Open AccessContactless Elevator Button Control System Based on Weighted K-NN Algorithm for AI Edge Computing Environment
Author(s) -
Sang-Yub Lee,
In-Pyo Cho,
Chung-Pyo Hong
Publication year - 2022
Publication title -
journal of web engineering/journal of web engineering on line
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 13
eISSN - 1544-5976
pISSN - 1540-9589
DOI - 10.13052/jwe1540-9589.21214
Subject(s) - elevator , computer science , enhanced data rates for gsm evolution , radial basis function , matching (statistics) , function (biology) , gesture , gesture recognition , algorithm , artificial intelligence , engineering , mathematics , artificial neural network , statistics , structural engineering , evolutionary biology , biology
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.