@InProceedings{8081366,
  author = {M. E. Benalcázar and A. G. Jaramillo and {Jonathan} and A. Zea and A. Páez and V. H. Andaluz},
  booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
  title = {Hand gesture recognition using machine learning and the Myo armband},
  year = {2017},
  pages = {1040-1044},
  abstract = {Gesture recognition has multiple applications in medical and engineering fields. The problem of hand gesture recognition consists of identifying, at any moment, a given gesture performed by the hand. In this work, we propose a new model for hand gesture recognition in real time. The input of this model is the surface electromyography measured by the commercial sensor the Myo armband placed on the forearm. The output is the label of the gesture executed by the user at any time. The proposed model is based on the Λ-nearest neighbor and dynamic time warping algorithms. This model can learn to recognize any gesture of the hand. To evaluate the performance of our model, we measured and compared its accuracy at recognizing 5 classes of gestures to the accuracy of the proprietary system of the Myo armband. As a result of this evaluation, we determined that our model performs better (86% accurate) than the Myo system (83%).},
  keywords = {electromyography;gesture recognition;learning (artificial intelligence);hand gesture recognition;machine learning;EMG;k-nearest neighbor;dynamic time warping algorithm;electromyography;Electromyography;Muscles;Gesture recognition;Real-time systems;Feature extraction;Hidden Markov models;Heuristic algorithms;Hand gesture recogntion;EMG;machine learning;k-nearest neighbor;dynamic time warping algorithm},
  doi = {10.23919/EUSIPCO.2017.8081366},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347665.pdf},
}

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H."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["M.","E."],"propositions":[],"lastnames":["Benalcázar"],"suffixes":[]},{"firstnames":["A.","G."],"propositions":[],"lastnames":["Jaramillo"],"suffixes":[]},{"firstnames":[],"propositions":[],"lastnames":["Jonathan"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Zea"],"suffixes":[]},{"firstnames":["A."],"propositions":[],"lastnames":["Páez"],"suffixes":[]},{"firstnames":["V.","H."],"propositions":[],"lastnames":["Andaluz"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Hand gesture recognition using machine learning and the Myo armband","year":"2017","pages":"1040-1044","abstract":"Gesture recognition has multiple applications in medical and engineering fields. The problem of hand gesture recognition consists of identifying, at any moment, a given gesture performed by the hand. In this work, we propose a new model for hand gesture recognition in real time. The input of this model is the surface electromyography measured by the commercial sensor the Myo armband placed on the forearm. The output is the label of the gesture executed by the user at any time. The proposed model is based on the Λ-nearest neighbor and dynamic time warping algorithms. This model can learn to recognize any gesture of the hand. To evaluate the performance of our model, we measured and compared its accuracy at recognizing 5 classes of gestures to the accuracy of the proprietary system of the Myo armband. As a result of this evaluation, we determined that our model performs better (86% accurate) than the Myo system (83%).","keywords":"electromyography;gesture recognition;learning (artificial intelligence);hand gesture recognition;machine learning;EMG;k-nearest neighbor;dynamic time warping algorithm;electromyography;Electromyography;Muscles;Gesture recognition;Real-time systems;Feature extraction;Hidden Markov models;Heuristic algorithms;Hand gesture recogntion;EMG;machine learning;k-nearest neighbor;dynamic time warping algorithm","doi":"10.23919/EUSIPCO.2017.8081366","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570347665.pdf","bibtex":"@InProceedings{8081366,\n author = {M. E. Benalcázar and A. G. Jaramillo and {Jonathan} and A. Zea and A. Páez and V. H. Andaluz},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Hand gesture recognition using machine learning and the Myo armband},\n year = {2017},\n pages = {1040-1044},\n abstract = {Gesture recognition has multiple applications in medical and engineering fields. The problem of hand gesture recognition consists of identifying, at any moment, a given gesture performed by the hand. In this work, we propose a new model for hand gesture recognition in real time. The input of this model is the surface electromyography measured by the commercial sensor the Myo armband placed on the forearm. The output is the label of the gesture executed by the user at any time. The proposed model is based on the Λ-nearest neighbor and dynamic time warping algorithms. This model can learn to recognize any gesture of the hand. 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