• DocumentCode
    662959
  • Title

    Evaluating upper-limb EMG-prosthesis user performance by combining psychometric measures and classification-rates

  • Author

    Nan Ge ; Goebel, Peter M. ; Amsuess, Sebastian ; Paredes, L. ; Pawlik, Roland ; Farina, Dario

  • Author_Institution
    Dept. of Strategy & Technol. Manage., Otto Bock Healthcare Products GmbH, Vienna, Austria
  • fYear
    2013
  • fDate
    6-8 Nov. 2013
  • Firstpage
    359
  • Lastpage
    362
  • Abstract
    The robustness of myo-electric prosthesis usage is largely influenced by user performance, where psychological factors (i.e. cognitive-skills, motor-skills and psychological status - such as motivation, will, and stress) play a prominent role. These factors become more important the more degrees of freedom (DOF) a multifunctional prosthesis provides. Despite the large amount of research efforts during the past decades on developing robust control and feedback methods, there has been limited attention on the importance of the aforementioned human factors on the usability of the prosthesis. Psychometric measures are necessary to get a better view on user-ability in prosthesis control. To achieve this aim, the work presented herein applies Item-Response Theory (IRT), which is a psychometric instrument that has been well established for testing human abilities, to introduce a novel score of user performance. This score can be utilized to judge the user´s performance at different stages of training, which means measuring improvement or deterioration in movement muscle control according to training activities. As pattern recognition is the control method of choice, classification-rate is taken as second information on the discrimination and repeatability of the recorded movement related EMG patterns. It is used to update the IRT score by taking the joint probability, which combines both measures to determine then the user performance score in a more meaningful way. The score was calibrated on well-trained, able-bodied subjects, who act as a “gold” standard when their movement error was below a certain threshold. Then four amputees with different training experience were selected and it was verified that the score could distinguish between them.
  • Keywords
    cognition; electromyography; feature extraction; feedback; medical control systems; medical signal processing; motion control; prosthetics; psychometric testing; robust control; signal classification; classification rates; cognitive skills; degrees-of-freedom; feedback methods; human ability testing; human factors; item-response theory; joint probability; motivation; motor skills; movement error; movement muscle control; movement related EMG patterns recording; multifunctional prosthesis; myoelectric prosthesis; pattern recognition; prosthesis control; psychological factors; psychological status; psychometric instrument; psychometric measures; robust control; stress; training experience; upper-limb EMG prosthesis user performance; well-trained able-bodied subjects; Electromyography; Muscles; Prosthetics; Psychology; Sociology; Tracking; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Engineering (NER), 2013 6th International IEEE/EMBS Conference on
  • Conference_Location
    San Diego, CA
  • ISSN
    1948-3546
  • Type

    conf

  • DOI
    10.1109/NER.2013.6695946
  • Filename
    6695946