publications([{
   "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"
},
{
   "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",
   "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"
}]);