| 摘要(英) |
In past few decades, electronic products have become a part of the building blocks of modern society; lifestyles of humans are changing progressively along with the development of digitized everyday objects. New types of human-machine interaction technologies are constantly introduced by employing different kinds of sensor technologies. The evolvement of human-machine interaction technologies makes us to be able to communicate with machines in natural and simple manners. In recently years, input devices which are based on motion sensing technologies have become the main stream of the development of human-machine interaction area. Products based on body motion or hand gesture, such as Wii (Nintendo), iPhone (Apple Computer) and Kinect (Microsoft), have achieved great success on the consumer market. By utilizing various kinds of sensing technologies in everyday objects, a new, convenient, and creative lifestyle is achievable in the near future. In this thesis, we use accelerometers to determine the variation of the acceleration of hand gestures in three-dimensional space. We use Newton’’s second law of motion to process the velocity and displacement data of a gesture and projected it to a reduced dimensional space to get the feature vectors of a gesture. And, we use normalize and interpolated feature vectors as input of a PNN to recognize the input gesture. The recognition framework we used in this thesis needs only few samples and time for learning; and with high toleration of data deviation, the identification rate of this recognition framework is high. For practical use and small size purpose, we use an embedded system platform based on ARM 32bit Cortex-M3 to implement the recognition framework. Experimental result shows that the method we proposed is simple, effective, capable of doing the real-time process of the gesture recognition, and suitable for the development of intuitional intelligent human-machine interaction systems.
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