An ANN-based gesture recognition algorithm for smart-home applications

• Phat Nguyen Huu,
• Quang Tran Minh,
• Hoang Lai The,

Vol. 14, No. 5, May 31, 2020
10.3837/tiis.2020.05.006, Download Paper (Free):

• 3-Dimensional Convolutional Network
• Human-Computer Interaction
• Smart-home
• Machine Learning
• IoT Applications.

Abstract

The goal of this paper is to analyze and build an algorithm to recognize hand gestures applying to smart home applications. The proposed algorithm uses image processing techniques combing with artificial neural network (ANN) approaches to help users interact with computers by common gestures. We use five types of gestures, namely those for Stop, Forward, Backward, Turn Left, and Turn Right. Users will control devices through a camera connected to computers. The algorithm will analyze gestures and take actions to perform appropriate action according to users requests via their gestures. The results show that the average accuracy of proposal algorithm is 92.6 percent for images and more than 91 percent for video, which both satisfy performance requirements for real-world application, specifically for smart home services. The processing time is approximately 0.098 second with 10 frames/sec datasets. However, accuracy rate still depends on the number of training images (video) and their resolution.

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