Real – Time Locomotion Classification using Transient Surface EMG signals

Real – Time Locomotion Classification using Transient Surface EMG signals Sarthak Pati1, Deepak Joshi2, Ashutosh Mishra2 and Sneh Anand2 1 – Dept. Of Biomedical Engineering, Manipal University 2 – Center for Biomedical Engineering, IIT – Delhi

Contents • Introduction to EMG and its acquisition • Importance of EMG • Pre – Processing of EMG signals • Features under consideration • Classifier design

What is EMG ? • It is a signal used to evaluate the electrical activity produced by skeletal muscles. Fig 1 : EMG Signal of Healthy Subject

Block Diagram

EMG Surface Electrodes Fig 2 : EMG Surface Electrodes Image Courtesy : Orthotics and Prosthetics Lab, BME Unit, AIIMS

Electrode Placement Fig 3 : Electrode Placement Diagram Image Courtesy : Orthotics and Prosthetics Lab, BME Unit, AIIMS

Importance of EMG • Diagnosis of • Neuro - Muscular Disorders • Motor Control Disorders • Prosthetic Control • Sensing of Isometric Motor Activity (motion–less gestures) • Flight control (Human Senses Group, NASA) • Machine–Human Interfacing (Advanced Robotics, MIT)

Why EMG for this study ? • Relatively easy to acquire and process • If properly utilised, gives good accuracy for control systems • High sensitivity • Single Muscle Recording Possible • Access to Deep Musculature • Little cross – talk concern

EMG – Signal Processing Fig 4 : Frequency Response of Band Pass Filter

Feature Selection • Criteria : • Computational Efficiency • High separability with respect to locomotion modes

Classifier Design • Obtaining LDA Transformation Matrix T • Each Locomotion Mode mapped to a single dimension data set using T • Threshold – based approach for classification

Results Fig 5 : LDA classification between all the four locomotion modes

Continued… Fig 6 : LDA classification between FW and SW

References • Deepak Joshi, Sneh Anand - Study of circular cross correlation and phase lag to estimate knee angle: an application to prosthesis; Int. J. Biomechatronics and Biomedical Robotics [in press] • Hargrove L. J., Huang H., Schultz A. E., Lock B. A., Lipschutz R., Kuiken T. A. - Toward the Development of a Neural Interface for Lower Limb Prosthesis Control; Delsys Prize Winner • Parker P., Englehart K., Hudgins B. - Myoelectric signal processing for control of powered limb prostheses; Journal of Electromyography and Kinesiology • Englehart K., Hudgins B. - A Robust, Real-Time Control Scheme for Multifunction Myoelectric Control; IEEE Transactions on Biomedical Engineering, Vol.50, No.7 • Chan F.H.Y., Yang Y.S., Lam F.K., Zhang Y.T., Parker P.A. - Fuzzy EMG Classification for Prosthesis Control; IEEE Transactions on Rehabilitation Engineering, Vol.8, No.3 • Englehart K., Hudgins B., Parker P., Maryhelen S. - Time-Frequency Representation for Classification of The Transient Myoelectric Signal; 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No 5 • Phinyomark A., Limsakul C., Phukpattaranont P. - A Novel Feature Extraction for Robust EMG Pattern Recognition; Journal of Computing, Vol 1, Issue 1, ISSN: 2151-9617

Thank You Any questions…?

Real – Time Locomotion Classification using Transient Surface EMG signals