title: The effect of involuntary motor activity on myoelectric pattern recognition: a case study with chronic stroke patients.
abstract: OBJECTIVE
  This study investigates the effect of the involuntary motor activity of paretic-spastic muscles on the classification of surface electromyography (EMG) signals.
  
  
  METHODS
  Two data collection sessions were designed for 8 stroke subjects to voluntarily perform 11 functional movements using their affected forearm and hand at relatively slow and fast speeds. For each stroke subject, the degree of involuntary motor activity present in the voluntary surface EMG recordings was qualitatively described from such slow and fast experimental protocols. Myoelectric pattern recognition analysis was performed using different combinations of voluntary surface EMG data recorded from the slow and fast sessions.
  
  
  RESULTS
  Across all tested stroke subjects, our results revealed that when involuntary surface EMG is absent or present in both the training and testing datasets, high accuracies (>96%, >98%, respectively, averaged over all the subjects) can be achieved in the classification of different movements using surface EMG signals from paretic muscles. When involuntary surface EMG was solely involved in either the training or testing datasets, the classification accuracies were dramatically reduced (<89%, <85%, respectively). However, if both the training and testing datasets contained EMG signals with the presence and absence of involuntary EMG interference, high accuracies were still achieved (>97%).
  
  
  CONCLUSIONS
  The findings of this study can be used to guide the appropriate design and implementation of myoelectric pattern recognition based systems or devices toward promoting robot-aided therapy for stroke rehabilitation.
authors.affiliation: Institute of Biomedical Engineering, University of Science and Technology of China, Hefei, People's Republic of China.
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.forename: Yun
authors.initials: Y
authors.lastname: Li
authors.forename: Xiang
authors.initials: X
authors.lastname: Chen
authors.forename: Guanglin
authors.initials: G
authors.lastname: Li
authors.forename: William Zev
authors.initials: WZ
authors.lastname: Rymer
authors.forename: Ping
authors.initials: P
authors.lastname: Zhou
mesh_terms: D000368:Aged; D004576:Electromyography / Q000379:methods*; D004867:Equipment Design; D019544:Equipment Failure Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009119:Muscle Contraction; D009128:Muscle Spasticity / Q000209:etiology / Q000503:physiopathology*; D018482:Muscle, Skeletal / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D020521:Stroke / Q000150:complications / Q000503:physiopathology*; D014836:Volition