Effect of personalized spinal profile on its biomechanical response in an EMG-assisted optimization musculoskeletal model of the trunk.

Recent developments in musculoskeletal (MS) modeling have been geared towards model customization. Personalization of the spine profile could affect estimates of spinal loading and stability, particularly in the upright standing posture where large inter-subject variations in the lumbar lordosis have been reported. This study investigates the biomechanical consequences of changes in the spinal profile. In 31 participants (healthy and with back pain), (1) the spine external profile was measured, (2) submaximal contractions were recorded in a dynamometer to calibrate the EMG-driven MS model and finally (3) static lifting in the upright standing challenging spine stability while altering load position and magnitude were considered. EMG signals of 12 trunk muscles and angular kinematics of 17 segments were recorded. For each participant, the MS model was constructed using either a generic or a personalized spinal profile and 17 biomechanical outcomes were computed, including individual muscle forces, ratios of muscle group forces, spinal loading and stability parameters. According to the ANOVA results and corresponding effect sizes, personalizing the spine profile induced medium and large effects on about half MS model outcomes related to the trunk muscle forces and negligible to small effects on spinal loading and stability as more aggregate outcomes. These effects are explained by personalized spine profiles that were a little more in extension as well as more pronounced spine curvatures (lordosis and kyphosis). These findings suggest that spine profile personalization should be considered in MS spine modeling as it may impact muscle force prediction and spinal loading.

Validation of a low-cost inertial motion capture system for whole-body motion analysis.

While some low-cost inertial motion capture (IMC) systems are now commercially available, generally, they have not been evaluated against gold standard optical motion capture (OMC). The objective was to validate the low-cost Neuron IMC system with OMC. Whole-body kinematics were recorded on five healthy subjects during manual handling of boxes for about 32 min while wearing 17 magnetic and inertial measurement units with Optotrak clusters serving as a reference. The kinematical model was calibrated anatomically for OMC and with poses for IMC. Local coordinate systems were aligned with angular velocities to dissociate differences due to technology or kinematical model. Descriptive statistics including the root mean square error (RMSE), coefficient of multiple correlation (CMC) and limits of agreement (LoA) were applied to the joint angle curves. The average technological error yielded 5.8° and 4.9° for RMSE, 0.87 and 0.96 for CMC and 0.4 ± 8.6° and -0.3 ± 6.0° for LoA about the frontal and transverse axes respectively, whereas the longitudinal axis yielded 10.5° for RMSE, 0.78 for CMC and 3.3 ± 13.1° for LoA. Differences due to technology and to the model contributed similarly to the total difference between IMC and OMC. For many joints and axes, RMSE stayed under 5°, CMC over 0.9 and LoA under 10°, especially for the transverse axis and lower limb. The Neuron low-cost IMC system showed potential for tracking complex human movements of long duration in a normal laboratory environment with a certain error level that may be suitable for many applications involving large IMC distribution.

A machine-learning method for classifying and analyzing foot placement: Application to manual material handling.

Foot placement strategy is an essential aspect in the study of movement involving full body displacement. To get beyond a qualitative analysis, this paper provides a foot placement classification and analysis method that can be used in sports, rehabilitation or ergonomics. The method is based on machine learning using a weighted k-nearest neighbors algorithm. The learning phase is performed by an observer who classifies a set of trials. The algorithm then automatically reproduces this classification on subsequent sets. The method also provides detailed analysis of foot placement strategy, such as estimating the average foot placements for each class or visualizing the variability of strategies. An example of applying the method to a manual material handling task demonstrates its usefulness. During the lifting phase, the foot placements were classified into four groups: front, contralateral foot behind, ipsilateral foot behind, and parallel. The accuracy of the classification, assessed with a holdout method, is about 97%. In this example, the classification method makes it possible to observe and analyze the handler's foot placement strategies with regards to the performed task.

Difference between male and female workers lifting the same relative load when palletizing boxes.

A few biomechanical studies have contrasted the work techniques of female and male workers during manual material handling (MMH). A recent study showed that female workers differed from males mostly in the strategy they used to lift 15-kg boxes from the ground, especially regarding task duration, knee and back postures and interjoint coordination. However, the lifting technique difference observed in females compared to males was perhaps due to a strength differences. The objective of this study was to test whether female workers would repeat the same lifting technique with a load adjusted to their overall strength (females: 10 kg; males: 15 kg), which can be considered a "relative load" since the overall back strength of females is 2/3 that of males. The task for the participants consisted in transferring boxes from one pallet to another. A dynamic 3D linked segment model was used to estimate the net moments at L5/S1, and different kinematic variables were considered. The results showed that the biomechanics of the lifting techniques used by males and females were similar in terms of task duration and cumulative loading, but different in terms of interjoint coordination pattern. The sequential interjoint coordination pattern previously seen in females with an absolute load (15 kg) was still present with the relative load, suggesting the influence of factors more intrinsically linked to sex. Considering that the female coordination pattern likely stretched posterior passive tissues when lifting boxes from the ground, potentially leading to higher risk of injury, the reason for this sex effect must be identified so that preventive interventions can be proposed.

Validation of four major algorithms for estimating the instantaneous helical axis with miniature triaxial gyroscope.

This work presents an experimental validation study of four major algorithms for estimating the instantaneous helical axis parameters for rigid-body motion. The angular velocity vector was first estimated from landmarks trajectories by four methods and compared to the measured one by a miniature triaxial gyroscope. It was found that the four methods are equivalent, that the estimated angular velocity closely matches the measured one and increasing the number of markers have the effect of smoothing the helical parameters.