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http://dx.doi.org/10.14372/IEMEK.2021.16.5.187

Design of Convolution Neural Network (CNN) Based Medicine Classifier for Nursing Robots  

Kim, Hyun-Don (Korea Polytechnics)
Kim, Dong Hyeon (Korea Polytechnics)
Seo, Pil Won (Korea Polytechnics)
Bae, Jongseok (Korea Polytechnics)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.16, no.5, 2021 , pp. 187-193 More about this Journal
Abstract
Our final goal is to implement nursing robots that can recognize patient's faces and their medicine on prescription. They can help patients to take medicine on time and prevent its abuse for recovering their health soon. As the first step, we proposed a medicine classifier with a low computational network that is able to run on embedded PCs without GPU in order to be applied to universal nursing robots. We confirm that our proposed model called MedicineNet achieves an 99.99% accuracy performance for classifying 15 kinds of medicines and background images. Moreover, we realize that the calculation time of our MedicineNet is about 8 times faster than EfficientNet-B0 which is well known as ImageNet classification with the high performance and the best computational efficiency.
Keywords
Convolutional Neural Network; Medicine classification; EfficientNet; Nursing robot; Autonomous mobile robot; Collaborative robot;
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