Rapid upper limb assessment method based on adaptive neuro fuzzy inference system optimization
Traditional methods have low sensitivity to changes in input variables for the risk assessment of work-relat-ed musculoskeletal diseases,resulting in insufficient accuracy and reliability of the risk assessment outputs.To con-duct human factors engineering risk assessment in a more accurate way,a Rapid Upper Limb Assessment(RULA)method was proposed based on Adaptive Neuro Fuzzy Inference System(ANFIS).Based on the convolutional neural network,the key points of the human working posture were detected and recognized in the video,with the joint an-gles calculated.Because of the ANFIS,the method was improved in the rapid upper limb assessment,and a risk as-sessment framework was constructed for work-related musculoskeletal diseases to solve the problem of obtaining the same score when different postures were evaluated.The joint angle data of different working postures were random-ly selected to train and test the network,and it was adjusted the optimal parameters of the work-related musculo-skeletal disease risk prediction model according to the ANFIS as well as the rapid upper limb assessment method.The first 15 working postures in the joint angle dataset were selected for correlation verification,whose results were compared with those of the original rapid upper limb assessment method.Also,the operation process of the branch pruning tool was applied to analyze the case to achieve real-time dynamic assessment of risk scores.The results showed that the optimized rapid upper limb assessment method was more sensitive than the original one,which veri-fied that the adaptive neuro fuzzy inference system could effectively improve the rapid upper limb assessment method and predict risk scores in real time.
NETL
NSTL
万方数据
