Enhancing Ergonomic Safety Effectiveness of Repetitive Job Activities: Prediction of Muscle Fatigue in Dominant and Nondominant Arms of Industrial Workers

Work?related musculoskeletal disorders are very common in the workplace, especially with tasks involving repetitive lifting and motions. Repetitive lifting of excessively heavy objects in the workplace could increase the severity and rates of work?related musculoskeletal disorders. In this article, the physiological effect of muscle fatigue on the dominant and nondominant arms of adult industrial workers performing various repetitive tasks was predicted using a muscular endurance model. Twenty?four (n = 24) industrial workers (18–45 years old) were randomly selected for this research. The effects of electromyography (EMG) were observed during incremental loading of 5–40 kg on the muscle of the dominant and nondominant arms of the subjects during static lifting activities. Results of the analysis showed that the endurance time decreased with the application of additional loads on the dominant and nondominant arms of all the subjects. This inverse relationship was used to predict the behavior of muscle fatigue. Additional findings indicated that workers performing repetitive lifting tasks could maintain maximum load capacities ranging from 20 to 30 kg. The acceptable maximum load capacity of 23 kg recommended by the National Institute for Occupational Safety and Health is within this range. The results obtained from this research could be used in the beginning steps of the efforts to reevaluate and reestablish guidelines and limits in the design of industrial jobs involving repetitive motion. © 2014 Wiley Periodicals, Inc.     

Multivariable process identification for mpc: the asymptotic method and its applications

In this work we will introduce the asymptotic method (ASYM) of identification and provide two case studies. The ASYM was developed for multivariable process identification for model based control. The method calculates time domain parametric models using frequency domain criterion. Fundamental problems, such as test signal design for control, model order/structure selection, parameter estimation and model error quantification, are solved in a systematic manner. The method can supply not only input/output model and unmeasured disturbance model which are asymptotic maximum likelihood estimates, but also the upper bound matrix for the model errors that can be used for model validation and robustness analysis. To demonstrate the use of the method for model predictive control (MPC), the identification of a Shell benchmark process (a simulated distillation column) and an industrial application to a crude unit atmospheric tower will be presented.