Wi-Multi: A Three-Phase System for Multiple Human Activity Recognition With Commercial WiFi Devices

Channel state information-based activity recognition has gathered immense attention over recent years. Many existing works achieved desirable performance in various applications, including healthcare, security, and Internet of Things, with different machine learning algorithms. However, they usually fail to consider the availability of enough samples to be trained. Besides, many applications only focus on the scenario where only single subject presents. To address these challenges, in this paper, we propose a three-phase system Wi-multi that targets at recognizing multiple human activities in a wireless environment. Different system phases are applied according to the size of available collected samples. Specifically, distance-based classification using dynamic time warping is applied when there are few samples in the profile. Then, support vector machine is employed when representative features can be extracted from training samples. Lastly, recurrent neural networks is exploited when a large number of samples are available. Extensive experiments results show that Wi-multi achieves an accuracy of 96.1% on average. It is also able to achieve a desirable tradeoff between accuracy and efficiency in different phases.