title: Selection of suitable hand gestures for reliable myoelectric human computer interface.
abstract: BACKGROUND
  Myoelectric controlled prosthetic hand requires machine based identification of hand gestures using surface electromyogram (sEMG) recorded from the forearm muscles. This study has observed that a sub-set of the hand gestures have to be selected for an accurate automated hand gesture recognition, and reports a method to select these gestures to maximize the sensitivity and specificity.
  
  
  METHODS
  Experiments were conducted where sEMG was recorded from the muscles of the forearm while subjects performed hand gestures and then was classified off-line. The performances of ten gestures were ranked using the proposed Positive-Negative Performance Measurement Index (PNM), generated by a series of confusion matrices.
  
  
  RESULTS
  When using all the ten gestures, the sensitivity and specificity was 80.0% and 97.8%. After ranking the gestures using the PNM, six gestures were selected and these gave sensitivity and specificity greater than 95% (96.5% and 99.3%); Hand open, Hand close, Little finger flexion, Ring finger flexion, Middle finger flexion and Thumb flexion.
  
  
  CONCLUSIONS
  This work has shown that reliable myoelectric based human computer interface systems require careful selection of the gestures that have to be recognized and without such selection, the reliability is poor.
authors.affiliation: Electrical Engineering Department, Centro Universitário da FEI, Av. Humberto de A. C. Branco, 3.972, São Bernardo do Campo, SP, 09850-901, Brazil. mclaudia@fei.edu.br.
authors.forename: Maria Claudia F
authors.initials: MC
authors.lastname: Castro
authors.affiliation: Biosignal Lab., School of Electrical and Computer Engineering, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia. sridhar.arjunan@rmit.edu.au.
authors.forename: Sridhar P
authors.initials: SP
authors.lastname: Arjunan
authors.affiliation: Biosignal Lab., School of Electrical and Computer Engineering, RMIT University, GPO Box 2476, Melbourne, VIC, 3001, Australia. dinesh@rmit.edu.au.
authors.forename: Dinesh K
authors.initials: DK
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D001186:Artificial Limbs; D004576:Electromyography; D005542:Forearm / Q000502:physiology; D005868:Gestures; D006225:Hand; D006801:Humans; D000069550:Machine Learning; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014584:User-Computer Interface; D055815:Young Adult