ScholarWorks@성균관대학교

초록

Objective: This study aims to comprehensively evaluate the predictive validity and practical applicability of the AnyBody Modeling System (AMS) by comparing muscle loads during parcel loading tasks across different types of delivery trucks (conventional, low-floor, and newly designed) using both EMG and AMS analyses. Background: With the growth of the logistics industry and the rise in contactless consumption, the demand for parcel delivery has surged, leading to an increased workload for delivery workers. To mitigate this burden, a newly designed delivery vehicle has been proposed, which needs a quantitative ergonomic evaluation of the newly designed truck's effectiveness in reducing muscle load. While EMG analysis has been widely used for such assessments through experimental studies, simulationbased analysis using the AMS has recently emerged as an alternative method for predicting muscle load without the need for using EMG muscle activity analyses. Despite its potential, further validation is required to determine whether the AMS method can fully replace the conventional EMG analysis method. Method: Twenty healthy male participants with experience in parcel delivery were recruited for this study, and EMG and AMS data from five of them were selected for analysis. Muscle activities of the erector spinae (ES), upper trapezius (UT), vastus lateralis (VL), biceps femoris (BF), tibialis anterior (TA), and lateral gastrocnemius (GASL) were measured using an EMG system. Additionally, the Xsens IMU motion capture system was used to provide posture input data for AMS modeling. Participants performed a loading task, carrying two 5kg boxes. Statistical analysis was conducted using the Kruskal-Wallis test and Dunn's post hoc test. Results: EMG analysis showed that the erector spinae (ES) and biceps femoris (BF) exhibited the highest muscle activation, while the upper trapezius (UT) showed the lowest, with statistically significant differences among muscle types (p<0.05). Although no significant main effect was found for truck type, muscle activation was generally higher in low-floor trucks. AMS analysis revealed that ES had the highest muscle activation at both the 50th and 90th percentiles, aligning with EMG trends. However, unlike EMG, AMS estimated the lowest activation in the calf muscles (GASL and TA). Regarding the interaction effects between truck type and muscle type, both EMG and AMS analyses at the 50th percentile showed that the newly designed truck exhibited the lowest activation for ES and BF. Conversely, for UT at the 50th percentile and TA at the 90th percentile, the newly designed truck showed the highest activation, displaying similar trends in both analyses. However, for TA at the 50th percentile, EMG and AMS results exhibited different trends. Conclusion: This study confirmed that the newly designed truck may help reduce muscle load, particularly in the erector spinae (ES) and biceps femoris (BF), suggesting its potential to alleviate lower back and lower limb muscle strain for delivery workers. Additionally, while EMG and AMS analyses exhibited similar trends for some muscles, discrepancies were observed in others. This indicates that AMS could serve as an alternative method to EMG in specific applications; however, further research is required to enhance its predictive accuracy. Application: This study suggests that improving delivery truck design can contribute to reducing muscle loads in delivery workers, emphasizing the importance of incorporating ergonomic design factors. Additionally, it highlights the potential applicability of AMS while underscoring the need for further research to improve its predictive accuracy.

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