An extensive set of research efforts have explored Channel State Information for human activity detection. By extracting CSI from a sequence of packets, one can statistically analyze the temporal variations embedded therein and recognize corresponding human activities. In this paper, we present Wi-Chase, a sensorless system based on CSI from ubiquitous WiFi packets for human activity detection. Different from existing schemes utilizing only CSI of one or a small subset of subcarriers, Wi-Chase fully utilizes all available subcarriers of the WiFi signal and incorporates variations in both their phases and magnitudes. As each subcarrier carries integral information that will improve the recognition accuracy because of detailed correlated information content in different subcarriers, we can achieve much higher detection accuracy. To the best of our knowledge, this is the first system that gathers information from all the subcarriers to identify and classify multiple activities. Our experimental results show that Wi-Chase is robust and achieves an average classification accuracy greater than 97% for multiple communication links.