention to the high risk for MSDs and work disability among informal e-waste workers, particularly collectors and dismantlers.

MAPO Method to Assess the Risk of Patient Manual Handling in Hospital Wards: A Validation Study

OBJECTIVE

To validate the effectiveness of MAPO method (Movement and Assistance of Hospital Patient) after the introduction of some changes to improve assessment objectivity.

BACKGROUND

The number of operators exposed to patient manual handling is increasing considerably. MAPO, proposed in 1999 as a useful tool to estimate the risk of patient manual handling, is a method characterized by analytical quickness. It has recently been improved to better match the 2012 ISO (International Organization for Standardization) technical report.

METHODS

A multicenter study was conducted between 2014 and 2016 involving 26 Italian hospitals in the Apulia Region. MAPO method was used to assess the risk of patient manual handling in 116 wards. A total of 1,998 exposed subjects were evaluated for the presence or absence of acute low back pain in the previous 12 months.

RESULTS

Only 12% of the investigated wards fell in the green exposure level (MAPO index = 0.1-1.5), 37% resulted in the average exposure level (MAPO index = 1.51-5) and the remaining 51% in the higher exposure level (MAPO index >5). The results confirmed a positive association between increasing levels of MAPO index and the number of episodes of acute low back pain (adjusted p trend = .001).

CONCLUSION

The improvements made over the past years led to a more objective assessment procedure. Despite the changes, the study confirmed the effectiveness of MAPO method to predict low back pain.

APPLICATION

MAPO method is an accurate risk assessment tool that identifies and evaluates workplace risks. The proper application of the method significantly improves working conditions.

A comparative study of in-field motion capture approaches for body kinematics measurement in construction

Due to physically demanding tasks in construction, workers are exposed to significant safety and health risks. Measuring and evaluating body kinematics while performing tasks helps to identify the fundamental causes of excessive physical demands, enabling practitioners to implement appropriate interventions to reduce them. Recently, non-invasive or minimally invasive motion capture approaches such as vision-based motion capture systems and angular measurement sensors have emerged, which can be used for in-field kinematics measurements, minimally interfering with on-going work. Given that these approaches have pros and cons for kinematic measurement due to adopted sensors and algorithms, an in-depth understanding of the performance of each approach will support better decisions for their adoption in construction. With this background, the authors evaluate the performance of vision-based (RGB-D sensor-, stereovision camera-, and multiple camera-based) and an angular measurement sensor-based (i.e., an optical encoder) approach to measure body angles through experimental testing. Specifically, measured body angles from these approaches were compared with the ones obtained from a marker-based motion capture system that has less than 0.1 mm of errors. The results showed that vision-based approaches have about 5–10 degrees of error in body angles, while an angular measurement sensor-based approach measured body angles with about 3 degrees of error during diverse tasks. The results indicate that, in general, these approaches can be applicable for diverse ergonomic methods to identify potential safety and health risks, such as rough postural assessment, time and motion study or trajectory analysis where some errors in motion data would not significantly sacrifice their reliability. Combined with relatively accurate angular measurement sensors, vision-based motion capture approaches also have great potential to enable us to perform in-depth physical demand analysis such as biomechanical analysis that requires full-body motion data, even though further improvement of accuracy is necessary. Additionally, understanding of body kinematics of workers would enable ergonomic mechanical design for automated machines and assistive robots that helps to reduce physical demands while supporting workers' capabilities.

Variable Lifting Index for Manual-Lifting Risk Assessment: A Preliminary Validation Study

OBJECTIVE

The aim of this study was to evaluate the efficacy of the new Variable Lifting Index (VLI) method, theoretically based on the Revised National Institute for Occupational Safety and Health [NIOSH] Lifting Equation (RNLE), in predicting the risk of acute low-back pain (LBP) in the past 12 months.

BACKGROUND

A new risk variable termed the VLI for assessing variable manual lifting has been developed, but there has been no epidemiological study that evaluates the relationship between the VLI and LBP.

