This paper represents a new application for existing classification techniques. A robotic worm device being developed for human endoscopy, fitted with a 3-axis accelerometer was driven over a variety of surfaces and the accelerometer data was used to identify, which surface the robot worm found itself. Within the Weka environment, three available classifiers, J48, LIBSVM and Perceptron were tested with both Fast Fourier Transform (FFT) and Mel-Frequency Cepstral Coefficients (MFCC) extraction techniques, frame sizes of 0.5 and 2 seconds. The highest testing accuracy demonstrated for this surface classification, was 83%. It is hoped that this machine learning will improve the operational use of the robot with the system identifying surface types and, later surface properties of hard to reach anatomical regions, both for locomotive efficiency and medical information.
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