@article{Biron_Englehart_Parker_2012, title={A Study of Kalman Filtering Applied to Myoelectric Signal State Tracking}, volume={35}, url={https://proceedings.cmbes.ca/index.php/proceedings/article/view/754}, abstractNote={<p>The goal of this work is to reduce the response time of pattern recognition based myoelectric prostheses without compromising stability. A Kalman filter (KF) was applied in feature-space to track class transitions and to determine when features have converged towards steady-state class. The system was tested against data collected during continuous movement where subjects transitioned between seven forearm and hand motions. For various data acquisition times, the signal-to-noise-ratio obtained from filtered and non-filtered features were compared, and the system classification accuracy and processing time were compared against state-of-the-art systems. Results show that while applying the proposed system, data acquisition time can be reduced from 100ms to 20ms without compromise to the system’s classification accuracy.</p>}, journal={CMBES Proceedings}, author={Biron, Katerina and Englehart, Kevin and Parker, Philip}, year={2012}, month={Jun.} }