Quantitative Assessment of
Load and Speed Effects on
Lower Limb Muscle Coordination
During Locomotion

Janell A. Beres1, B. C. Davis1, T.D. Johnson2, B.H. Dobkin1*, and Susan J. Harkema1
Department of Neuology1 and Biomathematics2, University of California at Los Angeles, Los Angeles, CA, USA

 
Table of Contents
::Home
::Introduction
::Methods
::Results
::Conclusions
::References
::Acknowledgments

::Introduction
The human lumbosacral spinal cord can modulate efferent motor patterns during locomotion in response to changes in associated sensory modalities (Harkema et al., 1997). In this study, we use a statistically valid method to quanitfy neuromuscular coordination, and describe the effects of variations in speed and load on the coordination of lower limb muscles of non-disabled (ND) and spinal cord injured (SCI) subjects during manually assisted stepping on a treadmill with body weight support (BWST).

 

::Methods
1. Locomotion:
Each subject wore a harness connected to an overhead motorized lift that allowed adjustment of lower limb loading. Body weight load (BWL) on the lower limbs ranged from no limb loading (0%) to full loading (100%) for the ND subjects, and from 0% to the maximum loading level at which knee flexion during stance could be avoided (50-80%) for the SCI subjects. Treadmill speeds for each subject ranged from 0.29 m/s to 1.7 m/s. Since stride length varies across subjects, we convert speed to step-cycle frequency, and express it in steps per second.

2. Measurements:
EMG activity of the soleus (SOL), medial gastrocnemius (MG), and tibialis anterior (TA), the amount of lower limb load (%BWL), and stepping speed (steps/sec) were recorded for 2 ND and 3 SCI subjects during stepping sequences at varying loads and speeds.

3. Muscle Activity Detection:
We use a novel Bayesian change point method, via reversible jump Markov chain Monte Carlo simulations, (Johnson et al., 2000) to identify points of change in the variance of the EMG signal. We then calculate a signal-to-noise ratio (SNR) from the EMG signal variance at each time point t and the EMG baseline (background noise) variance.


To define periods of muscle activation, we then apply physiologically appropriate event thresholds, specifically:
  • SNR greater than 2.5
  • Minimum activity duration of 32 msec
  • Minimum inactivity duration of 38 msec
(See poster 61.11)

4. Quantifying Coordination Coordination is defined as the proportion of time that two muscles are simultaneously active and simultaneously inactive, and is calculated by the following formula:


Figure 2 (below) shows the computation of coordination between the SOL and MG and between the SOL and TA. Time segments marked by:
    A= both muscles simultaneously active
    B= total activity (either muscle active)
    C= both muscles simultaneously inactive
    D= total inactivity (either muscle inactive)

5. Statistical Inference We then compute the probability that the coordination between any two of the three pairs of muscles is equal.

  • Compute difference in coordination for the 2 pairs [Eg. Coord(SOL/MG) - Coord(SOL/TA)]
  • We end up with a distribution of the difference in coordination values for the two muscle pairs.
  • If 0 lies between the 0.025 and the 0.975, the two coordination distributions are similar.
  • If 0 lies in one of the tails of the distribution, the coordination between the two pairs is different.

 

::Results
click thumbnails for actual size

Results for 2 ND and 3 SCI subjects are shown graphically in figure 3, which demonstrates the differences among subjects in the coordination of each lower limb muscle pair across increasing speed and load stepping conditions.

Figure 4 shows the differences in coordination among the 3 muscle pairs across speed and load conditions for ND-1. The changes in coordination shown in the summary line graphs on the left are expanded in selected linear envelope and scatter plots on the right. While there is a difference between unloaded and loaded states, changing limb loading does not affect SOL/MG coordination. SOL/TA and MG/TA coordination increases as load increases, as the TA becomes active during stance. There is an earlier onset of MG activity as speed increases, resulting in an increase in SOL/MG coordination. TA activation during stance decreases as speed increases, resulting in a decrease in SOL/TA and MG/TA coordination.


Figure 5 shows the differences in coordination among the 3 muscle pairs across speed and load conditions for SCI-C2. The changes in coordination shown in the summary line graphs on the left are expanded in selected linear envelope and scatter plots on the right. Antagonistic muscle pairs generally increase in coordination with increasing load and speed, as the TA becomes active during stance. There also appears to be some co-modulation of the MG and TA, as the coordination pattern does not change with speed.


Figure 6 shows the differences in coordination among the 3 muscle pairs across speed and load conditions for SCI-A1. The changes in coordination shown in the summary line graphs on the left are expanded in selected linear envelope and scatter plots on the right. This subject did not display TA activity during any stepping condition. Therefore, changes in SOL/TA and MG/TA coordination occur as a result of changes in SOL or MG activation pattern. The decrease in SOL/TA and MG/TA coordination occurs because both the SOL and MG increase in activity as load and speed increases. From 0% to 45% BWL SOL and MG increase in a similar fashion, whereas at 55% BWL, the MG is active throughout a greater portion of the step-cycle than the SOL. As speed increases, SOL/MG coordinaiton decreases as the MG is activated earlier.


Figure 7 shows the differences in coordination among the 3 muscle pairs across speed and load conditions for SCI-C1. The changes in coordination shown in the summary line graphs on the left are expanded in selected linear envelope and scatter plots on the right. At 0% BWL there is no activity present in any of the muscles studied. With loading, the SOL/MG coordination increases, as SOL and MG activity increases. The SOL/TA and MG/TA coordination decreases with increased loading as the TA remains inactive while the SOL and MG increase in activity. The high SOL/MG coordination at low speeds is due to high clonicity at those speeds, which decreases as speed increases. Any activity in the TA is clonic activity, but it is most clonic at the low speed. Thus SOL/TA and MG/TA coordination decreases as speed increases.


 

::Conclusion
1. Lower limb loading and stepping velocity can affect muscle coordination patterns in both ND and SCI subjects during stepping.
2. Agonist muscle pairs (SOL/MG) are less coordinated following SCI, as evidenced by a lower coordination value in SCI subjects compared with ND subjects across load and speed conditions.
3. Antagonist muscle pairs (SOL/TA and MG/TA) are more coordinated following SCI, as evidenced by a higher coordination value in SCI subjects compared with ND subjects across load and speed conditions.
4. Stepping in the absence of load is interpreted differently than weight bearing stepping, as evidenced by the large difference in coordination value among all three muscle pairs in both ND and SCI subjects.
5. The abnormal activation patterns seen in following SCI may not only be reflective of deficits following injury, but also of the abnormally low stepping velocities, as evidenced by figure 3.

 

::References
1. Harkema SJ, Hurley SL, Patel UK, Requejo PS, Dobkin BH, & Edgerton VR. (1997). Human lumbosacral spinal cord interprets loading during stepping.
2. Johnson TD, Elashoff RM, & Harkema SJ. (2000). Bayesian change-point analysis of muscular coordination from electromyographic data. (under review). [See also Davis BC, et al. (2000). Quantification of muscle coordination and clonic-like motor firing patterns in spinal-cord injured subjects during stepping. SFN 2000 Poster.]
3. Maynord FM, et al. International standards for neurological and functional classification of spinal cord injury (1997). American Spinal Injury Association. Spinal Cord: 35, 266-274.

 

:: Acknowledgements
We would like to thank Roscelle Joaquin, Morteza Tavakol, Rita Lukacs, Ani Balmanoukian and Sue Ono for their contributions to this poster.

This work was supported by NIH NS36854, NS16333, and RR00865.