A jaw based human-machine interface with machine learning

Resource type
Tobias Busch, Jennifer Zeilfelder, Kai Zhou, Wilhelm Stork
For a successful classification of jaw movement signals three different machine learning methods are implemented and evaluated: A Naive Bayes classifier, a Support Vector Machine (SVM) and a Convolutional Neuronal Network (CNN). In this case the present paper deals with the definition of intuitive jaw movements and their detection by a prototypical system for non-invasive measurement of pressure values in the closed external auditory canal. For this purpose, a database with 1013 recordings was created. The evaluation shows that all three learning methods provide a good recognition level. The Bayes classifier achieves with the used, small dataset with nearly 95% the best detection probability with realtime data. At the same time, the study shows that individual pressure profiles can severely impair recognition. Hence, the creation of a comprehensive database is required. Overall, this paper demonstrates that successful detection and differentiation of up to five jaw movement directions is possible. In total it could be shown using machine learning for jaw movement classification is a stable method with a high recognition level. However, the quality of classification increases with the size and diversity of the dataset.
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Research focus
Medical Information Technology
ROBINA – Robot-supported services for individual- and resource-focused intensive and palliative care for people with ALS
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Published by
Jennifer Zeilfelder