Feature extraction and quantification of the variability of dynamic performance profiles due to the different sagittal lift characteristics

Investigation of manual material handling (MMH) tasks, such as lifting, requires the quantification of the various kinematic and kinetic parameters of performance for assessment of the functional capacity and/or task demand profiles. Traditional statistical descriptive analyses usually involve computing the summary statistics (maximum, minimum, mean, and/or range) of the resulting performance parameters over the cycle duration (i.e., lifting/lowering cycle). Consequently, the significant information content of the time-varying signals is diminished, limiting the sensitivity of subsequent hypothesis testing procedures. The present study developed a methodology for representing and quantifying performance data variability of the kinematic and kinetic motion profiles due to the different lift characteristics (load, mode, and speed) during MMH tasks while capturing the temporal characteristics. Using a database of motion profiles from a manual lifting experiment, the Karhunen-Loeve Expansion (KLE) feature extraction technique was shown to be quite effective for representing the various motion profiles. The number of basis vectors (eigenvectors) and corresponding coefficients needed for accurate representation were substantially smaller than the original data set, resulting in data compression. Moreover, the effects of lift characteristics were investigated using analysis of variance techniques that recognize the vectorial constitution of the waveforms. The application of these techniques will enable the quantification of highly phasic profiles and enhance the ability to document the effect of intervening measures such as educational or physical training/exercise on the kinematic and kinetic patterns of performance. Additionally, the differential influence of lift characteristics on the variability of performance during different phases of lifting and lowering provides added resolution in the analysis of MMH tasks