Device free wireless localization and activity recognition with deep learning
Recent advance in device free wireless localization and activity recognition (DFLAR) technique has made it possible to acquire context information of the target without its participation. This novel technique has great potential for lots of applications, e.g. smart space, smart home, and security safeguard. One fundamental question of DFLAR is how to design discriminative features to characterize the raw wireless signal. Existing works manually design handcraft features, e.g. mean and variance of the raw signal, which is not universal for different activities. Inspired by the deep learning theory, we explore to learn universal and discriminative features automatically with a deep learning model. By merging the learned new features into a softmax regression based machine learning framework, we develop a deep learning based DFLAR system. Experimental evaluations with an 8 wireless nodes testbed confirms that compared with traditional handcraft features, DFLAR system with the learned features could achieve better performance.
2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops)