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Indoor human activity recognition remains a hot topic and receives tremendous research efforts during the last few decades. However, previous solutions either rely on special hardware, or demand the cooperation of subjects. Therefore, the scalability issue remains a great challenge. To this end, we present an online activity recognition system, which explores WiFi ambient signals for received signal strength indicator (RSSI) fingerprint of different activities. It can be integrated into any existing WLAN networks without additional hardware support. Also, it does not need the subjects to be cooperative during the recognition process. More specifically, we first conduct an empirical study to gain in-depth understanding of WiFi characteristics, e.g., the impact of activities on the WiFi RSSI. Then, we present an online activity recognition architecture that is flexible and can adapt to different settings/conditions/scenarios. Lastly …