Subject independent computational framework for myoelectric signals

A subject independent computational framework is one which do not require to be calibrated by the specific subject data to be ready to be used on the subject. The greatest challenge in developing such a framework is the variation in parameters across subjects which is termed as subject based variability. Subject based variability is the variability in data across subjects for the same task, activity or physiological condition. Physiological signals are highly subject specific in nature. Myoelectric signals are one such physiological signals generated in the muscles during any musco-skeletal activity of the body. Spectral and amplitude variations in the myoelectric signals are analyzed to determine the physiological status of a muscle with respect to the intensity of activity and the fatigue state of the muscle. But variations in the spectrum and magnitude of myoelectric signals across subjects pose a great challenge in developing a generalized framework for detecting physiological status of the muscle. In this paper we present statistical tools and techniques to measure subject based variability in myoelectric signals and also present a novel feature selection method based on robustness to subject based variability, with the aim of developing a subject independent measurement framework for fatigue using myoelectric signals. The proposed method provides a subject independent classification accuracy of 80.65%, which is an improvement of 10% to 18% compared to the existing techniques when tested with a wide range of classifiers such SVM, HMM, AdaBoost and KNN. More information and source code are available from the authors.