Gesture-Based Human Machine Interaction Using RCNNs in Limited Computation Power Devices

The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of i...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-12, Vol.21 (24), p.8202
Main Authors: Tellaeche Iglesias, Alberto, Fidalgo Astorquia, Ignacio, Vázquez Gómez, Juan Ignacio, Saikia, Surajit
Format: Article
Language:English
Subjects:
Algorithms
Color imagery
Communication
Computer vision
Deep learning
embedded systems
gesture detection
Gesture recognition
Gestures
Humans
Image processing
Image Processing, Computer-Assisted
Internet of Things
Methods
Neural networks
Neural Networks, Computer
Pattern recognition
real time
Recognition, Psychology
Sensors
Sign language
Voice recognition
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Summary:The use of gestures is one of the main forms of human machine interaction (HMI) in many fields, from advanced robotics industrial setups, to multimedia devices at home. Almost every gesture detection system uses computer vision as the fundamental technology, with the already well-known problems of image processing: changes in lighting conditions, partial occlusions, variations in color, among others. To solve all these potential issues, deep learning techniques have been proven to be very effective. This research proposes a hand gesture recognition system based on convolutional neural networks and color images that is robust against environmental variations, has a real time performance in embedded systems, and solves the principal problems presented in the previous paragraph. A new CNN network has been specifically designed with a small architecture in terms of number of layers and total number of neurons to be used in computationally limited devices. The obtained results achieve a percentage of success of 96.92% on average, a better score than those obtained by previous algorithms discussed in the state of the art.
ISSN:1424-8220
1424-8220
DOI:10.3390/s21248202