Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio

doi:10.1109/jbhi.2014.2356340

Cited by 52 publications

(18 citation statements)

References 27 publications

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“…Therefore, it is not limited to the Fourier time and frequency limit. Although EMD process is time consuming and it is sometimes difficult to determine the IMF combinations, it has been frequently applied to mechanical fault detection [ 29 , 30 ], electromyography (EMG) analysis [ 31 , 32 ] or EEG, and so forth. The details of the HHT method will be illustrated in methods section.…”

Section: Introductionmentioning

confidence: 99%

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Liu

Fan

et al. 2018

BioMed Research International

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Electroencephalogram (EEG) signal analysis is commonly employed to extract information on the brain dynamics. It mainly targets brain status and communication, thus providing potential to trace differences in the brain's activity under different anesthetics. In this article, two kinds of gamma-amino butyric acid (type A -GABAA) dependent anesthetic agents, propofol and desflurane (28 and 23 patients), were studied and compared with respect to EEG spectrogram dynamics. Hilbert-Huang Transform (HHT) was employed to compute the time varying spectrum for different anesthetic levels in comparison with Fourier based method. Results show that the HHT method generates consistent band power (slow and alpha) dominance pattern as Fourier method does, but exhibits higher concentrated power distribution within each frequency band than the Fourier method during both drugs induced unconsciousness. HHT also finds slow and theta bands peak frequency with better convergence by standard deviation (propofol-slow: 0.46 to 0.24; theta: 1.42 to 0.79; desflurane-slow: 0.30 to 0.25; theta: 1.42 to 0.98) and a shift to relatively lower values for alpha band (propofol: 9.94 Hz to 10.33 Hz, desflurane 8.44 Hz to 8.84 Hz) than Fourier one. For different stage comparisons, although HHT shows significant alpha power increases during unconsciousness stage as the Fourier did previously, it finds no significant high frequency (low gamma) band power difference in propofol whereas it does in desflurane. In addition, when comparing the HHT results within two groups during unconsciousness, high beta band power in propofol is significantly larger than that of desflurane while delta band power behaves oppositely. In conclusion, this study convincingly shows that EEG analyzed here considerably differs between the HHT and Fourier method.

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Section: Introductionmentioning

confidence: 99%

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Liu

Fan

et al. 2018

BioMed Research International

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“…Telehealth and fitness monitoring are some examples of an area where integrative research and development in wearable/portable technology are performed. As a result, high-capacity, low-power, low-cost, miniature and lightweight sensors have been embedded into clothes, belts, shoes, sunglasses, smartwatches and smartphones, or positioned directly on the body in order to collect a large amount of data such as body position and movement, heart rate, muscle fatigue, and skin temperature [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recent works have demonstrated how the sEMG signal is very helpful in monitoring person’s body posture, physical performance, and fitness level [ 8 , 22 – 26 ]. This is due to the fact that it can be obtained using intrinsically noninvasive measurement devices and is relatively easy to acquire.…”

Section: Introductionmentioning

confidence: 99%

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

et al. 2018

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BackgroundThe human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications.ResultsThe proposed system consists of several ultralight wireless sensing nodes that are able to acquire, process and efficiently transmit the motion-related (biological and accelerometer) body signals to one or more base stations through a 2.4 GHz radio link using an ad-hoc communication protocol designed on top of the IEEE 802.15.4 physical layer. A user interface software for viewing, recording, and analysing the data was implemented on a control personal computer that is connected through a USB link to the base stations. To demonstrate the capability of the system of detecting the user’s activity, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. The system was tested on four different exercises performed by three people, the automatic classifier achieved an overall accuracy of 85.7% combining the features extracted from acceleration and sEMG signals.ConclusionsA low cost wireless system for the acquisition of sEMG and accelerometer signals has been presented for healthcare and fitness applications. The system consists of wearable sensing nodes that wirelessly transmit the biological and accelerometer signals to one or more base stations. The signals so acquired will be combined and processed in order to detect, monitor and recognize human activities.

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“…To this end the electrical signals captured from muscles’ activity are very helpful in recognizing this kind of exercises as well as in monitoring person’s body posture, physical performance, and fitness level as it has been demonstrated by recent works [ 11 , 12 , 13 , 14 , 15 , 16 ]. This is due to the fact that sEMG signals can be collected using noninvasive sensor devices and they are relatively easy to acquire.…”

Section: Introductionmentioning

confidence: 99%

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

Biagetti

Crippa

Falaschetti

et al. 2018

Sensors

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The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.

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Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition

Cited by 52 publications

References 27 publications

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes

Classifier Level Fusion of Accelerometer and sEMG Signals for Automatic Fitness Activity Diarization

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