Motor modules of human locomotion: influence of EMG averaging, concatenation, and number of step cycles (original) (raw)

On the Methodological Implications of Extracting Muscle Synergies from Human Locomotion

We investigated the influence of three different high-pass (HP) and low-pass (LP) filtering conditions and a Gaussian (GNMF) and inverse-Gaussian (IGNMF) non-negative matrix factorization algorithm on the extraction of muscle synergies from myoelectric signals during human walking and running. To evaluate the effects of signal recording and processing on the outcomes, we analyzed the intraday and interday computation reliability. Results show that the IGNMF achieved a significantly higher reconstruction quality and on average needs one less synergy to sufficiently reconstruct the original signals compared to the GNMF. For both factorizations, the HP with a cutoff frequency of 250 Hz significantly reduces the number of synergies. We identified the filter configuration of fourth order, HP 50 Hz and LP 20 Hz as the most suitable to minimize the combination of fundamental synergies, providing a higher reliability across all filtering conditions even if HP 250 Hz is excluded. Defining a fundamental synergy as a single-peaked activation pattern, for walking and running we identified five and six fundamental synergies, respectively using both algorithms. The variability in combined synergies produced by different filtering conditions and factorization methods on the same data set suggests caution when attributing a neurophysiological nature to the combined synergies.

Multidimensional EMG-based assessment of walking dynamics

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2003

The electromyogram (EMG) provides a measure of a muscle's involvement in the execution of a motor task. Successful completion of an activity, such as walking, depends on the efficient motor control of a group of muscles. In this paper, we present a method to quantify the intricate phasing and activation levels of a group of muscles during gait. At the core of our method is a multidimensional representation of the EMG activity observed during a single stride. This representation is referred to as a "trajectory." A hierarchical clustering procedure is used to identify representative classes of muscle activity patterns. The relative frequencies with which these motor patterns occur during a session (i.e., a series of consecutive strides) are expressed as histograms. Changes in walking strategy will be reflected as changes in the relative frequency with which specific gait patterns occur. This method was evaluated using EMG data obtained during walking on a level and a mod...