Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study

Karlsson, Stig; Yu, Jun; Akay, Metin

doi:10.1109/10.821766

Cited by 302 publications

(218 citation statements)

References 26 publications

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“…As wavelet transforms have been suggested to have better accuracy and precision than other timefrequency analysis methods for stationary and nonstationary EMG signals, 32,33 frequency and intensity analyses were performed by using wavelet analysis. 34 In summary, the method used a filter bank of 13 nonlinearly scaled wavelets.…”

Section: Emg Measurementmentioning

confidence: 99%

Lower leg muscle atrophy in ankle osteoarthritis

et al. 2006

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The aim of this study was to determine changes in the lower leg muscles associated with ankle osteoarthritis. Fifteen unilateral ankle osteoarthritis patients and fifteen age-gender-matched normal subjects were assessed with clinical [osteoarthritis latency time, pain, alignment, AOFAS ankle score, ankle range of motion (ROM), calf circumference], radiological (ankle osteoarthritis grading), and muscular-physiological parameters [isometric maximal voluntary ankle torque, surface electromyography of the anterior tibial (AT), medial gastrocnemius (MG), soleus (SO), and peroneus longus (PL) muscle]. The osteoarthritis patients had increased pain (6.8 points) and reduced AOFAS score (33.7 points) compared to the control group. Compared to the contralateral healthy leg, the arthritic leg showed reduced mean dorsi-/plantar flexion ROM (16.08), reduced mean calf circumference (2.1 cm), smaller mean dorsiflexion (16.4 Nm) and plantar flexion (15.8 Nm) torques, lower mean electromyography frequency for all muscles (AT À22.6 Hz; MG À27.3 Hz; SO À25.9 Hz; PL À28.5 Hz), and lower mean electromyography intensity in the AT [À28.0 Â 10 3 (mv) 2 ], MG [À13.3 Â 10 3 (mv) 2 ], and PL [À12.8 Â 10 3 (mv) 2 ]. SO mean electromyography intensity was not significantly changed [þ2.0 Â 10 3 (mv) 2 ]. Unilateral ankle osteoarthritis is associated with atrophic changes of the lower leg muscles. This study supports previous observations on muscle dysfunction in knee osteoarthritis. ß

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Section: Emg Measurementmentioning

confidence: 99%

Lower leg muscle atrophy in ankle osteoarthritis

et al. 2006

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“…In the first part (sensor), we amplify the sEMG signal that has a very low magnitude (0-5mV) [23], [24]. The amplification process is designed to prevent noise and other disturbing artifacts [25]- [27]. After the amplification, an analog bandpass filter (20 − 500 Hz) is used to reduce the system noise and motion artifacts [28], [29].…”

Section: Introductionmentioning

confidence: 99%

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Ergeneci¹,

Gokcesu

Ertan³

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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Abstract-Wearable technology has gained increasing popularity in the applications of healthcare, sports science and biomedical engineering in recent years. Because of its convenient nature, the wearable technology is particularly useful in the acquisition of the physiological signals. Specifically, the sEMG systems, which measure the muscle activation potentials, greatly benefit from this technology in both clinical and industrial applications. However, the current wearable sEMG systems have several drawbacks including inefficient noise cancellation, insufficient measurement quality and difficult integration to customized applications. Additionally, none of these sEMG data acquisition systems can detect sEMG signals (i.e., contractions), which provides a valuable environment for further studies such as human machine interaction, gesture recognition and fatigue tracking. To this end, we introduce an embedded, 8-channel, noise canceling, wireless, wearable sEMG data acquisition system with adaptive muscle contraction detection. Our design consists of two stages, which are the sEMG sensors and the multi-channel data acquisition unit. For the first stage, we propose a low cost, dry and active sEMG sensor that captures the muscle activation potentials, a data acquisition unit that evaluates these captured multi-channel sEMG signals and transmits them to a user interface. In the data acquisition unit, the sEMG signals are processed through embedded, adaptive methods in order to reject the power line noise and detect the muscle contractions. Through extensive experiments, we demonstrate that our sEMG sensor outperforms a widely used, commercially available product and our data acquisition system achieves 4.583 dB SNR gain with 98.9784% accuracy in the detection of the contractions.

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“…To do a multiresolution signal analysis for equation (1), it may be noted that the wavelet transform of signals exhibit a greater change in value when position is odd. Moreover, such coefficients for noise are uniform for all scales.…”

Section: Wavelet Analysis In Emg Signal Processingmentioning

confidence: 99%

“…A group of action potentials of muscle fibers called MFAP give rise up MUAPS. Neuron signals [1][2] are superimposed on MFAP for maintaining a certain level of force, the alpha motor neuron excite the muscle fibres until these shrink [8]. …”

Section: Introduction "Electromyography (Emg) Is An Experimental Tmentioning

confidence: 99%

Wavelet Denoising Approach for Evaluation of EMG Signal in Sub 1 kHz Range Having Interference Signals

Karuna¹,

P²,

Das³

et al. 2017

IJET

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Abstract-Electromyography (EMG) signal picked up from the muscle fibers have signal in the range below 1KHZ coupled with some noise .The objective is to apply de-nosing by wavelet. Biomedical amplifier AD620 is used for detecting the EMG signal having the gain 4 and approach in 20 to 500Hz. A notch filter is used to eliminate 50Hz pick up. Wave let de-nosing is used to estimate the transform coefficients of basis signals removing the noise. MATLAB is used to calculate the average power of signal. The muscle activates of normal and abnormal persons are compared in case of writer's cramp the cases of alphabets A, B, C are taken for comparison by trapezoidal integration.

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Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study

Cited by 302 publications

References 26 publications

Lower leg muscle atrophy in ankle osteoarthritis

Lower leg muscle atrophy in ankle osteoarthritis

An Embedded, Eight Channel, Noise Canceling, Wireless, Wearable sEMG Data Acquisition System With Adaptive Muscle Contraction Detection

Wavelet Denoising Approach for Evaluation of EMG Signal in Sub 1 kHz Range Having Interference Signals

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