As with any gait lab, it is important to establish a repository of normative kinematic, kinetic, and electromyography (EMG) data to serve as a control for ongoing projects involving impaired populations. To this end, our project first and foremost included the initiation of data collections to build our unimpaired database using typical clinical and research walking trials. We developed an experimental protocol involving steady state treadmill walking and a series of over ground walking tasks including trials with 180 degree turns, walking along the length of a pseudo balance beam, and a collection of pivot turns in combination with a set of stair ascent and decent. To date, we have collected data from 3 subjects who have completed these tasks in barefoot and shod conditions.
Alongside initiating our normative data repository, we are currently leveraging our collected EMG data to investigate how the number and choice of muscles recorded effects overall synergy analysis in the lower limb. Synergy theorists conjecture that the brain orchestrates movement by controlling groupings of muscles (synergies) in concert rather than controlling each muscle individually. These synergies can be calculated by using EMG to identify which muscles may be grouped together. In projects involving EMG measurements amidst execution of functional tasks, experimenters must typically decide to record from a subset of muscles involved in the task due to having a finite number of EMG sensors. Muscle synergies calculated from these subsets of relevant muscles, however, may lack similarity between themselves and synergies garnered from a master set of all muscles involved in the task. Research teams should thus have a way of systematically choosing functionally relevant muscles, and a sufficient number of them, in order to produce synergies that resemble those calculated from a master set of muscles. In the upper limbs, previous studies have shown that these three things increase measures of similarity between synergies garnered from subsets of muscles and those produced from a master list: 1) increasing the number of muscles included in subsets, 2) recording from dominant (or most active) muscles and 3) recording from the largest muscles.
While these factors affect synergies produced in the upper limb, studies have yet to be conducted that corroborate these findings in the lower limb. To address whether these findings are supported in the lower limb, we recorded activity from 16 muscles in the dominant leg during all of our team’s aforementioned walking tasks. We then proceeded to perform synergy analysis using non-negative matrix factorization to identify a lower dimensional space of commonly activated muscles. Our preliminary results show similar structural organization between our data and existing literature, even when additional muscles are included. The inclusion of the core muscles, particularly rectus abdominis, has the largest impact on our synergy outputs, suggesting that more synergies may be required if they are included in the analysis. Interestingly, while simulations with synergies have suggested that the adductor magnus may need its own synergy, inclusion of this muscle did not dramatically rearrange our synergy structure in P001.
These experiments have implications for future studies comparing the robustness of motor control in typically developing and neurologically impaired patient populations. The results that we garner here will inform the lab’s future projects incorporating EMG recording methds, laying a foundation on which future synergy analyses and gait studies will lie.