• DocumentCode
    2096208
  • Title

    On the relationship between features extracted from EMG and force for constant and dynamic protocols

  • Author

    Andrade, A.O. ; Andrade, C.I.

  • Author_Institution
    Biomed. Eng. Lab. (Biolab), Fed. Univ. of Uberlandia, Uberlandia, Brazil
  • fYear
    2012
  • fDate
    Aug. 28 2012-Sept. 1 2012
  • Firstpage
    3392
  • Lastpage
    3395
  • Abstract
    The main objective of this study was to characterize the relationship between electromyography and force based on the results obtained from a developed analysis tool. The developed tool presents interesting features for the study of this relationship. Among them, it can be highlighted the possibility of simultaneous analysis of various features in the time domain (obtained from electromyographic signals), and the generation of graphics that allow the visualization of the relation between the selected features and the force signal. The tool also allows a feature evaluation based on different models (e.g., linear, quadratic and exponential) allowing a better understanding of the EMG-force relationship. In order to evaluate the developed tool and study the EMG-force correlation, electromyographic signals (EMG) and force measurements were collected from 15 subjects while executing eight different experimental protocols. The obtained results showed that statistical features (e.g., kurtosis and skewness) are less sensitive to dynamic force protocols; and also that features related to the amplitude of the signal are more appropriate to represent the relationship between EMG and force during the execution of constant force protocols. These results, besides having several practical applications, can be used as part of EMG signals simulators, developed for different applications, such as the evaluation of automatic systems used in the decomposition of EMG signals.
  • Keywords
    biomechanics; electromyography; feature extraction; force measurement; medical signal processing; statistical analysis; time-domain analysis; EMG feature extraction; EMG signal decomposition; EMG signal simulators; EMG-force correlation; EMG-force relationship; constant protocols; dynamic protocols; electromyographic signals; electromyography; exponential model; feature evaluation; force features; linear model; quadratic model; simultaneous time domain feature analysis; statistical features; Biological system modeling; Electromyography; Evolution (biology); Force; Force measurement; Muscles; Protocols; EMG-Force Relationship; Electromyography; First Dorsal Interosseous; Electromyography; Humans; Muscle, Skeletal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4119-8
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2012.6346693
  • Filename
    6346693