Numerical simulations play an important role in solving complex engineering problems and have the potential to revolutionize medical decision making and treatment strategies. In this paper, we combine the rapid model-based design, control systems and powerful numerical method strengths of MATLAB/Simulink with the simulation and human movement dynamics strengths of OpenSim by developing a new interface between the two software tools. OpenSim is integrated with Simulink using the MATLAB S-function mechanism, and the interface is demonstrated using both open-loop and closed-loop control systems. While the open-loop system uses MATLAB/Simulink to separately reproduce the OpenSim Forward Dynamics Tool, the closed-loop system adds the unique feature of feedback control to OpenSim, which is necessary for most human movement simulations. An arm model example was successfully used in both open-loop and closed-loop cases. For the open-loop case, the simulation reproduced results from the OpenSim Forward Dynamics Tool with root mean square (RMS) differences of 0.03° for the shoulder elevation angle and 0.06° for the elbow flexion angle. MATLAB’s variable step-size integrator reduced the time required to generate the forward dynamic simulation from 7.1 s (OpenSim) to 2.9 s (MATLAB). For the closed-loop case, a proportional–integral–derivative controller was used to successfully balance a pole on model’s hand despite random force disturbances on the pole. The new interface presented here not only integrates the OpenSim and MATLAB/Simulink software tools, but also will allow neuroscientists, physiologists, biomechanists, and physical therapists to adapt and generate new solutions as treatments for musculoskeletal conditions.
Stiff-knee gait is a troublesome movement disorder among children with cerebral palsy (CP), where peak swing phase knee flexion is diminished due to over-activity of the rectus femoris muscle. A common treatment for stiff-knee gait, rectus femoris transfer surgery, moves the muscle's distal tendon from the patella to the sartorius insertion on the tibia. As a biarticular muscle, rectus femoris may play a role in motor control and have unrecognized benefits for maintaining balance. We used musculoskeletal modeling, neuromuscular control, and forward dynamic simulation to investigate the role of rectus femoris tendon transfer surgery on balance recovery after support-surface perturbations for children with CP adopting two different crouched postures. We combined both high-level supraspinal and low-level spinal signals to generate 92 muscle excitations for tracking experimental whole body center of mass positions and velocities. Stability during balance recovery was evaluated by the minimum distance between the extrapolated center of mass and base of support boundary (bmin) and the minimum time to reach the boundary (TtBmin). The balance recovery of pre-surgical simulations (bmin=2.3+1.1cm, TtBmin=0.2+0.1s) were different (p=0.02), on average, than post-surgical simulations (bmin=-4.9+11.4cm, TtBmin=-0.1+0.3s) of rectus femoris transfers. The moderate crouch simulations (bmin=2.4+0.4cm, TtBmin=0.2+0.03s) were more stable than the mild crouch simulations (bmin=1.2+0.3cm, TtBmin=0.1+0.02s) following anterior translations of the support surface. These findings suggest that tendon transfer of rectus femoris affects balance recovery in children with CP.
Background: After a sport-related concussion (SRC), the risk for lower extremity injury is approximately 2 times greater, and the risk for another SRC may be as much as 3 to 5 times greater. Purpose: To assess the predictive validity of screening methods for identification of individual athletes who possess an elevated risk of SRC. Study Design: Case-control study; Level of evidence, 3. Methods: Metrics derived from a smartphone flanker test software application and self-ratings of both musculoskeletal function and overall wellness were acquired from American high school and college football players before study participation. Occurrences of core or lower extremity injury (CLEI) and SRC were documented for all practice sessions and games for 1 season. Receiver operating characteristic and logistic regression analyses were used to identify variables that provided the greatest predictive accuracy for CLEI or SRC occurrence. Results: Overall, there were 87 high school and 74 American college football players included in this study. At least 1 CLEI was sustained by 45% (39/87) of high school players and 55% (41/74) of college players. Predictors of CLEI included the flanker test conflict effect ≥69 milliseconds (odds ratio [OR], 2.12; 90% CI, 1.24-3.62) and a self-reported lifetime history of SRC (OR, 1.70; 90% CI, 0.90-3.23). Of players with neither risk factor, only 38% (29/77) sustained CLEI compared with 61% (51/84) of players with 1 or both of the risk factors (OR, 2.56; 90% CI, 1.50-4.36). SRC was sustained by 7 high school players and 3 college players. Predictors of SRC included the Overall Wellness Index score ≤78 (OR, 9.83; 90% CI, 3.17-30.50), number of postconcussion symptoms ≥4 (OR, 8.35; 90% CI, 2.71-25.72), the Sport Fitness Index score ≤78 (OR, 5.16; 90% CI, 1.70-15.65), history of SRC (OR, 4.03; 90% CI, 1.35-12.03), and the flanker test inverse efficiency ratio ≥1.7 (OR, 3.19; 90% CI, 1.08-9.47). Conclusion: Survey responses and smartphone flanker test metrics predicted greater injury incidence among individual football players classified as high-risk compared with that for players with a low-risk profile.
Wrist posture impacts the muscle lengths and moment arms of the extrinsic finger muscles that cross the wrist. As a result, the electromyographic (EMG) activity associated with digit movement at different wrist postures must also change. We sought to quantify the posture-dependence of extrinsic finger muscle activity using bipolar fine-wire electrodes inserted into the extrinsic finger muscles of able-bodied subjects during unrestricted wrist and finger movements across the entire range of motion. EMG activity of all the recorded finger muscles were significantly different (p < 0.05, ANOVA) when performing the same digit movement in five different wrist postures. Depending on the wrist posture, EMG activity changed by up to 70% in individual finger muscles for the same movement, with the highest levels of activity observed in finger extensors when the wrist was extended. Similarly, finger flexors were most active when the wrist was flexed. For the finger flexors, EMG variations with wrist posture were most prominent for index finger muscles, while the EMG activity of all finger extensor muscles were modulated in a similar way across all digits. In addition to comprehensively quantifying the effect of wrist posture on extrinsic finger EMG activity in able-bodied subjects, these results may contribute to designing control algorithms for myoelectric prosthetic hands in the future.
Many activities of daily living require a high level of neuromuscular coordination and balance control to avoid falls. Complex musculoskeletal models paired with detailed neuromuscular simulations complement experimental studies and uncover principles of coordinated and uncoordinated movements. Here, we created a closed-loop forward dynamic simulation framework that utilizes a detailed musculoskeletal model (19 degrees of freedom, and 92 muscles) to synthesize human balance responses after support-surface perturbation. In addition, surrogate response models of task-level experimental kinematics from two healthy subjects were provided as inputs to our closed-loop simulations to inform the design of the task-level controller. The predicted muscle activations and the resulting synthesized subject joint angles showed good conformity with the average of experimental trials. The simulated whole-body center of mass displacements, generated from a single kinematics trial per perturbation direction, were on average, within 7 mm (anterior perturbations) and 13 mm (posterior perturbations) of experimental displacements. Our results confirmed how a complex subject-specific movement can be reconstructed by sequencing and prioritizing multiple task-level commands to achieve desired movements. By combining the multidisciplinary approaches of robotics and biomechanics, the platform demonstrated here offers great potential for studying human movement control and subject-specific outcome prediction.
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