Suchergebnisse

Objective: The objective was to evaluate the effect of wrist posture on incidence of wrist tendinosis in a prospective cohort of blue-collar workers. Background: Previous studies have identified awkward wrist posture as a risk factor for wrist tendinosis, though the magnitude of the relationship is unclear. Method: Workers ( N = 413) at four industries were followed for up to 28 months with questionnaires and physical examinations every 4 months. Individualized exposure assessments of wrist posture were based on video analysis to determine the wrist extension/flexion angle for up to four tasks. Posture measures were calculated while in "heavy pinch" (> 1 kg force), "heavy power grip" (> 4 kg force), and across "all grips." A proportional hazards model estimated the relationship between time-weighted average posture measures and incidence of dominant-side wrist tendinosis. Results: In a model based on tertiles of exposure, adjusted for age, gender, hand force, and repetition of exertions, risk of tendinosis more than doubled in the highest category (HR = 2.69, 95% CI = 1.01–7.21) across all grips. The relative risk was highest during heavy pinch (HR = 5.03, 95% CI = 0.74–34.05), though not statistically significant. Increased median wrist extension while in heavy power grip was protective (HR = 0.24, 95% CI = 0.06–0.94). Conclusion: In this study of production workers, median wrist flexion of more than 7°, across all grips, was associated with an increased risk of tendinosis. The protective findings on median wrist extension during power grip deserve further investigation. Work tasks and tools should be designed to prevent sustained wrist flexion, especially during tasks involving forceful pinch.

Objective The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle. Background Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate. Method Fourteen individuals participated in this experiment. sEMG data for five forearm muscles and force output data were collected. Calibration data (25, 50, 75, 100% of maximum voluntary contraction (MVC)) were used to train ANN models to predict hand posture (pinch versus grip) and force magnitude while performing tasks that varied load, repetition rate, and duty cycle. Results Across all participants, overall hand posture prediction accuracy was 79% (0.79 ± .08), whereas overall hand force prediction accuracy was 73% (0.73 ± .09). Accuracy ranged between 0.65 and 0.93 based on varying repetition rate and duty cycle. Conclusion Hand posture and force prediction were possible using sEMG and ANNs, though there were important differences in the accuracy of predictions based on task characteristics including duty cycle and repetition rate. Application The results of this study could be applied to the development of a dosimeter used for distal upper extremity biomechanical exposure measurement, risk assessment, job (re)design, and return to work programs.