Risk assessments using the Strain Index and the TLV for HAL, Part I: Task and multi-task job exposure classifications

Assessment of work-related hand and elbow workloads using measurement-based TLV for HAL

Direct-measurement-based methods for assessing workloads of the hand or elbow in the field are rare. Aim of the study was to develop such a method based on the Threshold Limit Value for Hand Activity Level (TLV for HAL). Hence, HAL was quantified using kinematic data (mean power frequencies, angular velocities and micro-pauses) and combined with electromyographic data (root-mean-square values) in order to generate a measurement-based TLV for HAL (mTLV for HAL). The multi-sensor system CUELA including inertial sensors, potentiometers and a 4-channel surface electromyography module was used. For wrist and elbow regions, associations between mTLV for HAL and disorders/complaints (quantified by odds ratios (OR [95%-confidence interval])) were tested exploratively within a cross-sectional field study with 500 participants. Higher workloads were frequently significantly associated with arthrosis of distal joints (9.23 [3.29-25.87]), wrist complaints (2.89 [1.63-5.11]) or elbow complaints (1.99 [1.08-3.67]). The new method could extend previous application possibilities.

A computer vision approach for classifying isometric grip force exertion levels

Exposure to high and/or repetitive force exertions can lead to musculoskeletal injuries. However, measuring worker force exertion levels is challenging, and existing techniques can be intrusive, interfere with human-machine interface, and/or limited by subjectivity. In this work, computer vision techniques are developed to detect isometric grip exertions using facial videos and wearable photoplethysmogram. Eighteen participants (19-24 years) performed isometric grip exertions at varying levels of maximum voluntary contraction. Novel features that predict forces were identified and extracted from video and photoplethysmogram data. Two experiments with two (High/Low) and three (0%MVC/50%MVC/100%MVC) labels were performed to classify exertions. The Deep Neural Network classifier performed the best with 96% and 87% accuracy for two- and three-level classifications, respectively. This approach was robust to leave subjects out during cross-validation (86% accuracy when 3-subjects were left out) and robust to noise (i.e. 89% accuracy for correctly classifying talking activities as low force exertions). Practitioner summary: Forceful exertions are contributing factors to musculoskeletal injuries, yet it remains difficult to measure in work environments. This paper presents an approach to estimate force exertion levels, which is less distracting to workers, easier to implement by practitioners, and could potentially be used in a wide variety of workplaces. Abbreviations: MSD: musculoskeletal disorders; ACGIH: American Conference of Governmental Industrial Hygienists; HAL: hand activity level; MVC: maximum voluntary contraction; PPG: photoplethysmogram; DNN: deep neural networks; LOSO: leave-one-subject-out; ROC: receiver operating characteristic; AUC: area under curve.

Sex differences in muscle activity and motor variability in response to a non-fatiguing repetitive screwing task

Background

Musculoskeletal disorders are more prevalent among women than among men, which may be explained by aspects of motor control, including neuromuscular requirements and motor variability. Using an exploratory approach, this study aimed to evaluate sex differences in neuromuscular responses and motor variability during a repetitive task performed on 3 days.

Methods

Thirty women and 27 men performed the non-fatiguing, repetitive, 1-h screwing task. For neuromuscular responses, the mean and difference values of static, median, and peak percentile muscle activity levels (normalized to a reference voluntary contraction force) and, for motor variability, the mean and difference values of relative and absolute cycle-to-cycle variability across days were compared between both sexes for each muscle. A mixed-design analysis of variance was used to assess differences between both sexes.

Results

The non-fatiguing character of the screwing task was confirmed by the absence of decreased force levels in maximal voluntary contractions performed before and after the task and by absence of electromyographic signs of muscle fatigue. The static and median muscle activity levels tended to be higher among women (on average 7.86 and 27.23 %RVE) than men (on average 6.04 and 26.66 %RVE). Relative motor variability of the flexor and biceps muscles and absolute motor variability of both upper arm muscles were lower in women (on average 0.79 and 29.70 %RVE) than in men (on average 0.89 and 37.55 %RVE). The median activity level of both upper arms muscles tended to decrease within days among women (on average - 2.63 %RVE) but increase among men (on average + 1.19 %RVE). Absolute motor variability decreased within days among women (on average - 5.32 to - 0.34%RVE), whereas it tended to decrease less or increase within days among men (on average - 1.21 to + 0.25 %RVE).

