No abstract

Co-contraction of agonist-antagonist muscles is commonly observed when performing difficult motor tasks. The benefit of co-contraction is thought to be zero-delay corrections to unexpected disturbances from increased intrinsic muscle impedance. We used upper-limb postural and tracking tasks to characterize the effects of co-contraction on motor corrections to loads applied to the limb. We systematically controlled pre-perturbation muscle activity and showed that co-contraction improves subsequent corrective responses in both tasks. However, substantial improvements in the corrective response are only observed at the time when neural feedback pathways can also contribute. We demonstrate that muscle impedance appears to play a minor role in improving performance. Instead, co-contraction engages a dual agonist-antagonist control strategy to counter disturbances, that is distinct from the control strategy used when not co-contracting or selectively pre-activating a single muscle group. Critically, we showed that this dual agonist-antagonist control strategy improved performance even at low levels of co-contraction.

Abnormal loading of the knee joint contributes to the pathogenesis of knee osteoarthritis. Gait retraining is a noninvasive intervention that aims to reduce knee loads by providing audible, visual, or haptic feedback of gait parameters. The computational expense of joint contact force prediction has limited real-time feedback to surrogate measures of the contact force, such as the knee adduction moment. We developed a method to predict knee joint contact forces using motion analysis and a statistical regression model that can be implemented in near real-time. Gait waveform variables were deconstructed using principal component analysis, and a linear regression was used to predict the principal component scores of the contact force waveforms. Knee joint contact force waveforms were reconstructed using the predicted scores. We tested our method using a heterogenous population of asymptomatic controls and subjects with knee osteoarthritis. The reconstructed contact force waveforms had mean (SD) root mean square differences of 0.17 (0.05) bodyweight compared with the contact forces predicted by a musculoskeletal model. Our method successfully predicted subject-specific shape features of contact force waveforms and is a potentially powerful tool in biofeedback and clinical gait analysis.