Monitoring muscle fatigue following continuous load changes

Department of Human Factors EngineeringPrevious studies related to monitoring muscle fatigue during dynamic motion have focused on detecting the accumulation of muscle fatigue. However, it is necessary to detect both accumulation and recovery of muscle fatigue in dynamic muscle contraction while muscle load changes continuously. This study aims to investigate the development and recovery of muscle fatigue in dynamic muscle contraction conditions following continuous load changes. Twenty healthy males conducted repetitive elbow flexion and extension using 2kg and 1kg dumbbell, by turns. They performed the two tasks of different intensity (2kg intensity task, 1kg intensity task) alternately until they felt they could no longer achieve the required movement range or until they experienced unacceptable biceps muscle discomfort. Meanwhile, using EMG signal of biceps brachii muscle, fatigue detections were performed from both dynamic measurements during each dynamic muscle contraction task and isometric measurements during isometric muscle contraction right before and after each task. In each of 2kg and 1kg intensity tasks, pre, post and change value of EMG amplitude (AEMG) and center frequency were computed respectively. They were compared to check the validity of the muscle fatigue monitoring method using Wavelet transform with EMG signal from dynamic measurements. As a result, a decrease of center frequency in 2kg intensity tasks and an increase of center frequency in 1kg intensity tasks were detected. It shows that development and recovery of muscle fatigue were detected in 2kg and 1kg intensity tasks, respectively. Also, the tendency of change value of center frequency from dynamic measurements were corresponded with that from isometric measurements. It suggests that monitoring muscle fatigue in dynamic muscle contraction conditions using wavelet transform was valid to detect the development and recovery of muscle fatigue continuously. The result also shows the possibility of monitoring muscle fatigue in real-time in industry and it could propose a guideline in designing a human-robot interaction system based on monitoring user's muscle fatigue.clos

Wavelet transform for the evaluation of peak intensities in flow-injection analysis

The application of the wavelet transform in the determination of peak intensities in flow-injection analysis was studied with regard to its properties of minimizing the effects of noise and baseline drift. The results indicate that for white noise and a favourable peak shape a signal-to-noise ratio of 2 can be tolerated at the 5% error level, which means that a significant reduction in the detection limit can be obtained in comparison with the classical signal-processing methods. With regard to the influence of a changing baseline it was found that its d.c. level has a negligible effect, but a linear or exponentially rising baseline introduces an error that depends on the chosen frequency of the wavelet that is used to determine the peak intensity. The optimum choice of this frequency, in turn, depends on the shape of the peak that is studied. In this respect significant differences were observed for pure Gaussian and exponentially modified Gaussian peaks

Time–frequency Analysis of the EMG Digital Signals

In the article comparison of time-frequency spectra of EMG signals obtained by the following methods: Fast Fourier Transform, predictive analysis and wavelet analysis is presented. The EMG spectra of biceps and triceps while an adult man was flexing his arm were analysed. The advantages of the predictive analysis were shown as far as averaging of the spectra and determining the main maxima are concerned. The Continuous Wavelet Transform method was applied, which allows for the proper distribution of the scales, aiming at an accurate analysis and localisation of frequency maxima as well as the identification of impulses which are characteristic of such signals (bursts) in the scale of time. The modified Morlet wavelet was suggested as the mother wavelet. The wavelet analysis allows for the examination of the changes in the frequency spectrum in particular stages of the muscle contraction. Predictive analysis may also be very useful while smoothing and averaging the EMG signal spectrum in time

Classification of the mechanomyogram signal using a wavelet packet transform and singular value decomposition

Title on author’s file: Classification of mechanomyogram signal using wavelet packet transform and singular value decomposition for multifunction prosthesis control2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Mechanomyographic Parameter Extraction Methods: An Appraisal for Clinical Applications

The research conducted in the last three decades has collectively demonstrated that the skeletal muscle performance can be alternatively assessed by mechanomyographic signal (MMG) parameters. Indices of muscle performance, not limited to force, power, work, endurance and the related physiological processes underlying muscle activities during contraction have been evaluated in the light of the signal features. As a non-stationary signal that reflects several distinctive patterns of muscle actions, the illustrations obtained from the literature support the reliability of MMG in the analysis of muscles under voluntary and stimulus evoked contractions. An appraisal of the standard practice including the measurement theories of the methods used to extract parameters of the signal is vital to the application of the signal during experimental and clinical practices, especially in areas where electromyograms are contraindicated or have limited application. As we highlight the underpinning technical guidelines and domains where each method is well-suited, the limitations of the methods are also presented to position the state of the art in MMG parameters extraction, thus providing the theoretical framework for improvement on the current practices to widen the opportunity for new insights and discoveries. Since the signal modality has not been widely deployed due partly to the limited information extractable from the signals when compared with other classical techniques used to assess muscle performance, this survey is particularly relevant to the projected future of MMG applications in the realm of musculoskeletal assessments and in the real time detection of muscle activity

Masseter muscle activity resulting from stimulation of hypothalamic behavioral sites : wavelet analysis

Patterns of electromyographic (EMG) activity can give an insight into muscle activity associated with a given behavioral state. The masseter muscle is positioned closely to the temporomandibular joint and controls the position and movement of the jaw. The hypothalamus is the region of the brain associated with emotional behavior. In an effort to further understand the muscle activity underlying emotional display, the hypothalamus in two cats was stimulated to evoke a stereotyped emotional response, known as the rage response. Unsheathing of the claws, retraction of the ears, significant pupillary dilation and vocalization (hissing) characterize this behavior. EMG data obtained at the masseter muscle during this emotional state were compared to EMG activity recorded during mastication (eating), the simulated voluntary behavior for this study. The results of this study indicate that the emotional state significantly influences the EMG activity in the masseter muscle. This is evidenced statistically by a larger high frequency component in the EMG data. It is also evidenced by the ratio of stimulation to mastication power levels at different frequencies, which increases as frequency increases. The frequency range between 5-30 Hz has been utilized in the past in studies assessing fatigue. However, the results of this research indicate that the interpretation of the data in this frequency band must be different in studies of emotionally elicited muscle response. Recordings obtained during voluntary muscular activity reflected the typical fatigue response, and appropriate elevations in the power in the 5-30 Hz frequency range occurred, in agreement with previous findings. Recordings obtained during stimulation indicate that the highest power in this frequency band is achieved at the onset of hypothalamic stimulation, rather than at the point in time when fatigue typically occurs, in contrast to previous findings