METHOD

A sample of 3,402 study participants from 16 companies in different industrial sectors was analyzed. Of the participants, 2,374 were in the risk exposure group involving manual materials handling (MMH), and 1,028 were in the control group without MMH. The VLI was calculated for each participant in the exposure group using a systematic approach. LBP information was collected by occupational physicians at the study sites. The risk of acute LBP was estimated by calculating the odds ratio (OR) between levels of the risk exposure and the control group using a logistic regression analysis. Both crude and adjusted ORs for body mass index, gender, and age were analyzed.

RESULTS

Both crude and adjusted ORs showed a dose-response relationship. As the levels of VLI increased, the risk of LBP increased. This risk relationship existed when VLI was greater than 1.

CONCLUSION

The VLI method can be used to assess the risk of acute LBP, although further studies are needed to confirm the outcome and to define better VLI categories.

Prevalence of low back pain and associated factors among farmers during the rice transplanting process

[Purpose] The aim of this study was to investigate the prevalence of low back pain and associated factors in Thai rice farmers during the rice transplanting process. [Subjects and Methods] Three hundred and forty-four farmers, aged 20–59 years old, were asked to answer a questionnaire modified from the Standard Nordic Questionnaire (Thai version). The questionnaire sought demographic, back-related, and psychosocial data. [Results] The results showed that the prevalence of low back pain was 83.1%. Farmers younger than 45 years old who worked in the field fewer than six days were more likely to experience low back pain than those who worked for at least six days. Farmers with high stress levels were more likely to have low back pain. [Conclusion] In the rice transplanting process, the low back pain experienced by the farmers was associated with the weekly work duration and stress.

Trunk postures and upper-body muscle activations during physically demanding wildfire suppression tasks

This study examined the trunk postures and upper-body muscle activations during four physically demanding wildfire suppression tasks. Bilateral, wireless surface electromyography was recorded from the trapezius and erector spinae muscles of nine experienced, wildfire fighters. Synchronised video captured two retroreflective markers to allow for quantification of two-dimensional sagittal trunk flexion. In all tasks, significantly longer time was spent in the mild and severe trunk flexion (p ≤ 0.002) compared to the time spent in a neutral posture. Mean and peak muscle activation in all tasks exceeded previously established safe limits. These activation levels also significantly increased through the performance of each task (p < 0.001). The results suggest that the wildfire suppression tasks analysed impose significant musculoskeletal demand on firefighters. Fire agencies should consider developing interventions to reduce the exposure of their personnel to these potentially injurious musculoskeletal demands.

MAPO index for risk assessment of patient manual handling in hospital wards: a validation study

Manual handling of disabled patients - as regards movement - is one of the major factors affecting acute low back pain of exposed nursing staff. In the absence of quantitative methods assessing this kind of risk, the Research Unit Ergonomics of Posture and Movement of Milan developed in 1997 a risk assessment method called Movement and Assistance of Hospital Patients (MAPO), which is applicable in hospital wards.A first study conducted in 1999 allowed the identification of three levels of MAPO index corresponding with increasing probabilities of being affected by acute low back pain. In accordance with the well-known traffic light model, for MAPO index values between 0 and 1.5 the risk is considered to be absent or negligible. For values between 1.51 and 5.00 the risk is considered to be moderate. For values exceeding 5.00 the risk is considered to be high. In view of the limitations of the previous study, the results needed confirmation and so, in 2000-2001, another cross-sectional study was carried out, which included 191 hospital wards for acute and chronic patients and 2603 exposed subjects. This paper presents the analytical results of the association between the MAPO index and acute low back pain in this new data sample. The agreement between results of the two studies indicates that the MAPO index can be used as a risk index, although with some caution, as detailed in the paper. It can assess the risk exposure level of patient manual handling in wards and can be a useful tool for planning effective preventive actions to reduce the risk of work-related musculoskeletal disorders in health-care workers looking after disabled patients.