Dynamic electromyography. II. Normal patterns during gait

Abstract

Human gait is a complex phenomenon. Many descriptors are needed to completely describe gait in terms of the biomechanics involved. The descriptors, when expressed as a function of the gait cycle, are complex waveforms. For each of these variables, a single "normal" pattern with bands of deviation has generally been accepted as a reference in clinical/research use to explain the abnormalities in a patient's walking pattern. In fact, one observes many "normal" patterns, and a body of research has been devoted to explaining the differences between these patterns in terms of walking speed, age, cadence, sex, etc. It would be simpler in one sense to start with the fact that different people walk with different patterns, not one pattern with bands of deviation. Numerical representation of the waveforms simplifies the analysis and interpretation of waveform data and facilitates comparison between subjects or groups of subjects. When combined with pattern recognition techniques, it also is useful for identifying subpatterns within a group. In this article, the numerical representation of electromyographic data by Karhunen-Loeve expansion are combined with cluster analysis to obtain patterns of dynamic phasic activity of 10 muscles of the lower extremity. From the 35 normal subjects walking at self-selected speed, two to four patterns are developed for each of the muscles and the physiological significance of the patterns are discussed.

Coordination of Locomotion with Voluntary Movements in Humans

“…Mammalian locomotion is another behavior that might provide insights about motor control strategies (Shiavi and Griffin, 1981;Patla, 1985;Patla et al, 1985;Wootten et al, 1990;Davis and Vaughan, 1993;Lacquaniti et al, 2002;Yakovenko et al, 2002;Poppele and Bosco, 2003). Consistent with some of the primitives ideas, we and others (Davis and Vaughan, 1993;Olree and Vaughan, 1995;Ivanenko et al, 2003Ivanenko et al, , 2004 have shown that muscle activity during human locomotion is driven by a few (approximately five) temporal activation components.…”

An open source lower limb model: Hip joint validation

“…5. Comparison between the EMG profiles for walking published by Wootten et al (1990) (gray shaded areas, activity scale between 0 and 1 on the left side of the plots) and the muscle forces of an example gait trial of Subject S1 (thin black lines, force scale on the right side of the plots) estimated using different objective functions. Only hip crossing muscles are represented: gluteus maximus (8 selected bundles), gluteus medius (12 bundles), adductor longus (6 bundles), semitendinosus (single bundle), biceps femoris caput longum (single bundle), rectus femoris (2 bundles).…”

“…The assessment of the predicted muscle forces is based on experimental EMG profiles from healthy subjects available in the literature for level walking (Wootten et al, 1990) and stair climbing (McFadyen and Winter, 1988). However, the staircase inclination was less in the HIP98 trials than in the EMG dataset.…”

“…The estimated muscle forces were evaluated against activation profiles available in the literature for level walking (Wootten et al, 1990;Perry, 1992) and stair climbing (McFadyen and Winter, 1988).…”

Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis

“…One typical application of PCA to gait data is to examine interrelationships between joints, muscles, and/or body segments in healthy gait (e.g., MacLellan & McFadyen, 2010;Palta, 1985;Sadeghi et al, 1997;Wootten et al, 1990). In these applications, PCA is often applied to gait waveform data in one of two manners: (a) conduct one PCA for each subject using a joints/muscles/segments × time matrix (e.g., Bennett et al, 2010;Herr & Popovic, 2008;Mah et al, 1994;Raptopoulos et al, 2006); or (b) conduct one PCA for each joint/muscle/segment using a subjects × time matrix (e.g., Ivanenko et al, 2004;Laassel et al, 1992;Loslever et al, 1994;Sadeghi et al, 2001).…”