High energy spectrogram with integrated prior knowledge for EMG-based locomotion classification

• Published in: Medical engineering & physics Vol. 37; no. 5; pp. 518 - 524
• Main Authors: Joshi, Deepak, Nakamura, Bryson H., Hahn, Michael E.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01.05.2015
• Subjects: Accelerometry - methods; Algorithms; Electromyography; Electromyography - methods; Female; Gait cycle; Humans; Leg - physiology; Locomotion; Locomotion - physiology; Male; Muscle, Skeletal - physiology; Myoelectric control; Radiology; Signal Processing, Computer-Assisted; Spectrogram; Time Factors; Time-frequency; Young Adult; Myoelectric control; Locomotion; Time-frequency; Electromyography; Gait cycle; Spectrogram
• ISSN: 1350-4533; 1873-4030
• DOI: 10.1016/j.medengphy.2015.03.001

•Transition between locomotion modes is critical to activities of daily living.•A spectrogram approach is used to classify locomotion and transitions using EMG.•Use of prior knowledge with the spectrogram enhances the classification structure.•This approach can aid the control of assistive devices in multi-mode control. 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.