Wavelet transform methods for identifying onset of SEMG activity

Quantifying improvements in motor control is predicated on the accurate identification of the onset of surface electromyograpic (sEMG) activity. Applying methods from wavelet theory developed in the past decade to digitized signals, a robust algorithm has been designed for use with sEMG collected during reaching tasks executed with the less-affected arm of stroke patients. The method applied both Discretized Continuous Wavelet Transforms (CWT) and Discrete Wavelet Transforms (DWT) for event detection and no-lag filtering, respectively. Input parameters were extracted from the assessed signals. The onset times found in the sEMG signals using the wavelet method were compared with physiological instants of motion onset, determined from video data. Robustness was evaluated by considering the response in onset time with variations of input parameter values. The wavelet method found physiologically relevant onset times in all signals, averaging 147 ms prior to motion onset, compared to predicted onset latencies of 90-110 ins. Latency exhibited slight dependence on subject, but no other variables.

Name thesis:degree_name

Master of Science in Biomedical Engineering - (M.S.)

Discipline thesis:degree_discipline

Biomedical Engineering

Year

2004

Author dc:creator

• Wilen, Janina

Contributors dc:contributor

• Stanley S. Reisman
• Richard A. Foulds
• Gail Forrest

• Surface electromyographic activity
• Motor control
• Wavelet transform
• Biomedical Engineering and Bioengineering

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/535

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1534