title: User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control.
abstract: OBJECTIVE
  Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses.
  
  
  METHODS
  In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees.
  
  
  RESULTS
  It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance.
  
  
  CONCLUSIONS
  These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.
authors.affiliation: State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
authors.forename: Jiayuan
authors.initials: J
authors.lastname: He
authors.forename: Dingguo
authors.initials: D
authors.lastname: Zhang
authors.forename: Ning
authors.initials: N
authors.lastname: Jiang
authors.forename: Xinjun
authors.initials: X
authors.lastname: Sheng
authors.forename: Dario
authors.initials: D
authors.lastname: Farina
authors.forename: Xiangyang
authors.initials: X
authors.lastname: Zhu
mesh_terms: D000222:Adaptation, Physiological / Q000502:physiology*; D000328:Adult; D001132:Arm; D004576:Electromyography / Q000379:methods*; D025461:Feedback, Physiological / Q000502:physiology; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008297:Male; D008328:Man-Machine Systems; D008875:Middle Aged; D009068:Movement / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D019736:Prostheses and Implants; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis