Abstract
Antagonistic muscle activity can impair performance, increase metabolic cost, and increase joint stability. Excessive antagonist muscle activity may also cause an undesirable increase in joint contact forces in certain populations such as persons with knee osteoarthritis. Co-contraction of antagonistic muscles measured by electromyography (EMG) is a popular method used to infer muscle forces and subsequent joint forces. However, EMG alone cannot completely describe joint loads that are experienced. This study compares a co-contraction index from EMG to a co-contraction index calculated from simulated muscle moments during gait. Co-contraction indices were calculated from nine healthy, able-bodied subjects during treadmill walking at self-selected speed. Musculoskeletal simulations that tracked experimental kinematics and kinetics were generated for each subject. Experimentally measured EMG was used to constrain the model's muscle excitation for the vastus lateralis and semimembranosus muscles. Using the model's excitations as constrained by EMG, muscle activation and muscle moments were calculated. A common co-contraction index (CCI) based on EMG was compared with co-contraction based on normalized modeled muscle moments (MCCI). While the overall patterns were similar, the co-contraction predicted by MCCI was significantly lower than CCI. Because a simulation can account for passive muscle forces not detected with traditional EMG analysis, MCCI may better reflect physiological knee joint loads. Overall, the application of two co-contraction methods provides a more complete description of muscle co-contraction and joint loading than either method individually.
Original language | English (US) |
---|---|
Pages (from-to) | 607-611 |
Number of pages | 5 |
Journal | Journal of Electromyography and Kinesiology |
Volume | 22 |
Issue number | 4 |
DOIs | |
State | Published - Aug 2012 |
Externally published | Yes |
Keywords
- Co-contraction
- Electromyography
- Joint moment
- Knee
- Simulation
ASJC Scopus subject areas
- Neuroscience (miscellaneous)
- Biophysics
- Clinical Neurology