Fractal analysis of surface EMG signals from the biceps

Gupta, Vineet; Sajja, Srikanth; Reddy, Narender P.

doi:10.1016/s1386-5056(97)00029-4

Cited by 113 publications

(57 citation statements)

References 6 publications

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“…Likewise, temporal fractals have been identified in physiological and behavioural processes such as: the inter-beat intervals of the heart, fetal breathing dynamics, electromyographic signals, center-of-pressure displacement during stance and, of particular relevance to this research, the stride-to-stride fluctuations of human gait (Kobayashi and Musha, 1982;Szeto et al, 1992;Gupta et al, 1997;Collins and Luca, 1993;Hausdorff et al, 1995;Delignieres and Torre, 2009). Specifically, temporal fractals are described by statistical self-similarity, whereby the statistical properties of part of a time series are proportional to those of the whole (Bassingthwaighte et al, 1994).…”

Section: Background 21 Fractals In Naturementioning

confidence: 99%

The effect of treadmill walking on the stride interval dynamics of children

Fairley

Sejdić

Chau

2010

Human Movement Science

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The stride interval of typical human gait is correlated over thousands of strides. This statistical persistence diminishes with age, disease, and pace-constrained walking. Considering the widespread use of treadmills in rehabilitation and research, it is important to understand the effect of this speed-constrained locomotor modality on stride interval dynamics. To this end, and given that the dynamics of children have been largely unexplored, this study investigated the impact of treadmill walking, both with and without handrail use, on paediatric stride interval dynamics. An initial stationarity analysis of stride interval time series identified both non-stationary and stationary signals during all walking conditions. Subsequent scaling analysis revealed diminished stride interval persistence during unsupported treadmill walking compared to overground walking. Finally, while the correlation between stride interval dynamics and gross energy expenditure was investigated in an effort to elucidate the clinical meaning of persistence, no simple linear correlation was found.ii DedicationTo my grandmother, whose inspiration I will carry with me always.iii Acknowledgements A sincere thank-you to my supervisor, Dr. Tom Chau, whose patience, support and guidance has allowed me to navigate the world of research. I am continually amazed by your dedication to your students and work, and am grateful for the knowledge and training that you have provided me.To my committee members, Dr. Karl Zabjek and Dr. Brian Maki, thank you for your invaluable feedback and advice.To the members of the PRISM lab, I am truly appreciative of your insights, perspectives and friendship. A special thank-you to Brian Nhan, Jorge Torres, Sarah Power and Stefanie Blain, with whom I shared an office, for your considerable help, thoughtful comments, good humour, and fun. Many thanks also to: Ervin Sejdić, for your expertise and contribution; Ka Lun Tam, for your technical wizardry; and Matthew Chang, for the thought-provoking discussions.

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Section: Background 21 Fractals In Naturementioning

confidence: 99%

The effect of treadmill walking on the stride interval dynamics of children

Fairley

Sejdić

Chau

2010

Human Movement Science

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“…Many parameters have been successfully applied to classify sEMG signals (Jang et al, 1994;Zardoshti-Kermani et al, 1995;Kang et al, 1995;Gupta et al, 1997;Englehart et al, 1999;Hu et al, 2005;Crawford et al, 2005;Huang et al, 2005;Kim et al, 2006), among which nonlinear dynamical analysis is a quite powerful approach. The calculations of most nonlinear dynamic measures, however, are frequently confronted with insufficient data points and noisy backgrounds.…”

Section: Introductionmentioning

confidence: 99%

Characterization of surface EMG signals using improved approximate entropy

Chen

Wang

Ren

2006

J. Zhejiang Univ. - Sci. B

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An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals.

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“…The estimation range of the FD CE is a broad band. We realize that a minute difference in a value of the FD, even at 0.1, is regarded as a Brought to you by | MIT Libraries Authenticated Download Date | 5/10/18 10:15 AM significant change [10][11][12][13][14][15][16][17][18][19][20][21][22]. This contributed to the usefulness of the FD feature obtained from the CE method.…”

Section: Fd Based On the Ce Methodsmentioning

confidence: 99%

“…However, the FD methods have shown an advance achievement in many research works compared with other non-linear analysis methods. During the past decade, the FD estimators based on the time domain have been proposed in analysis of the sEMG signal such as Higuchi's (HG) method, Katz's method, and boxcounting method [9][10][11][12][13][14], whereas the FD estimators based on the frequency domain have not yet been applied.…”

Section: Introductionmentioning

confidence: 99%

Critical Exponent Analysis Applied to Surface EMG Signals for Gesture Recognition

Phinyomark¹,

Phothisonothai²,

Phukpattaranont³

et al. 2011

Metrology and Measurement Systems

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Based on recent advances in non-linear analysis, the surface electromyography (sEMG) signal has been studied from the viewpoints of self-affinity and complexity. In this study, we examine usage of critical exponent analysis (CE) method, a fractal dimension (FD) estimator, to study properties of the sEMG signal and to deploy these properties to characterize different movements for gesture recognition. SEMG signals were recorded from thirty subjects with seven hand movements and eight muscle channels. Mean values and coefficient of variations of the CE from all experiments show that there are larger variations between hand movement types but there is small variation within the same type. It also shows that the CE feature related to the self-affine property for the sEMG signal extracted from different activities is in the range of 1.855~2.754. These results have also been evaluated by analysis-of-variance (p-value). Results show that the CE feature is more suitable to use as a learning parameter for a classifier compared with other representative features including root mean square, median frequency and Higuchi's method. Most p-values of the CE feature were less than 0.0001. Thus the FD that is computed by the CE method can be applied to be used as a feature for a wide variety of sEMG applications.

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Fractal analysis of surface EMG signals from the biceps

Cited by 113 publications

References 6 publications

The effect of treadmill walking on the stride interval dynamics of children

The effect of treadmill walking on the stride interval dynamics of children

Characterization of surface EMG signals using improved approximate entropy

Critical Exponent Analysis Applied to Surface EMG Signals for Gesture Recognition

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