Design And Implementation Of Acoustic Based Gesture Recognition System

The application of human sensing and gesture recognition in smart home has an urgent need and commercial value.In recent years,human-computer interaction with reasonable design and low additional hardware cost is a research priority.The existing work mainly bases on normal cameras,depth cameras and wearable sensors.The camera-based methods have been widely employed by technology companies.But the disadvantage of the camera-based methods is that it cannot be used in private occasion and is easily blocked by environmental objects.Meanwhile,camera-based methods are provided with higher energy consumption.The wearable devices-based methods have higher accuracy and low energy consumption,but consumers must purchase additional hardware.In this work,we investigated the principles of acoustic signal based gesture recognition system.We proposed a device-free gesture recognition system that can accurately sense the hand in-air movements using channel impulse response(CIR)and domain adaptation network.The CIR-based methods increase the measurement accuracy compared with the pioneer acoustic-based human gesture recognition system.To solve the problem that a gesture recognition model trained on a specific object does not work well when being applied to predict another object's gestures,we proposed a domain adaptation network which can remove the environment information and object-specific features from the training data and increase the recognition accuracy.The main content of this thesis includes the following aspects: Firstly,we analyzed the features which are demodulated from different transmitted audio signals and designed different gesture recognition systems.We proposed a Channel Impulse Response based method which increases the accuracy for gesture detection.Secondly,we proposed a domain adaptation deep learning network which can increase the recognition accuracy to a higher level than the performance of traditional convolutional neural networks(CNN)based model.Finally,we implemented an acoustic based gesture recognition system.After that we simulated the algorithms and collected the real-word data to test our system.The feasibility of the gesture recognition system and the novel learning model were verified.The average recognition accuracy reached 98%.