Analysis of muscle fatigue determination and sensitivity for parameters to detect muscle fatigue from surface EMG signals

被引:0
|
作者
Lee J. [1 ]
机构
[1] Dept. of Control and Instrumentation Engineering, Kangwon National Univ., Samcheok
关键词
: Surface EMG; Estimation parameter; Muscle fatigue;
D O I
10.5370/KIEE.2019.68.4.573
中图分类号
R318.08 [生物材料学]; Q [生物科学];
学科分类号
07 ; 0710 ; 0805 ; 080501 ; 080502 ; 09 ;
摘要
- The purpose of this study is to compare the performance of seven fatigue detection parameters, MNF(mean frequency), MDF(median frequency), FBR(frequency band ratio), SMR(spectral moment ratio), ZCF(zero-crossing frequency), TUF (turn frequency) and SPF(spike frequency), based on muscle fatigue determination and sensitivity. Surface EMG signals(a total of 198 signals) were recorded in biceps brachii muscle with isometric 20%, 50% and 80% MVC contractions from eleven subjects. The parameters were calculated from the signals and the resulting fatigue curves were compared considering fatigue determination performance and fatigue sensitivity performance. Results of this study suggest that SMR is more reliable and sensitive parameters than others for differentiating muscle fatigue level from surface EMG signal obtained by isometric voluntary contraction. Copyright The Korean Institute of Electrical Engineers
引用
收藏
页码:573 / 578
页数:5
相关论文
共 50 条
  • [41] AUTOMATIC EMG SELECTION FOR MUSCLE FATIGUE DIAGNOSIS
    SINDERBY, C
    LINDSTROM, L
    GRASSINO, AE
    AMERICAN REVIEW OF RESPIRATORY DISEASE, 1993, 147 (04): : A697 - A697
  • [42] Surface EMG mapping of the human trapezius muscle: the topography of monopolar and bipolar surface EMG amplitude and spectrum parameters at varied forces and in fatigue
    Kleine, BU
    Schumann, NP
    Stegeman, DF
    Scholle, HC
    CLINICAL NEUROPHYSIOLOGY, 2000, 111 (04) : 686 - 693
  • [43] Muscle fatigue analysis using surface EMG signals and time–frequency based medium-to-low band power ratio
    Karthick, P.A. (pakarthick1@gmail.com), 1600, John Wiley and Sons Inc (52):
  • [44] The measurement of sensitivity of numerical parameters in quantification of local muscle fatigue
    Kim, JY
    Jung, MC
    PROCEEDINGS OF THE HUMAN FACTORS AND ERGONOMICS SOCIETY 42ND ANNUAL MEETING, VOLS 1 AND 2, 1998, : 936 - 939
  • [45] Classification of Muscle Fatigue using Surface Electromyography Signals and Multifractals
    Marri, Kiran
    Swaminathan, Ramakrishnan
    2015 12TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD), 2015, : 669 - 674
  • [46] Surface EMG-based Profiling and Fatigue Analysis of the Biceps Brachii Muscle of Cricket Bowlers
    Rizwan, Muhammad
    Khan, Nadeem
    Ahmad, Rushda
    Ijaz, Muneeb
    BIOSIGNALS: PROCEEDINGS OF THE 14TH INTERNATIONAL JOINT CONFERENCE ON BIOMEDICAL ENGINEERING SYSTEMS AND TECHNOLOGIES - VOL 4: BIOSIGNALS, 2021, : 192 - 199
  • [47] Muscle fatigue analysis of EMG signal for female workers in department store
    Kwon, YG
    Kim, SR
    ERGONOMICS AND SAFETY FOR GLOBAL BUSINESS QUALITY AND PRODUCTIVITY, 2000, : 345 - 348
  • [48] EMG QUANTITATIVE-ANALYSIS OF MUSCLE FATIGUE IN MYOTONIC-DYSTROPHY
    ROSSI, B
    SICILIANO, G
    ANGELOTTI, M
    RISALITI, R
    ELECTROPHYSIOLOGICAL KINESIOLOGY /, 1988, 804 : 417 - 420
  • [49] EMG Analysis of Human Inspiratory Muscle Resistance to Fatigue During Exercise
    Segizbaeva, M. O.
    Donina, Zh. A.
    Timofeev, N. N.
    Korolyov, Yu. N.
    Golubev, V. N.
    Aleksandrova, N. P.
    NEUROBIOLOGY OF RESPIRATION, 2013, 788 : 197 - 205
  • [50] REAL-TIME MEASUREMENT OF MUSCLE FATIGUE RELATED CHANGES IN SURFACE EMG
    KRAMER, CGS
    HAGG, T
    KEMP, B
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 1987, 25 (06) : 627 - 630