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Discomfort Evaluation of Truck Ingress/Egress Motions Based on Biomechanical Analysis.

Choi NC, Lee SH - Sensors (Basel) (2015)

Bottom Line: This paper presents a quantitative discomfort evaluation method based on biomechanical analysis results for human body movement, as well as its application to an assessment of the discomfort for truck ingress and egress.Next, the maximum voluntary contraction (MVC) ratios of the muscles were calculated through a biomechanical analysis of the musculoskeletal human model for the captured motion.The validation results showed that the correlation between the objective and subjective discomforts was significant and could be described by a linear regression model.

View Article: PubMed Central - PubMed

Affiliation: Intelligent HMI/CAD Lab, Graduate School of Automotive Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 136-702, Korea. naamus@dayou.co.kr.

ABSTRACT
This paper presents a quantitative discomfort evaluation method based on biomechanical analysis results for human body movement, as well as its application to an assessment of the discomfort for truck ingress and egress. In this study, the motions of a human subject entering and exiting truck cabins with different types, numbers, and heights of footsteps were first measured using an optical motion capture system and load sensors. Next, the maximum voluntary contraction (MVC) ratios of the muscles were calculated through a biomechanical analysis of the musculoskeletal human model for the captured motion. Finally, the objective discomfort was evaluated using the proposed discomfort model based on the MVC ratios. To validate this new discomfort assessment method, human subject experiments were performed to investigate the subjective discomfort levels through a questionnaire for comparison with the objective discomfort levels. The validation results showed that the correlation between the objective and subjective discomforts was significant and could be described by a linear regression model.

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Related in: MedlinePlus

Discomforts for the eighteen truck step conditions according to the step type, step count, and first step height: (a) maximum-%MVC discomfort and (b) average-%MVC discomfort.
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sensors-15-13568-f013: Discomforts for the eighteen truck step conditions according to the step type, step count, and first step height: (a) maximum-%MVC discomfort and (b) average-%MVC discomfort.

Mentions: The maximum- and average-%MVC discomforts according to the step type, step count, and step height are illustrated in the bar graphs in Figure 13. The discomfort has a decreasing tendency in the order of ladder, straight stair, and oblique stair. As the number of steps increases, the discomfort decreases, but the discomfort tends to increase as the height of the first step increases. These tendencies agree with the results of the survey of subjective discomfort, as described in Section 5. However, the decreasing rates are somewhat different from those of the subjective discomfort.


Discomfort Evaluation of Truck Ingress/Egress Motions Based on Biomechanical Analysis.

Choi NC, Lee SH - Sensors (Basel) (2015)

Discomforts for the eighteen truck step conditions according to the step type, step count, and first step height: (a) maximum-%MVC discomfort and (b) average-%MVC discomfort.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507608&req=5

sensors-15-13568-f013: Discomforts for the eighteen truck step conditions according to the step type, step count, and first step height: (a) maximum-%MVC discomfort and (b) average-%MVC discomfort.
Mentions: The maximum- and average-%MVC discomforts according to the step type, step count, and step height are illustrated in the bar graphs in Figure 13. The discomfort has a decreasing tendency in the order of ladder, straight stair, and oblique stair. As the number of steps increases, the discomfort decreases, but the discomfort tends to increase as the height of the first step increases. These tendencies agree with the results of the survey of subjective discomfort, as described in Section 5. However, the decreasing rates are somewhat different from those of the subjective discomfort.

Bottom Line: This paper presents a quantitative discomfort evaluation method based on biomechanical analysis results for human body movement, as well as its application to an assessment of the discomfort for truck ingress and egress.Next, the maximum voluntary contraction (MVC) ratios of the muscles were calculated through a biomechanical analysis of the musculoskeletal human model for the captured motion.The validation results showed that the correlation between the objective and subjective discomforts was significant and could be described by a linear regression model.

View Article: PubMed Central - PubMed

Affiliation: Intelligent HMI/CAD Lab, Graduate School of Automotive Engineering, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 136-702, Korea. naamus@dayou.co.kr.

ABSTRACT
This paper presents a quantitative discomfort evaluation method based on biomechanical analysis results for human body movement, as well as its application to an assessment of the discomfort for truck ingress and egress. In this study, the motions of a human subject entering and exiting truck cabins with different types, numbers, and heights of footsteps were first measured using an optical motion capture system and load sensors. Next, the maximum voluntary contraction (MVC) ratios of the muscles were calculated through a biomechanical analysis of the musculoskeletal human model for the captured motion. Finally, the objective discomfort was evaluated using the proposed discomfort model based on the MVC ratios. To validate this new discomfort assessment method, human subject experiments were performed to investigate the subjective discomfort levels through a questionnaire for comparison with the objective discomfort levels. The validation results showed that the correlation between the objective and subjective discomforts was significant and could be described by a linear regression model.

Show MeSH
Related in: MedlinePlus