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Development of a Wearable Electrical Impedance Tomographic Sensor for Gesture Recognition with Machine Learning.

08:00 EDT 4th October 2019 | BioPortfolio

Summary of "Development of a Wearable Electrical Impedance Tomographic Sensor for Gesture Recognition with Machine Learning."

A wearable electrical impedance tomographic (wEIT) sensor with 8 electrodes is developed to realize gesture recognition with machine learning algorithms. To optimize the wEIT sensor, gesture recognition rates are compared by using a series of electrodes with different materials and shapes. To improve the gesture recognition rates, several Machine Learning algorithms are used to recognize three different gestures with the obtained voltage data. To clarify the gesture recognition mechanism, an electrical model of the electrode-skin contact impedance is established. Experimental results show that: rectangular copper electrodes realize the highest recognition rate; the existence of the electrode-skin contact impedance could improve the gesture recognition rate; Medium Gaussian SVM (Support Vector Machine) algorithm is the optimal algorithm with an average recognition rate of 95.

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This article was published in the following journal.

Name: IEEE journal of biomedical and health informatics
ISSN: 2168-2208
Pages:

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