  Recent years have witnessed the rapid development in the research topic of
WiFi sensing that automatically senses human with commercial WiFi devices. This
work falls into two major categories, i.e., the activity recognition and the
indoor localization. The former work utilizes WiFi devices to recognize human
daily activities such as smoking, walking, and dancing. The latter one, indoor
localization, can be used for indoor navigation, location-based services, and
through-wall surveillance. The key rationale behind this type of work is that
people behaviors can influence the WiFi signal propagation and introduce
specific patterns into WiFi signals, called WiFi fingerprints, which can be
further explored to identify human activities and locations. In this paper, we
propose a novel deep learning framework for joint activity recognition and
indoor localization task using WiFi Channel State Information~(CSI)
fingerprints. More precisely, we develop a system running standard IEEE 802.11n
WiFi protocol, and collect more than 1400 CSI fingerprints on 6 activities at
16 indoor locations. Then we propose a dual-task convolutional neural network
with 1-dimensional convolutional layers for the joint task of activity
recognition and indoor localization. Experimental results and ablation study
show that our approach achieves good performances in this joint WiFi sensing
task. Data and code have been made publicly available at
https://github.com/geekfeiw/apl.
