Two stage EMG onset detection method

Drapała, Jarosław; Brzostowski, Krzysztof; Szpala, Agnieszka; Rutkowska-Kucharska, Alicja

doi:10.2478/v10170-011-0033-z

Cited by 25 publications

(20 citation statements)

References 23 publications

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“…These algorithms, like the KM method, are potentially beneficial because they avoid the initial processes of establishing a user dependent threshold (Vannozzi et al, 2010). Evidence, from non-gait data, suggests that these ''K-mean like'' algorithms may be better than the AGL method (Drapała et al, 2012). However, because the different classes of algorithms have not been validated together against the VIS method across the range of gait speeds encountered in clinical research, it is unknown which algorithm operates best at this range of speeds.…”

Section: Introductionmentioning

confidence: 96%

“…Previous studies have also evaluated a class of algorithms that like the KM method do not require setting a threshold a priori (Drapała et al, 2012;Vannozzi et al, 2010). These algorithms, like the KM method, are potentially beneficial because they avoid the initial processes of establishing a user dependent threshold (Vannozzi et al, 2010).…”

Section: Introductionmentioning

confidence: 98%

“…Computer programs using algorithms, such as the simple threshold (STH) (Di Fabio, 1987;Hodges and Bui, 1996), the approximated generalized likelihood-step (AGL-step) (Roetenberg et al, 2003;Staude et al, 2001;Staude and Wolf, 1999;Staude, 2001), and k-means (KM) methods (Den Otter et al, 2006), overcome the limitations of the gold-standard VIS method by being 100% repeatable (Di Fabio, 1987) and less labor intensive (Drapała et al, 2012). The STH method is the most commonly used algorithmic method, possibly because of its simplicity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The concurrent validity of three computerized methods of muscle activity onset detection

Carter¹,

Gutierrez²

2015

Journal of Electromyography and Kinesiology

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Section: Introductionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The concurrent validity of three computerized methods of muscle activity onset detection

Carter¹,

Gutierrez²

2015

Journal of Electromyography and Kinesiology

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“…Because of the stochastic characteristic of sEMG signals, muscle activity detection is a challenging task, especially in weak sEMG signals due to the gradual increase of amplitude and frequency, artefacts noise, spurious background spikes, and random variations in the background noise (usually introduced by electrode movement over the skin) [1]. Many algorithms have been developed in the literature to detect muscle activities from the sEMG signal.…”

Section: Introductionmentioning

confidence: 99%

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Hameed

Hasan

Shafie

et al. 2020

IJEECS

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The amplitude dependent muscle activity detection algorithms of the surface electromyography (sEMG) signals are very sensitive to the changes in the background noise levels and the performance of these amplitude-based methods is highly deteriorated when the Signal to Noise ratio (SNR) of the sEMG signal is low. sEMG signals of deep and small muscles as well as sEMG signals recorded from patients that have neuromuscular diseases may not meet this SNR requirement which motivates the need for amplitude independent algorithms that can detect weak muscle activities. Moreover, the sEMG signal amplitude is not constant during the recording time due to the variation in the characteristics of the electrode-skin interface and due to the changes in the ground reference level. Therefore, the performance of the muscle activity detection algorithms should not be affected by the involuntary amplitude variations of the sEMG signal in order to achieve reliable control of robotic devices intended for disabled people. To accentuate the importance of the amplitude independent muscle activity detection methods over the amplitude dependent detection methods, a comparative study has been conducted in this paper between the performance of an amplitude independent muscle activity detection algorithm (FLA-MSE algorithm) and three amplitude dependent algorithms with respect to the detection capability of weak muscle activities and with respect to the immunity against false alarms. The results have showed that the performance of the amplitude independent algorithm outperformed the performance of the amplitude dependent algorithms for detecting weak muscle activities and for robustness against false alarms.

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“…The next solution worth mentioning is a two-stage onset estimator as presented by Drapala, Brzostowski, Szpala, and Rutkowska-Kucharska (2012). It consists of two subsequent phases.…”

Section: Literature Reviewmentioning

confidence: 99%

Emg Onset Detection - A Hidden Factor

Magda

2016

CBUP

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Electromyographic (EMG) examination has gained popularity and was deployed in both practical and research fields. This technology enables one to observe the voltage generated by a neuromuscular system and to interpret body movements. Over time, it has been proved highly difficult to precisely determine the beginning of a muscle contraction. A relatively low signal to noise ratio, which is even smaller in case of a surface electromyography (sEMG), complicates this task, and more advanced onset detection algorithms were proposed. Despite the existence of statistically advanced algorithms, proposed in recent years, in many cases a manual onset detection performed by an EMG specialist remains in use, mainly due to lack of reliable solutions. This paper presents a hidden factor, not found in the literature until now, which directly relates subsequent voltage values with muscle activity. Observation of this parameter helps detect muscle onset precisely. Combined with other statistical factors, it can shed light on a totally new, enhanced branch of muscle onset estimators. This study conducts a numerical comparison to follow the hypothesis that the examined factor is correlated with a muscle contraction state.UDC Classification: 612 DOI: http://dx

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Two stage EMG onset detection method

Cited by 25 publications

References 23 publications

The concurrent validity of three computerized methods of muscle activity onset detection

The concurrent validity of three computerized methods of muscle activity onset detection

Amplitude independent versus amplitude dependent muscle activity detection algorithms: a comparative study

Emg Onset Detection - A Hidden Factor

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