Generation and analysis of synthetic surface electromyography signals under varied muscle fiber type proportions and validation using recorded signals

被引:0
|
作者
Narayanan, Sidharth [1 ,2 ,3 ,4 ]
Gopinath, Venugopal [1 ,3 ]
机构
[1] NSS Coll Engn, Dept Instrumentat & Control Engn, Palakkad, Kerala, India
[2] NSS Coll Engn, Dept Elect & Commun Engn, Palakkad, Kerala, India
[3] APJ Abdul Kalam Technol Univ, Thiruvananthapuram, Kerala, India
[4] NSS Coll Engn, Dept Instrumentat & Control Engn, Palakkad 678008, Kerala, India
关键词
Surface electromyography (sEMG); fiber types; triceps brachii; adductor pollicis; sEMG model; motor unit; HUMAN ADDUCTOR POLLICIS; MOTOR UNITS; FATIGUE CONDITIONS; EMG; MODEL; SIZE; CONTRACTIONS; RECRUITMENT; EXTRACTION; FREQUENCY;
D O I
10.1177/09544119221149234
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
The magnitude and duration of muscle force production are influenced by the fiber type proportion. In this work, surface electromyography (sEMG) signals of muscles with varied fiber type proportions, are generated. For this, relevant components of existing models reported in various literature have been adopted. Also, a method to calculate the motor unit size factor is proposed. sEMG signals of adductor pollicis (AP) and triceps brachii (TB) muscles are simulated from the onset of force production to muscle fatigue state at various percentages of maximal voluntary contraction (MVC) values. The model is validated using signals recorded from these muscles using well-defined isometric exercise protocols. Root mean square and mean power spectral density values extracted from the simulated and recorded signals are found to increase for TB and decrease for AP with time. A linear variation of the features with %MVC values is obtained for simulated and experimental results. The Bland-Altman plot is used to analyze the agreement between simulated and experimental feature values. Good agreement is obtained for the feature values at various %MVCs. The mean endurance time calculated using the model is found to be comparable to that of the experimental value. This method can be used to generate sEMG signals of different muscles with varying fiber type ratios under various neuromuscular conditions.
引用
收藏
页码:209 / 223
页数:15
相关论文
共 44 条
  • [1] Analysis of Recorded Surface Electromyography Signals Under Varied Muscle Fiber Proportions Using Fractal Dimension
    Abhijith, M.
    Nair, Remya R.
    Venugopal, G.
    IEEE SENSORS LETTERS, 2024, 8 (08) : 1 - 4
  • [2] Analysis of Muscle Fiber Type Proportions in Surface Electromyography Signals of Athletes Using Reassigned Morlet Scalogram
    Nair, Remya R.
    Venugopal, G.
    Swaminathan, Ramakrishnan
    IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2024, 73 : 1 - 10
  • [3] Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals
    P. A. Karthick
    G. Venugopal
    S. Ramakrishnan
    Journal of Medical Systems, 2016, 40
  • [4] Analysis of Muscle Fatigue Progression using Cyclostationary Property of Surface Electromyography Signals
    Karthick, P. A.
    Venugopal, G.
    Ramakrishnan, S.
    JOURNAL OF MEDICAL SYSTEMS, 2016, 40 (01) : 1 - 11
  • [5] Generation of synthetic surface electromyography signals under fatigue conditions for varying force inputs using feedback control algorithm
    Venugopal, G.
    Deepak, P.
    Ghosh, Diptasree M.
    Ramakrishnan, S.
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART H-JOURNAL OF ENGINEERING IN MEDICINE, 2017, 231 (11) : 1025 - 1033
  • [6] 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
  • [7] Analysis of Surface Electromyography Signals in Fatigue Conditions Under Dynamic Contractions Using Time Difference of Muscle Activations
    Shiva, J.
    Chandrasekaran, S.
    Makaram, N.
    Karthick, P. A.
    Swaminathan, R.
    2020 IEEE SIGNAL PROCESSING IN MEDICINE AND BIOLOGY SYMPOSIUM, 2020,
  • [8] Muscle fatigue analysis in isometric contractions using geometric features of surface electromyography signals
    Jero, S. Edward
    Bharathi, K. Divya
    Karthick, P. A.
    Ramakrishnan, S.
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 68
  • [9] Synthetic GNSS spoofing data generation using field recorded signals
    Khan, Abdul Malik
    Iqbal, Naveed
    Khan, Muhammad Faisal
    METHODSX, 2018, 5 : 1272 - 1280
  • [10] A Muscle Fiber Conduction Velocity Estimator Using Surface Electromyography Signals Acquired From Vastus Lateralis
    Nair, Remya R.
    Venugopal, G.
    2022 IEEE 19TH INDIA COUNCIL INTERNATIONAL CONFERENCE, INDICON, 2022,