publications([{ "bibtype": "article", "publisher": "IEEE", "doi": "http://dx.doi.org/10.1109/TIM.2016.2534378", "lang": "en", "uri": "http://iihm.imag.fr/publication/TPQ+16a/", "title": "Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments", "url": "http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7438830&isnumber=4407674", "journal": "IEEE Transactions on Instrumentation and Measurement", "authors": { "1": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "2": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2016, "number": 7, "pages": "1547 - 1557", "volume": 65, "id": 758, "editor": "IEEE", "address": "US", "date": "2016-04-24", "type": "Revues internationales avec comité de lecture", "abstract": "The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR1 and KR2). The results show that at low SNR (<;5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR.\r\n", "type_publi": "irevcomlec", "abbr": "TPQ+16a" }, { "bibtype": "article", "publisher": "Elsevier", "doi": "https://doi.org/10.1016/j.cmpb.2014.06.013", "lang": "en", "uri": "http://iihm.imag.fr/publication/PQC+14a/", "title": "Feature extraction of the first difference of EMG time series for EMG pattern recognition", "url": "http://www.sciencedirect.com/science/article/pii/S0169260714002478", "journal": "Computer Methods and Programs in Biomedicine", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Sylvie", "last_name": "Charbonnier" }, "4": { "first_name": "Christine", "last_name": "Serviere" }, "5": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "6": { "first_name": "Yann", "last_name": "Laurillau" } }, "year": 2014, "number": 2, "pages": "247-256", "volume": 117, "id": 698, "abbr": "PQC+14a", "address": "New York, NY, USA", "date": "2014-06-24", "document": "http://iihm.imag.fr/publs/2014/ESWA-Draft.pdf", "type": "Revues internationales avec comité de lecture", "abstract": "This paper demonstrates the utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary. The technique was evaluated by three stationarity tests consisting of the variation of two statistical properties, i.e., mean and standard deviation, and the reverse arrangements test. As a result of the proposed technique, the first difference of EMG time series became more stationary compared to the original measured signal. Based on this finding, the performance of time-domain features extracted from raw and transformed EMG was investigated via an EMG classification problem (i.e., eight dynamic motions and four EMG channels) on data from eighteen subjects. The results show that the classification accuracies of all features extracted from the transformed signals were higher than features extracted from the original signals for six different classifiers including quadratic discriminant analysis. On average, the proposed differencing technique improved classification accuracies by 2%-8%.", "type_publi": "irevcomlec" }, { "chapter": 15, "publisher": "IGI Global", "doi": "http://doi.org/10.4018/978-1-4666-6090-8", "lang": "en", "title": "The Relationship between Anthropometric Variables and Features of Electromyography Signal for Human–Computer Interface", "url": "http://www.igi-global.com/book/applications-challenges-advancements-electromyography-signal/99892", "abstract": "Muscle-computer interfaces (MCIs) based on surface electromyography (EMG) pattern recognition have been developed based on two consecutive components: feature extraction and classification algorithms. Many features and classifiers are proposed and evaluated, which yield the high classification accuracy and the high number of discriminated motions under a single-session experimental condition. However, there are many limitations to use MCIs in the real-world contexts, such as the robustness over time, noise, or low-level EMG activities. Although the selection of the suitable robust features can solve such problems, EMG pattern recognition has to design and train for a particular individual user to reach high accuracy. Due to different body compositions across users, a feasibility to use anthropometric variables to calibrate EMG recognition system automatically/semi-automatically is proposed. This chapter presents the relationships between robust features extracted from actions associated with surface EMG signals and twelve related anthropometric variables. The strong and significant associations presented in this chapter could benefit a further design of the MCIs based on EMG pattern recognition.", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Yann", "last_name": "Laurillau" } }, "year": 2014, "uri": "http://iihm.imag.fr/publication/PQL14a/", "pages": "325-357", "bibtype": "inbook", "id": 682, "editor": "Dr. Ganesh Naik", "address": "Autralia", "date": "2014-05-01", "document": "http://iihm.imag.fr/publs/2014/Full_Chapter_3rd.pdf", "type": "Chapitres d'ouvrages", "booktitle": "Applications, Challenges, and Advancements in Electromyography Signal Processing", "type_publi": "chapitre", "abbr": "PQL14a" }, { "lang": "en", "publisher": "IEEE", "doi": "http://dx.doi.org/10.1109/ECTICon.2013.6559485", "title": "Optimal EMG Amplitude Detectors for Muscle-Computer Interface", "url": "http://ecticon2013.ecticon.org/", "abstract": "To develop an advanced muscle–computer interface (MCI) based on surface electromyography (EMG) signal, a suitable signal processing and classification technique has a key role to play, particularly the selection of EMG features. Two sufficient and well-known methods to extract