Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses

Publication Type Conference Paper
Authors Rui Zhang, Severin Bernhart, Oliver Amft
Title Diet eyeglasses: Recognising food chewing using EMG and smart eyeglasses
Abstract We utilise smart eyeglasses for dietary monitoring, in particular to sense food chewing. Our approach is based on a 3D-printed regular eyeglasses design that could accommodate processing electronics and Electromyography (EMG) electrodes. Electrode positioning was analysed and an optimal electrode placement at the temples was identified. We further compared gel and dry fabric electrodes. For the subsequent analysis, fabric electrodes were attached to the eyeglasses frame. The eyeglasses were used in a data recording study with eight participants eating different foods. Two chewing cycle detection methods and two food classification algorithms were compared. Detection rates for individual chewing cycles reached a precision and recall of 80%. For five foods, classification accuracy for individual chewing cycles varied between 43% and 71%. Majority voting across intake sequences improved accuracy, ranging between 63% and 84%. We concluded that EMG-based chewing analysis using smart eyeglasses can contribute essential chewing structure information to dietary monitoring systems, while the eyeglasses remain inconspicuous and thus could be continuously used.
Date June 2016
Proceedings Title Proceedings of the International Conference on Wearable and Implantable Body Sensor Networks (BSN' 16)
Conference Name Body Sensor Networks Conference (BSN' 16)
Publisher IEEE
Pages 7--12
DOI 10.1109/BSN.2016.7516224
Extra bibtex: Zhang2016DietEyeglassesRecognisingFood
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Friedrich-Alexander-Universität Erlangen-Nürnberg