title: A neuro-fuzzy model for estimating electromyographical activity of trunk muscles due to manual lifting.
abstract: The main objective of this study was to develop a hybrid neuro-fuzzy system for estimating the magnitude of EMG responses of 10 trunk muscles based on two lifting task variables (trunk velocity and trunk moment) as model inputs. The input and output variables were represented using the fuzzy membership functions. The initial fuzzy rules were generated by the neural network using true EMG data. Two different laboratory-derived EMG data sets were used for model development and validation, respectively. The mean absolute error (MAE) between the actual and model-estimated normalized EMG values was calculated. Across all muscles, the average value of MAE was 8.43% (SD=2.87%) of the normalized EMG data. The larger absolute errors occurred in the left side of the trunk, which exhibited higher levels of muscular activity. Overall, the developed model was capable of estimating the normalized EMG values with average value of the mean absolute differences of 6.4%. It was hypothesized that model performance could be improved by increasing the number of inputs, including additional task variables as well as the subjects' characteristics.
authors.affiliation: Department of Industrial Management, Kumoh National University of Technology,188 Shin-Pyung Dong, Kumi, South-Korea 730-701.
authors.forename: Wookgee
authors.initials: W
authors.lastname: Lee
authors.forename: Waldemar
authors.initials: W
authors.lastname: Karwowski
authors.forename: William S
authors.initials: WS
authors.lastname: Marras
authors.forename: David
authors.initials: D
authors.lastname: Rodrick
mesh_terms: D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004576:Electromyography; D006804:Ergonomics; D017143:Fuzzy Logic; D006801:Humans; D017770:Lifting; D008161:Lumbosacral Region / Q000502:physiology*; D008959:Models, Neurological; D009119:Muscle Contraction / Q000502:physiology*; D016571:Neural Networks, Computer; D012307:Risk Factors; D013647:Task Performance and Analysis; D013997:Time Factors