signal amplitude are root mean square (RMS) and mean absolute value (MAV). Their classification performance is comparable to an advanced and high computational time-scale feature, e.g. discrete wavelet transform. The performance of RMS and MAV, however, depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian, and the PDF of motions associated with EMG signals is still not clear yet. In addition, both features provide the same distribution in feature space, thus only one of them should be used to avoid redundancy in a classification scheme. This study investigated the PDFs of eight hand, wrist and forearm motions and then estimated the signal-to-noise ratio (SNR), defined as a mean value divided by its fluctuation, of both amplitude detectors. On average, the experimental EMG density was closer to the Laplacian density, and MAV had slightly higher SNR than RMS for both forearm extensor and flexor muscles and both genders. Lastly, the accuracy of both features in MCI- based EMG classification was reviewed. For MCI applications, MAV is recommended to be used as an optimal EMG amplitude detector.", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2013, "uri": "http://iihm.imag.fr/publication/PTQ+13a/", "pages": "1-6", "bibtype": "inproceedings", "id": 629, "abbr": "PTQ+13a", "address": "USA", "date": "2013-05-03", "document": "http://iihm.imag.fr/publs/2013/ECTI-CON-Conference-Optimal-EMG-SNR_3rd.pdf", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "Proceedings of the IEEE 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2013)", "type_publi": "icolcomlec" }, { "lang": "en", "publisher": "IEEE", "doi": "http://dx.doi.org/10.3402/jartt.v1i0.22501", "title": "Effects of Window Size and Contraction Types on the Stationarity of Biceps Brachii Muscle EMG Signals", "url": "http://dl.acm.org/citation.cfm?id=2567480&CFID=496452259&CFTOKEN=46792879", "abstract": "In order to analyze surface electromyography (EMG) signals, it is necessary to use techniques based on time (temporal) domain or frequency (spectral) domain. However, these techniques are based on the mathematical assumption of signal stationarity. On the other hand, EMG signal stationarity varies depending on analysis window size and contraction types. So in this paper, a suitable window size for an analysis of EMG during static and dynamic contractions was investigated using a stationarity test, the modified reverse arrangement test. More than 90% of the signals measured during static contraction can be considered as stationary signals for all window sizes. On average, a window size of 375 ms provides the most stationary information, 94.29% of EMG signals for static muscle contraction. For dynamic muscle contraction, the percentage of stationary signals decreased as the window size was increased. If the threshold of 80% stationarity was set to validate stationarity for each window size, a suitable window size should be 250 ms or lesser. For a real-time application that a size of analysis window plus processing time should be less than 300 ms, a window size of 250 ms is suggested for both contraction types.", "authors": { "1": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "2": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "3": { "first_name": "Franck", "last_name": "Quaine" }, "4": { "first_name": "Yann", "last_name": "Laurillau" }, "5": { "first_name": "Wongkittisuksa", "last_name": "Booncharoen" }, "6": { "first_name": "Chusak", "last_name": "Limsakul" }, "7": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "year": 2013, "uri": "http://iihm.imag.fr/publication/TPQ+13a/", "pages": "44:1--44:4", "bibtype": "inproceedings", "id": 643, "abbr": "TPQ+13a", "address": "US", "date": "2013-08-20", "document": "http://iihm.imag.fr/publs/2013/Full_Paper_Stationarity_6th.pdf", "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet", "booktitle": "IEEE 7th International Convention on Rehabilitation Engineering and Assistive Technology 2013 (i-Create 2013)", "type_publi": "icolcomlec" }, { "lang": "en", "volume": 26, "doi": "http://dx.doi.org/10.1016/j.engappai.2013.01.004", "bibtype": "article", "title": "A Feasibility Study on the Use of Anthropometric Variables to Make Muscle-Computer Interface More Practical", "url": "http://www.sciencedirect.com/science/article/pii/S0952197613000146", "abstract": "High classification accuracy has been achieved for muscle–computer interfaces (MCIs) based on surface electromyography (EMG) recognition in many recent works with an increasing number of discrimi- nated movements. However, there are many limitations to use these interfaces in the real-world contexts. One of the major problems is compatibility. Designing and training the classification EMG system for a particular individual user is needed in order to reach high accuracy. If the system can calibrate itself automatically/semi-automatically, the development of standard interfaces that are compatible with almost any user could be possible. Twelve anthropometric variables, a measurement of body dimensions, have been proposed and used to calibrate the system in two different ways: a weighting factor for a classifier and a normalizing value for EMG features. The experimental results showed that a number of relationships between anthropometric variables and EMG time-domain features from upper-limb muscles and movements are statistically strong and significant. In this paper, the feasibility to use anthropometric variables to calibrate the EMG classification system