Effects on efficiency in repetitive lifting of load and frequency combinations at a constant total power output

Modeling the Metabolic Reductions of a Passive Back-Support Exoskeleton

Despite several attempts to quantify the metabolic savings resulting from the use of passive back-support exoskeletons (BSEs), no study has modeled the metabolic change while wearing an exoskeleton during lifting. The objectives of this study were to: 1) quantify the metabolic reductions due to the VT-Lowe's exoskeleton during lifting; and 2) provide a comprehensive model to estimate the metabolic reductions from using a passive BSE. In this study, 15 healthy adults (13M, 2F) of ages 20 to 34 years (mean=25.33, SD=4.43) performed repeated freestyle lifting and lowering of an empty box and a box with 20% of their bodyweight. Oxygen consumption and metabolic expenditure data were collected. A model for metabolic expenditure was developed and fitted with the experimental data of two prior studies and the without-exoskeleton experimental results. The metabolic cost model was then modified to reflect the effect of the exoskeleton. The experimental results revealed that VT-Lowe's exoskeleton significantly lowered the oxygen consumption by ~9% for an empty box and 8% for a 20% bodyweight box, which corresponds to a net metabolic cost reduction of ~12% and ~9%, respectively. The mean metabolic difference (i.e., without-exo minus with-exo) and the 95% confidence interval were 0.36 and (0.2-0.52) [Watts/kg] for 0% bodyweight, and 0.43 and (0.18-0.69) [Watts/kg] for 20% bodyweight. Our modeling predictions for with-exoskeleton conditions were precise, with absolute freestyle prediction errors of <2.1%. The model developed in this study can be modified based on different study designs, and can assist researchers in enhancing designs of future lifting exoskeletons.

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.

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.

Load knowledge affects low-back loading and control of balance in lifting tasks

This study investigated the effect of the presence or absence of load knowledge on the low-back loading and the control of balance in lifting tasks. Low-back loading was quantified by the net sagittal plane torque at the lumbo-sacral joint. The control of balance was studied by the position of the centre of gravity relative to the base of support, the horizontal and vertical momentum of the centre of gravity and the angular momentum of the whole body. In a first experiment, 8 male subjects lifted a rather heavy load (22% of body mass), using a leglift and a backlift, while they were familiar with the load mass. To counteract the threat to balance, imposed by picking up a load in front of the body, the subjects performed specific preparations, based upon the known load mass; prior to load pick-up, profound changes in the horizontal and angular momentum were found. The preparations were technique specific. Preserving balance seemed easier while picking up a load with a backlift than with a leglift. In the second experiment, 25 male subjects lifted a 6 kg box, which they expected to be 16 kg, because, in a series of lifts, the load mass was changed from 16 to 6 kg without their knowledge. Despite the 10 kg difference in actual load mass, the net torque at the lumbo-sacral joint was not different between lifting 6 and 16 kg, until 150 ms after box lift-off. Moreover, lifting of the overestimated load mass caused a disturbance of balance in 92% of the trials. The postural reactions aimed at regaining balance were not accompanied by an increased low-back loading. It was concluded that the absence of load knowledge, and the following overestimation of the load mass to be lifted, lead to an increased mechanical load on the lumbar spine and to an increased risk of losing balance in lifting tasks. Both events may contribute to a higher risk of low-back injury in manual materials handling tasks.

Dissociated oxygen uptake response to an incremental intermittent repetitive lifting and lowering exercise in humans

Five subjects performed a maximal exercise test of repetitive lifting and lowering, with a discontinuous protocol of incremental exercise (3 min) and relative rest (2 min). Exercise periods consisted of repetitive lifting and repetitive lifting and lowering at increasing movement frequencies. Relative rest periods consisted of ergometer cycling at a constant, low power output. An unexpected, dissociated, response of cardiovascular and pulmonary parameters was found: during relative rest, values for oxygen uptake, carbon dioxide production, pulmonary ventilation and tidal volume were significantly higher than during the preceding exercise periods, though exercise intensity was much lower. To our knowledge, such a response has not been reported in previous studies. Since the response could not be attributed to methodological or technical factors, it is hypothesized that the type of exercise itself impeded the optimal performance of the oxygen transporting system. The function of the pulmonary system could have been influenced by a high intra-abdominal pressure, the involvement of respiratory muscles in stabilizing trunk and head, a flexed trunk posture and the entrainment of respiratory frequency with movement frequency. More likely, the function of the cardiovascular system was hindered by a high blood pressure and high intramuscular pressures. Since this response occurred at low exercise intensities, optimal functioning of the cardiovascular and pulmonary system during daily activities of repetitive lifting and lowering could similarly be impeded. The hypotheses put forward could also explain the lower peak oxygen uptake reported during repetitive lifting, compared to running and cycling.

Mechanically corrected EMG for the continuous estimation of erector spinae muscle loading during repetitive lifting

Few studies have been carried out on the changes in biomechanical loading on low-back tissues during prolonged lifting. The purpose of this paper was to develop a model for continuously estimating erector spinae muscle loads during repetitive lifting and lowering tasks. The model was based on spine kinematics and bilateral lumbar and thoracic erector spinae electromyogram (EMG) signals and was developed with the data from eight male subjects. Each subject performed a series of isometric contractions to develop extensor moments about the low back. Maximum voluntary contractions (MVCs) were used to normalize all recorded EMG and moment time-histories. Ramp contractions were used to determine the non-linear relationship between extensor moments and EMG amplitudes. In addition, the most appropriate low-pass filter cut-off frequencies were calculated for matching the rectified EMG signals with the moment patterns. The mean low-pass cut-off frequency was 2.7 (0.4) Hz. The accuracy of the non-linear EMG-based estimates of isometric extensor moment were tested with data from a series of six rapid contractions by each subject. The mean error over the duration of these contractions was 9.2 (2.6)% MVC. During prolonged lifting sessions of 20 min and of 2 h, a model was used to calculate changes in muscle length based on monitored spine kinematics. EMG signals were first processed according to the parameters determined from the isometric contractions and then further processed to account for the effects of instantaneous muscle length and velocity. Simple EMG estimates were found to underestimate peak loading by 9.1 (4.0) and 25.7 (11.6)% MVC for eccentric and concentric phases of lifting respectively, when compared to load estimates based on the mechanically corrected EMG. To date, the model has been used to analyze over 5300 lifts.