Learning Probabilistic Classifiers from Electromyography Data for Predicting Knee Abnormalities

Identifying movement abnormalities from raw Electromyography (EMG) data requires three steps that are the data pre-processing, the feature extraction and training a classifier. As EMG data shows large variation (even for consecutive trials in a single subject) probabilistic classifiers like naive Bayes or probabilistic support vector machines have been proposed. The used feature representations (e.g., PCA, NMF, wavelet transformation) however, can not capture the variation. Here, we propose a fully Bayesian approach where both, the features and the classifier, are probabilistic models. The generative model reproduces the observed variance in the EMG data, provides an estimate of the reliability of the predictions and can be applied in combination with dimensionality reduction techniques such as PCA and NMF. We found the optimal number of components and Gaussians for each model and tuned their metaparameters. Besides the the focus on the four EMG channels, we tested the knee angle alone and EMG channels with the knee angle. We found that these probabilistic extensions outperforms classical approaches in terms of the prediction of knee abnormalities from few samples. We also show that the robustness against noise of the proposed probabilist model is superior than classical methods.