Phasic behavior of EMG signals during gait: Use of multivariate statistics

Davis, Brian L.; Vaughan, Christopher L.

doi:10.1016/1050-6411(93)90023-p

Cited by 104 publications

(98 citation statements)

References 6 publications

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“…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.…”

Section: Introductionmentioning

confidence: 52%

Coordination of Locomotion with Voluntary Movements in Humans

et al. 2005

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Muscle activity occurring during human locomotion can be accounted for by five basic temporal activation patterns in a variety of locomotion conditions. Here, we examined how these activation patterns interact with muscle activity required for a voluntary movement. Subjects produced a voluntary movement during locomotion, and we examined the resulting kinematics, kinetics, and EMG activity in 16 -31 ipsilateral limb and trunk muscles during the tasks. There were four voluntary tasks added to overground walking (ϳ5 km/h) in which subjects kicked a ball, stepped over an obstacle, or reached down and grasped an object on the floor (weight support on either the right or the left foot). Statistical analyses of EMG waveforms showed that the five basic locomotion patterns were invariantly present in each task, although they could be differently weighted across muscles, suggesting a characteristic locomotion timing of muscle activations. We also observed a separate activation that was timed to the voluntary task. The coordination of locomotion with the voluntary task was accomplished by combining activation timings that were associated separately with the voluntary task and locomotion. Activation associated with the voluntary tasks was either synchronous with the timing for locomotion or had additional activations not represented in the basic locomotion timing. We propose that this superposition of an invariant locomotion timing pattern with a voluntary activation timing may be consistent with the proposal suggesting that compound movements are produced through a superposition of motor programs.

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Section: Introductionmentioning

confidence: 52%

Coordination of Locomotion with Voluntary Movements in Humans

et al. 2005

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“…Our decomposition algorithm is similar to other procedures used to analyze the muscle activity patterns, such as principal component analysis or factor analysis (Patla, 1985;Davis and Vaughan, 1993;Olree and Vaughan, 1995;Merkle et al, 1998;Weijs et al, 1999;Ivanenko et al, 2003Ivanenko et al, , 2004Krishnamoorthy et al, 2003), independent component analysis (McKeown, 2000;Hart and Giszter, 2004), and non-negative matrix factorization (Tresch et al, 1999;Saltiel et al, 2001;Ting and Macpherson, 2005), all of which allow complex spatiotemporal patterns to be expressed as combinations of a limited number of components; however, these other procedures decompose the time-varying multidimensional muscle activity into combinations of synchronous synergies (vectors with the same dimension as the number of muscles) multiplied by a set of time-varying coefficients (vectors with the same dimension as the number of samples). Here, instead, the decomposition of the muscle patterns into time-varying syn- Figure 11.…”

Section: Discussionmentioning

confidence: 99%

Control of Fast-Reaching Movements by Muscle Synergy Combinations

et al. 2006

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How the CNS selects the appropriate muscle patterns to achieve a behavioral goal is an open question. To gain insight into this process, we characterized the spatiotemporal organization of the muscle patterns for fast-reaching movements. We recorded electromyographic activity from up to 19 shoulder and arm muscles during point-to-point movements between a central location and 8 peripheral targets in each of 2 vertical planes. We used an optimization algorithm to identify a set of time-varying muscle synergies, i.e., the coordinated activations of groups of muscles with specific time-varying profiles. For each one of nine subjects, we extracted four or five synergies whose combinations, after scaling in amplitude and shifting in time each synergy independently for each movement condition, explained 73-82% of the data variation. We then tested whether these synergies could reconstruct the muscle patterns for point-to-point movements with different loads or forearm postures and for reversal and via-point movements. We found that reconstruction accuracy remained high, indicating generalization across these conditions. Finally, the synergy amplitude coefficients were directionally tuned according to a cosine function with a preferred direction that showed a smaller variability with changes of load, posture, and endpoint than the preferred direction of individual muscles. Thus the complex spatiotemporal characteristics of the muscles patterns for reaching were captured by the combinations of a small number of components, suggesting that the mechanisms involved in the generation of the muscle patterns exploit this low dimensionality to simplify control.

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“…Consequently, some gait analysts (e.g., Davis & Vaughan, 1993;Mandeville et al, 2009;Merkle et al, 1998;Sadeghi et al, 2000) have applied Kaiser's (1958) varimax rotation for a simpler interpretation.…”

Section: Principal Component Analysismentioning

confidence: 99%

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

Helwig

Hong

Polk

2012

Human Movement Science

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Gait data are typically collected in multivariate form, so some multivariate analysis is often used to understand interrelationships between observed data. Principal Component Analysis (PCA), a data reduction technique for correlated multivariate data, has been widely applied by gait analysts to investigate patterns of association in gait waveform data (e.g., interrelationships between joint angle waveforms from different subjects and/or joints). Despite its widespread use in gait analysis, PCA is for two-mode data, whereas gait data are often collected in higher-mode form. In this paper, we present the benefits of analyzing gait data via Parallel Factor Analysis (Parafac), which is a component analysis model designed for three-or higher-mode data. Using three-mode joint angle waveform data (subjects × time × joints), we demonstrate Parafac's ability to (a) determine interpretable components revealing the primary interrelationships between lowerlimb joints in healthy gait and (b) identify interpretable components revealing the fundamental differences between normal and perturbed subjects' gait patterns across multiple joints. Our results offer evidence of the complex interconnections that exist between lower-limb joints and limb segments in both normal and abnormal gaits, confirming the need for the simultaneous analysis of multi-joint gait waveform data (especially when studying perturbed gait patterns).ii To my loving and supportive family.iii

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Phasic behavior of EMG signals during gait: Use of multivariate statistics

Cited by 104 publications

References 6 publications

Coordination of Locomotion with Voluntary Movements in Humans

Coordination of Locomotion with Voluntary Movements in Humans

Control of Fast-Reaching Movements by Muscle Synergy Combinations

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

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