title: High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification.
abstract: Electromyogram (EMG) signal representation is crucial in classification applications specific to locomotion and transitions. For a given signal, classification can be performed using discriminant functions or if-else rule sets, using learning algorithms derived from training examples. In the present work, a spectrogram based approach was developed to classify (EMG) signals for locomotion mode. Spectrograms for each muscle were calculated and summed to develop a histogram. If-else rules were used to classify test data based on a matching score. Prior knowledge of locomotion type reduced class space to exclusive locomotion modes. The EMG data were collected from seven leg muscles in a sample of able-bodied subjects while walking over ground (W), ascending stairs (SA) and the transition between (W-SA). Three muscles with least discriminating power were removed from the original data set to examine the effect on classification accuracy. Initial classification error was <20% across all modes, using leave one out cross validation. Use of prior knowledge reduced the average classification error to <11%. Removing three EMG channels decreased the classification accuracy by 10.8%, 24.3%, and 8.1% for W, W-SA, and SA respectively, and reduced computation time by 42.8%. This approach may be useful in the control of multi-mode assistive devices.
authors: Department of Human Physiology, University of Oregon, Eugene, OR 97403-1240, United States . Electronic address: joshideepak2004@yahoo.co.in.
authors: Deepak
authors: D
authors: Joshi
authors: Department of Human Physiology, University of Oregon, Eugene, OR 97403-1240, United States . Electronic address: bnakamur@uoregon.edu.
authors: Bryson H
authors: BH
authors: Nakamura
authors: Department of Human Physiology, University of Oregon, Eugene, OR 97403-1240, United States . Electronic address: mhahn@uoregon.edu.
authors: Michael E
authors: ME
authors: Hahn
mesh_terms: D061725:Accelerometry / Q000379:methods*; D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005260:Female; D006801:Humans; D007866:Leg / Q000502:physiology; D008124:Locomotion / Q000502:physiology*; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055815:Young Adult
keywords: Electromyography; Gait cycle; Locomotion; Myoelectric control; Spectrogram; Time-frequency