Objectives Vertical jump performance (height) is a more representative metric for knee function than horizontal hop performance (distance) in healthy individuals. It is not known what the biomechanical status of athletes after anterior cruciate ligament (ACL) reconstruction (ACLR) is at the time they are cleared to return to sport (RTS) or whether vertical performance metrics better evaluate knee function. Methods Standard marker-based motion capture and electromyography (EMG) were collected from 26 male athletes cleared to RTS after ACLR and 22 control healthy subjects during single leg vertical jumps (SLJ) and single leg drop jumps (SLDJ). Performance outcomes, jump height and the Reactive Strength Index, were calculated. Sagittal plane kinematics, joint moments and joint work were obtained using inverse dynamics and lower limb muscle forces were computed using an EMG-constrained musculoskeletal model. Muscle contribution was calculated as a percentage of the impulse of all muscle forces in the model. Between-limb and between-group differences were explored using mixed models analyses. Results Jump performance, assessed by jump height and Reactive Strength Index, was significantly lower in the involved than the uninvolved limb and controls, with large effect sizes. For the ACLR group, jump height limb symmetry index was 83% and 77% during the SLJ and SLDJ, respectively. Work generation was significantly less in the involved knee compared to uninvolved limb and controls during the SLJ (p<0.001; d=1.19; p=0.003, d=0.91, respectively) and during the SLDJ (p<0.001; d=1.54; p=0.002, d=1.05, respectively). Hamstrings muscle contribution was greater in the involved compared to the uninvolved limb and controls, whereas soleus contribution was lower in the involved limb compared to controls. Conclusions During vertical jumps, male athletes after ACLR at RTS still exhibit knee biomechanical deficits, despite symmetry in horizontal functional performance and strength tests. Vertical performance metrics like jump height and RSI can better identify interlimb asymmetries than the more commonly used hop distance and should be included in the testing battery for the RTS.

Background We evaluated the lower limb status of athletes after anterior cruciate ligament reconstruction (ACLR) during the propulsion and landing phases of a single leg hop for distance (SLHD) task after they had been cleared to return to sport. We wanted to evaluate the biomechanical components of the involved (operated) and uninvolved legs of athletes with ACLR and compare these legs with those of uninjured athletes (controls). Methods We captured standard video-based three-dimensional motion and electromyography (EMG) in 26 athletes after ACLR and 23 healthy controls during SLHD and calculated lower limb and trunk kinematics. We calculated lower limb joint moments and work using inverse dynamics and computed lower limb muscle forces using an EMG-constrained musculoskeletal modelling approach. Between-limb (within ACLR athletes) and between-group differences (between ACLR athletes and controls) were evaluated using paired and independent sample t-tests, respectively. Results Significant differences in kinematics (effect sizes ranging from 0.42 to 1.56), moments (0.39 to 1.08), and joint work contribution (0.55 to 1.04) were seen between the involved and uninvolved legs, as well as between groups. Athletes after ACLR achieved a 97%±4% limb symmetry index in hop distance but the symmetry in work done by the knee during propulsion was only 69%. During landing, the involved knee absorbed less work than the uninvolved, while the uninvolved knee absorbed more work than the control group. Athletes after ACLR compensated for lower knee work with greater hip work contribution and by landing with more hip flexion, anterior pelvis tilt, and trunk flexion. Conclusion Symmetry in performance on a SLHD test does not ensure symmetry in lower limb biomechanics. The distance hopped is a poor measure of knee function, and largely reflects hip and ankle function. Male athletes after ACLR selectively unload the involved limb but outperform controls on the uninvolved knee.

Cartilage is responsive to the loading imposed during cyclic routine activities. However, the local relation between cartilage in terms of thickness distribution and biochemical composition and the local contact pressure during walking has not been established. The objective of this study was to evaluate the relation between cartilage thickness, proteoglycan and collagen concentration in the knee joint and knee loading in terms of contact forces and pressure during walking. 3D gait analysis and MRI (3D-FSE, T1ρ relaxation time and T2 relaxation time sequence) of fifteen healthy subjects were acquired. Experimental gait data was processed using musculoskeletal modeling to calculate the contact forces, impulses and pressure distribution in the tibiofemoral joint. Correlates to local cartilage thickness and mean T1ρ and T2 relaxation times of the weight-bearing area of the femoral condyles were examined. Local thickness was significantly correlated with local pressure: medial thickness was correlated with medial condyle contact pressure and contact force, and lateral condyle thickness was correlated with lateral condyle contact pressure and contact force during stance. Furthermore, average T1ρ and T2 relaxation time correlated significantly with the peak contact forces and impulses. Increased T1ρ relaxation time correlated with increased shear loading, decreased T1ρ and T2 relaxation time correlated with increased compressive forces and pressures. Thicker cartilage was correlated with higher condylar loading during walking, suggesting that cartilage thickness is increased in those areas experiencing higher loading during a cyclic activity such as gait. Furthermore, the proteoglycan and collagen concentration and orientation derived from T1ρ and T2 relaxation measures were related to loading.

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.

(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.