Researchers from Nanyang University of Technology and Sydney University of Technology have developed a system for machine
They noticed that when people decide practicaltasks, they usually combine visual and somatosensory information obtained from the environment. These two types of information complement each other, since in combination they give a better idea of all the data that is needed to solve the problem.
“Our idea came after westudied the properties of the brain to process information. It turned out that in the human brain, perceptual activity depends not only on specific sensory information, but also on the complex integration of several sensors. This inspired us to combine visual and somatosensory information (touch, temperature) to implement a system that accurately recognizes gestures.”
lead author of the study Xiaodong Chen
Developing your own gesture recognition technique,Chen and his colleagues provided the opportunity to integrate various types of sensory information collected using several sensors. Supplementing them with computer vision, they created a system that can recognize gestures with an accuracy of 85%.
Its multi-layered and hierarchical structuremimics the structure of the brain, but with artificial neural networks instead of regular ones. In addition, some of the networks in their architecture process the same modal sensory data processed by neural networks in the brain. For example, a sectional neural network (CNN) specifically performs conversion operations by artificially reproducing the function of the local receptor field within biological nervous systems. In this way, it imitates the initial processing of visual information that occurs in those parts of the human brain that are responsible for vision.
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