Determination of the effect of lift characteristics on dynamic performance profiles during manual materials handling tasks

In any quantitative gait or occupational biomechanics investigation, the quantification of the different kinematic, kinetic, and electromyographic parameters is essential towards assessment of functional capacity and development of a biomechanical profile of the task demands. In the current study, the authors presented a methodology for using inferential statistics to evaluate the effect of lift characteristics on phase-dependent and phase-independent variability in performance. Using a database of kinematic and kinetic profiles obtained from a manual lifting study, the phase-dependent effects of lift characteristics: box mass (load), mode (technique of lift), and speed (frequency of lift) were investigated through the use of analysis of variance (ANOVA) techniques, which recognize the vectorial constitution of the profiles. In addition, the Karhunen-Loeve Expansion (KLE) feature extraction method was used for representing the lifting patterns of measured joint angular position, velocity, acceleration, and net muscular torque profiles obtained from a 2-D biomechanical lifting model in order to study the phase-independent effects. In comparison to traditional descriptive statistical analyses currently used in various occupational biomechanics experimental investigations, this method allows the significant information content of the time varying signal to be captured, enhancing the sensitivity of subsequent hypothesis testing procedures. The application of this technique to MMH investigations allows identification of the lift characteristics that dominate the variability of task demands, hence aiding in the design and assessment of ergonomic solutions.