Jung, Hyunwoo
(Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 16419, Republic of Korea)
,
Choi, Ahnryul
(Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 16419, Republic of Korea)
,
Moon, Jose
(Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 16419, Republic of Korea)
,
Chae, Seung Heon
(Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 16419, Republic of Korea)
,
Lee, Kyungsuk
(Agricultural Health and Safety Division, Rural Development Administration, 25601, Republic of Korea)
,
Kim, Kyungran
(Agricultural Health and Safety Division, Rural Development Administration, 25601, Republic of Korea)
,
Mun, Joung Hwan
(Department of Bio-Mechatronic Engineering, College of Biotechnology and Bioengineering, Sungkyunkwan University, 16419, Republic of Korea)
Most agricultural workers are exposed to musculoskeletal disorders due to the characteristics of agricultural work performed manually. As observational methods to prevent musculoskeletal disorders, a cube method has been proposed that considers the risk factors of posture, time and force workload si...
Most agricultural workers are exposed to musculoskeletal disorders due to the characteristics of agricultural work performed manually. As observational methods to prevent musculoskeletal disorders, a cube method has been proposed that considers the risk factors of posture, time and force workload simultaneously. However, force workload could evaluate using the weight of an object or qualitative measurement to prevent interfering with a worker’s occupation. The purpose of this study is to propose a novel method for evaluating quantitatively the risk factor of force in agricultural field using insole system and artificial neural network model. Agricultural simulated experiments were performed on ten healthy adult males and six observers were recruited to evaluate the risk factors of force for the experiments. The model was constructed using the signals measured in the insole system and the consensus among observers about evaluation results. To verify the performance of the model, the performance measurement was calculated using 10-fold cross-validation. The results of the proposed method are compared with those of the observers to verify reproducibility and usefulness. The model showed more than 97% prediction accuracy in all risk levels, and the proposed method showed 1.59%, 0.99 and 0.98 in the coefficient of variation, proportion agreement index, Cohen’s kappa coefficient, and high reproducibility and usefulness when compared with the observers’ evaluation. The method of quantitatively evaluating the risk factor of force proposed in this study is possible to be applied to various agricultural works using observational methods.
Most agricultural workers are exposed to musculoskeletal disorders due to the characteristics of agricultural work performed manually. As observational methods to prevent musculoskeletal disorders, a cube method has been proposed that considers the risk factors of posture, time and force workload simultaneously. However, force workload could evaluate using the weight of an object or qualitative measurement to prevent interfering with a worker’s occupation. The purpose of this study is to propose a novel method for evaluating quantitatively the risk factor of force in agricultural field using insole system and artificial neural network model. Agricultural simulated experiments were performed on ten healthy adult males and six observers were recruited to evaluate the risk factors of force for the experiments. The model was constructed using the signals measured in the insole system and the consensus among observers about evaluation results. To verify the performance of the model, the performance measurement was calculated using 10-fold cross-validation. The results of the proposed method are compared with those of the observers to verify reproducibility and usefulness. The model showed more than 97% prediction accuracy in all risk levels, and the proposed method showed 1.59%, 0.99 and 0.98 in the coefficient of variation, proportion agreement index, Cohen’s kappa coefficient, and high reproducibility and usefulness when compared with the observers’ evaluation. The method of quantitatively evaluating the risk factor of force proposed in this study is possible to be applied to various agricultural works using observational methods.
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