RESUMO
In this paper, we propose a force estimation model to compute the handgrip force from SEMG signal during fatiguing muscle contraction tasks. The appropriate frequency range was analyzed using various combinations of a wavelet scale, and the highest accuracy was achieved at a range from 242 to 365 Hz. After that, eight healthy individuals performed a series of static (70%, 50%, 30%, and 20% MVC) and dynamic (0-50% MVC) muscle contraction tasks to evaluate the performance of this technique in comparison with that of former method using the Root Mean Square of the SEMG signal. Both methods had comparable results at the beginning of the experiments, before the onset of muscle fatigue. However, differences were clearly observed as the degree of muscle fatigue began to increase toward the endurance time. Under this condition, the estimated handgrip force using the proposed method improved from 17% to 134% for static contraction tasks and 40% for dynamic contraction tasks. This study overcomes the limitation of the former method during fatiguing muscle contraction tasks and, therefore, unlocks the potential of utilizing the SEMG signal as an indirect force estimation method for various applications.
Assuntos
Algoritmos , Eletromiografia/métodos , Força da Mão/fisiologia , Contração Muscular/fisiologia , Fadiga Muscular/fisiologia , Músculo Esquelético/fisiologia , Adulto , Humanos , Masculino , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Processamento de Sinais Assistido por Computador , Estresse MecânicoRESUMO
Muscle fatigue is commonly associated with the musculoskeletal disorder problem. Previously, various techniques were proposed to index the muscle fatigue from electromyography signal. However, quantitative measurement is still difficult to achieve. This study aimed at proposing a method to estimate the degree of muscle fatigue quantitatively. A fatigue model was first constructed using handgrip dynamometer by conducting a series of static contraction tasks. Then the degree muscle fatigue can be estimated from electromyography signal with reasonable accuracy. The error of the estimated muscle fatigue was less than 10% MVC and no significant difference was found between the estimated value and the one measured using force sensor. Although the results were promising, there were still some limitations that need to be overcome in future study.
Assuntos
Eletromiografia/métodos , Contração Muscular/fisiologia , Fadiga Muscular/fisiologia , Processamento de Sinais Assistido por Computador , Adulto , Calibragem , Força da Mão/fisiologia , Humanos , Masculino , Modelos Estatísticos , Força Muscular/fisiologia , Dinamômetro de Força Muscular , Reprodutibilidade dos Testes , Fatores de TempoRESUMO
Static and dynamic handgrip experiments are performed in order to evaluate the effectiveness of utilizing frequency-band wavelet analysis in measuring force and muscle fatigue simultaneously. SEMG signals are recorded from flexor muscle and analyzed using continuous wavelet transform (CWT). The wavelet coefficients are grouped into high frequency (65Hz - 350Hz) and low frequency (5Hz - 45Hz) band. A significant correlation is discovered between amplitude of high frequency band and force level. On the other hand, the amplitude of low frequency band is associated with muscle fatigue. These results have an important implication for estimating force and muscle fatigue simultaneously especially during dynamic contraction.