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.


Download data is not yet available.

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.


Zijian Dong, Delong Zhu and Max Q. H Meng, “An Autonomous Elevator Button Recognition System Based on Convolutional Neural Networks,” in Proc. IEEE Int. Conference on Robotics and Biometrics, pp. 2533–2538, Dec. 2017.

Heon-Hui Kim, Dae-Jin Kim and Kwang-Hyun Park, “Robust Elevator Button Recognition in the Presence of Partial Occlusion and Clutter by Specular Reflections,” IEEE Trans. Ind. Electron., vol. 59, no. 3, pp. 1597–1611, Mar. 2012.

E. Sato, T. Yamaguchi, and F. Harashima, “Natural interface using pointing behavior for human–robot gestural interaction,” IEEE Trans. Ind. Electron., vol. 54, no. 2, pp. 1105–1112, Apr. 2007.

S. Zhou et al., “2D human gesture tracking and recognition by the fusion of MEMS inertial and vision sensors,” IEEE Sensors Journal, vol. 14, no. 4, pp. 1160–1170, Apr. 2014.

R. Xu, S. Zhou, and W. J. Li, “MEMS accelerometer based nonspecific user hand gesture recognition,” IEEE Sensors Journal, vol. 12, no. 5, pp. 1166–1173, May 2012.

R. Xie, X. Sun, X, Xia, and J. Cao, “Matching-Based Extensible Hand Gesture Recognition,” IEEE Sensors J., vol. 15, no. 6, pp. 3475–3483, June 2015

T. Lu, “A motion control method of intelligent wheelchair based on hand gesture recognition,” in Proc. 8th IEEE Conf. Ind. Electron. Appl. (ICIEA), pp. 957–962. Jun. 2013,

B. Zeng, G. Wang, and X. Lin, “A hand gesture based interactive presentation system utilizing heterogeneous cameras,” Tsinghua Sci. Technol., vol. 17, no. 3, pp. 329–336, Jun. 2012.

S. Lian, W. Hu, and K. Wang, “Automatic user state recognition for hand gesture based low-cost television control system,” IEEE Trans. Consum. Electron., vol. 60, no. 1, pp. 107–115, Feb. 2014.

C. Zhu and W. Sheng, “Wearable sensor-based hand gesture and daily activity recognition for robot-assisted living,” IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 41, no. 3, pp. 569–573, May 2011.

S. Berman and H. Stern, “Sensors for gesture recognition systems,” IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 42, no. 3, pp. 277–290, May 2012.

J. Alon, V. Athitsos, Q. Yuan, and S. Sclaroff, “A unified framework for gesture recognition and spatiotemporal gesture segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 9, pp. 1685–1699, Sep. 2009.

J. K. Oh et al., “Inertial sensor based recognition of 3-D character gestures with an ensemble classifiers,” in Proc. 9th Int. Workshop Frontiers Handwriting Recognit. (IWFHR), pp. 112–117, Oct. 2004.

S. Zhou, Z. Dong, W. J. Li, and C. P. Kwong, “Hand-written character recognition using MEMS motion sensing technology,” in Proc. IEEE/ASME Int. Conf. Adv. Intell. Mechatron., pp. 1418–1423, Jul. 2008.

A. Akl, C. Feng, and S. Valaee, “A novel accelerometer-based gesture recognition system,” IEEE Trans. Signal Process., vol. 59, no. 12, pp. 6197–6205, Dec. 2011.

C. C. Yang, Y. L. Hsu, K. S. Shih, and J. M. Lu, “Real-Time Gait Cycle Parameter Recognition Using a Wearable Accelerometry System,” Journal of Sensors, pp. 7314-7326, Nov. 2011.

T. Schlomer, B. Poppinga, N. Henze and S. Boll, “Gesture recognition with a Wii controller,” in Proc. 2th Tangible and Embedded Interaction (TEI’08), 2008, pp. 11–14.

G. Costante, L. Porzi, O. Lanz, P. Valigi and E. Ricci, “Personalizing a smartwatch-based gesture interface with transfer learning,” in Proc. Signal Processing Conference (EUSIPCO), 2014, pp. 2530–2534.

S. Shin and W. Sung, “Dynamic Hand Gesture Recognition for Wearable Devices with Low Complexity Recurrent Neural Networks,” in Proc. IEEE International symposium on circuits and systems (ISCAS 2016), 2016, pp. 2274–2277.

G. Devineau, F. Moutarde, W. Xi and J. Yang, “Deep Learning for Hand Gesture Recognition on Skeletal Data,” in Proc. IEEE International Conferce on Automatic Face & Gesture Recognition 2018, pp. 106–113.

Genuino 101.

Haiming Huang, Junhao Lin, Linyuan Wu, Bin Fang, Zhenkun Wen, and Fuchun Sunj, “Machine Learning-Based Multi-Modal Information Perception for Soft Robotic Hands,” Tsinghua Science and Technology, pp. 255–269, April, 2020.

Gregory de Boer, Nicholas Raske, Hongbo Wang, Junwai Kow, Ali Alazmani, Mazdak Ghajari, Peter Culmer and Robert Hewson, “Design Optimisation of a Magnetic Field Based Soft Tactile Sensor,” IEEE Sensors, no. 9, vol. 2, pp. 1–20, 2017.

Young Hyun Yoon, Dong Hyun Hwang, Jun Hyeok Yang and Seung Eun Lee, “Intellino: Processor for Embedded Artificial Intelligence,” electronics, no. 9, vol. 1169, pp. 1–12, 2020.

Kang, H.J., “Short floating-point representation for convolutional neural network inference,” IEICE Electron. Express, vol. 16, pp. 1–11, 2019.

Kota A., Kodai U., Yuka O., Kazutoshi H., Ryota U., Takumi K., Masayuki I., Tetsuya A., and Shinya T.Y., “Hardware/Algorithm Co-Design for Accurate Quantized Neural Networks,” IEICE Trans. Inf. Syst. E102.D, pp. 2341–2353, 2019.

Gou, Jianping Du, Lan Zhang, Yuhong and Xiong, Taisong, “A New Distance-weighted k -nearest Neighbor Classifier,”. J. Inf. Comput. Sci., 9, 2011.




How to Cite

Lee, S.-Y. ., Cho, I.-P. ., & Hong, C.-P. . (2022). Contactless Elevator Button Control System Based on Weighted K-NN Algorithm for AI Edge Computing Environment. Journal of Web Engineering, 21(02), 443–458.



SPECIAL ISSUE ON Future Multimedia Contents and Technology on Web in the 5G Era