Conclusion

Women showed higher levels of muscle activity and lower initial relative and absolute motor variability than males when performing the same occupational task, implying women may have a higher risk for developing disorders and point to both sexes using different intrinsic motor control strategies in task performance. Clearly, biological aspects alone cannot explain why women would be at higher risk for developing disorders than men. Therefore, a wider range of individual and environmental factors should be taken into account for optimizing work station designs and organizations by taking into account sex differences.

Health risk assessment on musculoskeletal disorders among potato-chip processing workers

Musculoskeletal disorders (MSDs) are the most common complaint among industrial workers. The potato-chip processing industry involves workers in repetitive activities leading to MSDs. This cross-sectional descriptive study aimed to assess MSDs health risk among potato-chip processing workers. It was conducted among 107 randomly sampled workers from a distribution like other groups exposed to similar ergonomics factors. A MSDs health-risk assessment produced a matrix of combined results based on a self-report questionnaire (5 levels) and an ergonomics risk assessment using RULA (4 levels). The self-reported MSDs questionnaire showed that workers had moderate to very high discomfort levels, i.e., 11.21% trunk, 9.35% lower limbs, 8.41% upper limbs and 4.66% for the neck. Ergonomic risks were found to be at a very high level, 77.57%, and high risk level, 19.63%. The combined matrix assessments showed that most workers were at moderate to very high MSDs risk, i.e., 43.92% trunk, 36.45% upper limbs, 32.71% lower limbs and 20.56% for the neck. This health risk matrix found a higher proportion of workers presenting with MSDs health risk compared with the musculoskeletal disorders self-assessment alone. Therefore, the MSDs risk matrix assessment could be useful for surveillance screening prior to implementing a risk-reduction program. Further, using ergonomics training programs and improving work stations for high-risk groups are also recommended based on the ergonomic and health risk assessments in this study.

Risk assessments using the Strain Index and the TLV for HAL, Part II: Multi-task jobs and prevalence of CTS

The Strain Index (SI) and the American Conference of Governmental Industrial Hygienists (ACGIH) threshold limit value for hand activity level (TLV for HAL) have been shown to be associated with prevalence of distal upper-limb musculoskeletal disorders such as carpal tunnel syndrome (CTS). The SI and TLV for HAL disagree on more than half of task exposure classifications. Similarly, time-weighted average (TWA), peak, and typical exposure techniques used to quantity physical exposure from multi-task jobs have shown between-technique agreement ranging from 61% to 93%, depending upon whether the SI or TLV for HAL model was used. This study compared exposure-response relationships between each model-technique combination and prevalence of CTS. Physical exposure data from 1,834 workers (710 with multi-task jobs) were analyzed using the SI and TLV for HAL and the TWA, typical, and peak multi-task job exposure techniques. Additionally, exposure classifications from the SI and TLV for HAL were combined into a single measure and evaluated. Prevalent CTS cases were identified using symptoms and nerve-conduction studies. Mixed effects logistic regression was used to quantify exposure-response relationships between categorized (i.e., low, medium, and high) physical exposure and CTS prevalence for all model-technique combinations, and for multi-task workers, mono-task workers, and all workers combined. Except for TWA TLV for HAL, all model-technique combinations showed monotonic increases in risk of CTS with increased physical exposure. The combined-models approach showed stronger association than the SI or TLV for HAL for multi-task workers. Despite differences in exposure classifications, nearly all model-technique combinations showed exposure-response relationships with prevalence of CTS for the combined sample of mono-task and multi-task workers. Both the TLV for HAL and the SI, with the TWA or typical techniques, appear useful for epidemiological studies and surveillance. However, the utility of TWA, typical, and peak techniques for job design and intervention is dubious.