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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/871

Title: The use of EMG for load prediction during manual lifting
Authors: Chan, Sonya

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Keywords: EMG
Manual lifting
Load prediction
Issue Date: 2007
Series/Report no.: Canadian theses
Abstract: The Ergonomics Research Group at Queen’s University, supported by the Workplace and Safety Insurance Board, has been developing an on-line system to estimate peak and cumulative joint loading in the workplace. This study will aid the project by examining the muscle activation levels (MALs) in upper extremity and trunk muscles during a manual lifting task using both hands. It was hypothesized that MAL’s are correlated with the magnitude of the load in the hands and thus could be used to predict the load which in turn will be used to predict the lower back moments. Alterations in the muscle activation patterns due to lifting different loads were examined. Electromyographic signals (EMG) and kinematic data were recorded from different sites on the trunk and upper limb as subjects lifted a load from the floor to a shelf using squat, stoop and freestyle lift techniques. All raw EMG data were processed to obtain the linear envelopes (LE) which provides estimates of the MAL’s. The peak, mean and area of the linear envelopes were calculated. Using regression analysis, a relationship between the parameters and load lifted was found to exist. A non-linear parallel cascade type architecture was used to develop a model to predict the load in the hands. The model uses the EMG parameters as inputs and fits the data via linear and non-linear cascades to the output, i.e. the load in the hands. A model was successfully developed for the squat lift posture using the area, peak and mean of the zero-normalized EMG LE recorded from the erector spinae (L4 level), with a prediction error of ± 1.03kg and for the stoop posture, a prediction error of ± 2.34kg. Given the predicted loads, moments in the lower back were computed using the method of Hof (1992).
Description: Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2007-09-28 16:15:23.077
URI: http://hdl.handle.net/1974/871
Appears in Collections:Queen's Graduate Theses and Dissertations
Department of Electrical and Computer Engineering Graduate Theses

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