Learning Probabilistic Features from EMG Data for Predicting Knee Abnormalities

Abstract

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., principal components analysis, non negative matrix factorization, 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. In first tests, we found that these probabilistic extensions outperforms classical approaches in terms of the prediction of knee abnormalities from few samples with a performance of 86 percent of correctly classified abnormalities.