  WiFi-based sensing has aroused immense attention over recent years. The
rationale is that the signal fluctuations caused by humans carry the
information of human behavior which can be extracted from the channel state
information of WiFi. Still, the prior studies mainly focus on single-task
sensing (STS), e.g., gesture recognition, indoor localization, user
identification. Since the fluctuations caused by gestures are highly coupling
with body features and the user's location, we propose a WiFi-based multi-task
sensing model (Wimuse) to perform gesture recognition, indoor localization, and
user identification tasks simultaneously. However, these tasks have different
difficulty levels (i.e., imbalance issue) and need task-specific information
(i.e., discrepancy issue). To address these issues, the knowledge distillation
technique and task-specific residual adaptor are adopted in Wimuse. We first
train the STS model for each task. Then, for solving the imbalance issue, the
extracted common feature in Wimuse is encouraged to get close to the
counterpart features of the STS models. Further, for each task, a task-specific
residual adaptor is applied to extract the task-specific compensation feature
which is fused with the common feature to address the discrepancy issue. We
conduct comprehensive experiments on three public datasets and evaluation
suggests that Wimuse achieves state-of-the-art performance with the average
accuracy of 85.20%, 98.39%, and 98.725% on the joint task of gesture
recognition, indoor localization, and user identification, respectively.
