title: A real-time, practical sensor fault-tolerant module for robust EMG pattern recognition.
abstract: BACKGROUND
  Unreliability of surface EMG recordings over time is a challenge for applying the EMG pattern recognition (PR)-controlled prostheses in clinical practice. Our previous study proposed a sensor fault-tolerant module (SFTM) by utilizing redundant information in multiple EMG signals. The SFTM consists of multiple sensor fault detectors and a self-recovery mechanism that can identify anomaly in EMG signals and remove the recordings of the disturbed signals from the input of the pattern classifier to recover the PR performance. While the proposed SFTM has shown great promise, the previous design is impractical. A practical SFTM has to be fast enough, lightweight, automatic, and robust under different conditions with or without disturbances.
  
  
  METHODS
  This paper presented a real-time, practical SFTM towards robust EMG PR. A novel fast LDA retraining algorithm and a fully automatic sensor fault detector based on outlier detection were developed, which allowed the SFTM to promptly detect disturbances and recover the PR performance immediately. These components of SFTM were then integrated with the EMG PR module and tested on five able-bodied subjects and a transradial amputee in real-time for classifying multiple hand and wrist motions under different conditions with different disturbance types and levels.
  
  
  RESULTS
  The proposed fast LDA retraining algorithm significantly shortened the retraining time from nearly 1 s to less than 4 ms when tested on the embedded system prototype, which demonstrated the feasibility of a nearly "zero-delay" SFTM that is imperceptible to the users. The results of the real-time tests suggested that the SFTM was able to handle different types of disturbances investigated in this study and significantly improve the classification performance when one or multiple EMG signals were disturbed. In addition, the SFTM could also maintain the system's classification performance when there was no disturbance.
  
  
  CONCLUSIONS
  This paper presented a real-time, lightweight, and automatic SFTM, which paved the way for reliable and robust EMG PR for prosthesis control.
authors.affiliation: School of Engineering, San Francisco State University, 1600 Holloway Ave, San Francisco, CA, USA. xrzhang@sfsu.edu.
authors.forename: Xiaorong
authors.initials: X
authors.lastname: Zhang
authors.affiliation: NCSU/UNC Rehabilitation Engineering Center (REC), NCSU/UNC Department of Biomedical Engineering, North Carolina State University, 4402C Engineering Building III, Raleigh, NC, USA. helen-huang@unc.edu.|University of North Carolina at Chapel Hill, 150A MacNider Hall, Chapel Hill, NC, USA. helen-huang@unc.edu.
authors.forename: He
authors.initials: H
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000674:Amputees; D001132:Arm; D001186:Artificial Limbs; D057005:Bioengineering / Q000295:instrumentation / Q000379:methods; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods*; D005260:Female; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors