Poster Open Access

Feedback matters – or doesn't it? User training for pattern-recognition controlled prostheses (Abstract title: User training for pattern-recognition based myoelectric prostheses using a serious game)

Kristoffersen, Morten Bak; Franzke, Andreas; Murgia, Alessio; van der Sluis, Corry; Bongers, Raoul


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    "creators": [
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        "affiliation": "University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, the Netherlands", 
        "name": "Kristoffersen, Morten Bak", 
        "orcid": "0000-0002-3901-2856"
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      {
        "affiliation": "University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, the Netherlands", 
        "name": "Franzke, Andreas"
      }, 
      {
        "affiliation": "University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, the Netherlands", 
        "name": "Murgia, Alessio"
      }, 
      {
        "affiliation": "University of Groningen, University Medical Center Groningen, Department of Rehabilitation Medicine, Groningen, the Netherlands", 
        "name": "van der Sluis, Corry"
      }, 
      {
        "affiliation": "University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, the Netherlands", 
        "name": "Bongers, Raoul"
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    "description": "<p>Please note there was a problem with the upload. The file is a pdf, but the file ending is missing. Manually opening the file in a pdf reader should work. Otherwise feel free to contact me.&nbsp;</p>\n\n<p>Abstract</p>\n\n<p>Individuals with upper-limb deficiency who are fitted<br>\nwith a prosthesis are normally trained in the use of such<br>\ndevice. This is even true for individuals who are fitted with a<br>\nmyoelectric prosthesis that uses control algorithms based on<br>\npattern-recognition, despite the intent of pattern-recognition<br>\ncontrol of exploiting &ldquo;intuitive&rdquo; phantom movements.<br>\nConventionally, training individuals for pattern-recognition<br>\ncontrol usually involves an expert who guides the user to<br>\nproduce electromyogram (EMG) signals that optimize<br>\npattern recognition. In the training the individual is<br>\nstimulated to adapt their EMG signals as to make them more<br>\ndistinct in terms of the resulting patterns. To achieve this, for<br>\ninstance, small movements can be added to the basic pattern,<br>\nsuch as flexing the little finger during open hand. Although<br>\ntraining improves online accuracy it still involves<br>\nconsiderable trial and error. Moreover, expert guidance is<br>\ncurrently done based on visual perusal of EMG patterns or<br>\nfeatures thereof and not based on specific metrics<br>\ncharacterizing those EMG signal patterns. Rather than using<br>\nintuitive phantom movements for control, we instead propose<br>\nto use those phantom movements which are most distinct in<br>\nterms of EMG. To find the set of phantom movements that<br>\nprovides the most distinct EMG activation patterns, we<br>\npropose to use a serious game. Using a game, we can train<br>\nindividuals to make EMG patterns distinct while performing<br>\nthem in a robust manner. This game is controlled using the<br>\nEMG captured from 8 electrodes positioned around the<br>\nforearm. Inspired by the work of Radhakrishnan et. al and<br>\nPistohl et. al, the EMG from each electrode is mapped to a<br>\ndirection of the game avatar in the 2D environment. We<br>\nhypothesize that this training will make individuals utilize<br>\ntheir EMG activation space to a greater extent and become<br>\nbetter at generating only EMG activity at specific electrode<br>\nsites so that patterns are more distinct.<br>\nWe are currently conducting an experiment in which 4<br>\nexperimental groups receive different kinds of training.<br>\nGroup 1 receives conventional training without coaching.<br>\nGroup 2 receives conventional training with feedback. Group<br>\n3 receives training with the proposed serious game and group<br>\n4 receives training without any feedback (control). The<br>\nlearning effects between groups are analysed using the<br>\nmetrics proposed by Bunderson et al. and the motion test.</p>", 
    "doi": "10.5281/zenodo.1217832", 
    "grants": [
      {
        "acronym": "INPUT", 
        "code": "687795", 
        "funder": {
          "acronyms": [
            "EC"
          ], 
          "doi": "10.13039/501100000780", 
          "links": {
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          "name": "European Commission"
        }, 
        "links": {
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        }, 
        "program": "H2020", 
        "title": "Intuitive Natural Prosthesis UTilization"
      }
    ], 
    "keywords": [
      "Serious games", 
      "User training", 
      "Motor learning", 
      "Prosthesis", 
      "Myocontrol", 
      "Pattern-recognition"
    ], 
    "license": {
      "id": "CC-BY-4.0"
    }, 
    "meeting": {
      "acronym": "MEC", 
      "dates": "15-08-2017 - 18-08-2017", 
      "place": "Fredericton, New Brunswick, Canada", 
      "title": "Myoelectric Controls Symposium", 
      "url": "http://www.unb.ca/research/institutes/biomedical/mec/"
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    "publication_date": "2017-08-15", 
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    "title": "Feedback matters \u2013 or doesn't it? User training for pattern-recognition controlled prostheses (Abstract title: User training for pattern-recognition based myoelectric prostheses using a serious game)"
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