No abstract

(1) Background: Yearly, more than 40% of the European employees suffer from work-related musculoskeletal disorders. Still, ergonomic guidelines defining optimal lifting techniques to decrease work-related musculoskeletal disorders (WMSDs) has not been unambiguously defined. Therefore, this study investigates if recommended squat lifting imposes lower musculoskeletal loading than stoop lifting while using a complex full body musculoskeletal OpenSim model. (2) Methods: Ten healthy participants lifted two different weights using both lifting techniques. 3D marker trajectories and ground reaction forces were used as input to calculate joint angles, moments and power using a full body musculoskeletal model with articulated lumbar spine. In addition, the muscle activity of nine different muscles was measured to investigate muscle effort when lifting. (3) Results: Peak moments and peak joint power in L5S1 were not different between the squat and the stoop, but higher peak moments and peak power in the hip, knee, elbow and shoulder were found during squat lifting. Moment impulses in L5S1 were higher during stoop lifting. This is reflected in higher peak electromyography (EMG) but lower muscle effort in prior described muscles during the squat. (4) Conclusions: Squat lifting imposes higher peak full body musculoskeletal loading but similar low back loading compared to stoop lifting, as reflected in peak moments, peak power, and peak EMG.

Inertial capture (InCap) systems combined with musculoskeletal (MSK) models are an attractive option for monitoring 3D joint kinematics in an ecological context. However, the primary limiting factor is the sensor-to-segment calibration, which is crucial to estimate the body segment orientations. Walking, running, and stair ascent and descent trials were measured in eleven healthy subjects with the Xsens InCap system and the Vicon 3D motion capture (MoCap) system at a self-selected speed. A novel integrated method that combines previous sensor-to-segment calibration approaches was developed for use in a MSK model with three degree of freedom (DOF) hip and knee joints. The following were compared: RMSE, range of motion (ROM), peaks, and R2 between InCap kinematics estimated with different calibration methods and gold standard MoCap kinematics. The integrated method reduced the RSME for both the hip and the knee joints below 5°, and no statistically significant differences were found between MoCap and InCap kinematics. This was consistent across all the different analyzed movements. The developed method was integrated on an MSK model workflow, and it increased the sensor-to-segment calibration accuracy for an accurate estimate of 3D joint kinematics compared to MoCap, guaranteeing a clinical easy-to-use approach.

No abstract

This paper presents a framework for recognition and prediction of ongoing human motions. The predictions generated by this framework could be used in a controller for a robotic device, enabling the emergence of intuitive and predictable interactions between humans and a robotic collaborator. The framework includes motion onset detection, phase speed estimation, intent estimation and conditioning. For recognition and prediction of a motion, the framework makes use of a motion model database. This database contains several motion models learned using the probabilistic Principal Component Analysis (PPCA) method. The proposed framework is evaluated with joint angle trajectories of eight subjects performing squatting, stooping and lifting tasks. The motion onset and phase speed estimation modules are first evaluated separately. Next, an evaluation of the full framework provides more insight in the current challenges regarding motion prediction. A brief comparison between PPCA and the Probabilistic Movement Primitives (ProMP) method for learning motion models is made based on the influence of both methodologies on the performance of the framework. Both PPCA and ProMP motion models are able to predict motions over a short time horizon but struggle to predict motions over a longer horizon.