is shown obviously and the proposed calibration technique is suggested to further improve the robustness and practical use of MCIs based on EMG pattern recognition.", "publisher": "Elsevier", "year": 2013, "uri": "http://iihm.imag.fr/publication/PQC+13a/", "pages": "1681-1688", "note": "IF 1.84", "id": 614, "abbr": "PQC+13a", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Sylvie", "last_name": "Charbonnier" }, "4": { "first_name": "Christine", "last_name": "Serviere" }, "5": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "6": { "first_name": "Yann", "last_name": "Laurillau" } }, "date": "2013-01-20", "document": "http://iihm.imag.fr/publs/2013/Manuscript_Anthropometric1_4thDraft.pdf", "type": "Revues internationales avec comité de lecture", "journal": "International Scientific Journal Engineering Applications of Artificial Intelligence", "type_publi": "irevcomlec" }, { "bibtype": "article", "publisher": "World Scientific Company", "doi": "http://doi.org/10.1142/S0219477513500168", "lang": "en", "uri": "http://iihm.imag.fr/publication/PQL+13a/", "title": "EMG Amplitude Estimators Based on Probability Distribution for Muscle-Computer Interface", "url": "http://www.worldscientific.com/doi/abs/10.1142/S0219477513500168", "journal": "Fluctuation and Noise Letters", "year": 2013, "number": 3, "pages": "1350016-1-1350016-18", "volume": 12, "id": 642, "abbr": "PQL+13a", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Yann", "last_name": "Laurillau" }, "4": { "first_name": "Sirinee", "last_name": "Thongpanja" }, "5": { "first_name": "Chusak", "last_name": "Limsakul" }, "6": { "first_name": "Pornchai", "last_name": "Phukpattaranont" } }, "date": "2013-08-20", "document": "http://iihm.imag.fr/publs/2013/Manuscript_RMSMAV_3rdDraft.pdf", "type": "Revues internationales avec comité de lecture", "abstract": "To develop an advanced muscle–computer interface (MCI) based on surface electromyo- graphy (EMG) signal, the amplitude estimations of muscle activities, i.e., root mean square (RMS) and mean absolute value (MAV) are widely used as a convenient and accurate input for a recognition system. Their classification performance is comparable to advanced and high computational time-scale methods, i.e., the wavelet transform. However, the signal-to-noise-ratio (SNR) performance of RMS and MAV depends on a probability density function (PDF) of EMG signals, i.e., Gaussian or Laplacian. The PDF of upper-limb motions associated with EMG signals is still not clear, especially for dynamic muscle contraction. In this paper, the EMG PDF is investigated based on surface EMG recorded during finger, hand, wrist and forearm motions. The results show that on average the experimental EMG PDF is closer to a Laplacian density, partic- ularly for male subject and flexor muscle. For the amplitude estimation, MAV has a higher SNR, defined as the mean feature divided by its fluctuation, than RMS. Due to a same discrimination of RMS and MAV in feature space, MAV is recommended to be used as a suitable EMG amplitude estimator for EMG-based MCIs.", "type_publi": "irevcomlec" }, { "bibtype": "article", "volume": 40, "doi": "http://dx.doi.org/10.1016/j.eswa.2013.02.023", "lang": "en", "uri": "http://iihm.imag.fr/publication/PQC+13b/", "title": "EMG Feature Evaluation for Improving Myoelectric Pattern Recognition Robustness", "url": "http://www.sciencedirect.com.gate6.inist.fr/science/article/pii/S0957417413001395", "abstract": "In pattern recognition-based myoelectric control, high accuracy for multiple discriminated motions is presented in most of related literature. However, there is a gap between the classification accuracy and the usability of practical applications of myoelectric control, especially the effect of long-term usage. This paper proposes and investigates the behavior of fifty time-domain and frequency-domain features to classify ten upper limb motions using electromyographic data recorded during 21 days. The most stable single feature and multiple feature sets are presented with the optimum configuration of myoelectric control, i.e. data segmentation and classifier. The result shows that sample entropy (SampEn) outperforms other features when compared using linear discriminant analysis (LDA), a robust classifier. The averaged test classification accuracy is 93.37%, when trained in only initial first day. It brings only 2.45% decrease compared with retraining schemes. Increasing number of features to four, which consists of SampEn, the fourth order cepstrum coefficients, root mean square and waveform length, increase the classification accuracy to 98.87%. The proposed techniques achieve to maintain the high accuracy without the retraining scheme. Additionally, this continuous classification allows the real-time operation.", "publisher": "Elsevier", "year": 2013, "number": 12, "pages": "4832–4840", "note": "IF: 2.203", "id": 616, "abbr": "PQC+13b", "authors": { "1": { "first_name": "Angkoon", "last_name": "Phinyomark" }, "2": { "first_name": "Franck", "last_name": "Quaine" }, "3": { "first_name": "Sylvie", "last_name": "Charbonnier" }, "4": { "first_name": "Christine", "last_name": "Serviere" }, "5": { "first_name": "Franck", "last_name": "Tarpin-Bernard" }, "6": { "first_name": "Yann", "last_name": "Laurillau" } }, "date": "2013-02-22", "document": "http://iihm.imag.fr/publs/2013/ESWA-Draft.pdf", "type": "Revues internationales avec comité de lecture", "journal": "Expert Systems with Applications", "type_publi": "irevcomlec" }]);