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

Abstract

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.

Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method

“…Its performance relies on interactive sensing between the user community and the product [ 18 ]. Using the principles of reflective projection and stereoscopic imaging, smart technology is incorporated into the application scenario of public lifts [ 19 ]. This type of sensor less touch interaction is different from the physical buttons of conventional devices, and is able to completely avoid the risk of cross-infection from physical contact with bacteria and viruses [ 20 ].…”