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

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.