Evaluation and Improvement of Activity Detection Systems with Recurrent Neural Network

Channel State Information of WiFi signal has attracted tremendous interests in recent years for activity identification. Although existing work can achieve desirable performance using different algorithms, similar system modules are often shared. In this paper, we first summarize and compare various techniques employed in different modules such as preprocessing, activity extraction, feature dimension reduction, and classification. Specifically, different feature reduction methods are applied in order to address the challenge of classifying various length signals and extracting representative abstractions, including manually selecting features and Dynamic Time Warping based classification with Principal Component Analysis. By targeting at multiple human activities, we then compare the performance of two common system structures from difference aspects. Experimental results show that it can be subjective and environment dependent by manually selecting particular features, while DTW based classification can be time consuming especially with larger dataset. In order to address these challenges, we propose a novel framework based on Deep Learning Network. Long Short Term Memory model, a type of Recurrent Neutral Network, is employed for time-series sequence classification. Extensive results show that it can achieve higher efficiency and accuracy.