Contactless Elevator Button Control System Based on Weighted K-NN Algorithm for AI Edge Computing Environment




Machine Learning, Magnetic Sensor, Weighted K-NN, K-NN, RBF, Contactless, Elevator control, Low-Power, Edge Computing


In recent years, attempts have been made to create a door-opening or elevator button that operates based on gestures when entering and exiting a building. This can consider the convenience of an individual carrying luggage, and in some cases, has the advantage of preventing the spread of disease between people through contact. In this study, we propose a method for operating elevator buttons without contact. Elevators cannot utilize high-performance processors owing to production costs. Therefore, this paper introduces a prototype of a low-performance processor-based system that can be used in elevators, and then introduces a weighted K-nearest neighbors (K-NN) based user gesture learning and number matching method for application in an optimal non-contact button control method that can be used in such an environment. As a result, through the proposed method, a performance gain of 7.5% in comparison to a conventional K-NN method and a performance improvement of 9.7% compared to a radial basis function were achieved in a relatively low-performance processor-based system.


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Author Biographies

Sang-Yub Lee, Korea Electronic Technology Institute, Republic of Korea

Sang-Yub Lee received BS and MS degrees in electronic and electrical engineering from the Yonsei University, South Korea, in 2005. He received a PhD degree in computer and information science from Korea University, South Korea, in 2019. He is a principal researcher at the Korea Electronics Technology Institute. He was with Samsung Electro-Mechanics as a senior engineer at the Central R&D center. His research is focused on energy IT and digital twin platforms.

In-Pyo Cho, Korea Electronic Technology Institute, Republic of Korea

In-Pyo Cho received BS and MS degrees in computer science from Yonsei University, Seoul, South Korea, in 2012. He is a senior researcher at the Korea Electronics Technology Institute. He was with LG Electronics as a senior engineer at the Central R&D Center. His research focuses on energy IT and digital twin platforms.

Chung-Pyo Hong, Division of Computer Engineering, Hoseo University, Republic of Korea

Chung-Pyo Hong received BS and MS degrees in computer science from Yonsei University, Seoul, Korea, in 2004 and 2006, respectively. In 2012, he received a PhD in computer science from Yonsei University, Seoul, Korea. He is currently an associate professor of Computer Engineering at Hoseo University, Asan, Korea. His research interests include machine learning, explainable AI, and data science.


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