  In recent years, indoor human presence detection based on supervised learning
(SL) and channel state information (CSI) has attracted much attention. However,
existing studies that rely on spatial information of CSI are susceptible to
environmental changes which degrade prediction accuracy. Moreover, SL-based
methods require time-consuming data labeling for retraining models. Therefore,
it is imperative to design a continuously monitored model using a
semi-supervised learning (SSL) based scheme. In this paper, we conceive a
bifold teacher-student (BTS) learning approach for indoor human presence
detection in an adjoining two-room scenario. The proposed SSL-based primal-dual
teacher-student network intelligently learns spatial and temporal features from
labeled and unlabeled CSI datasets. Additionally, the enhanced penalized loss
function leverages entropy and distance measures to distinguish drifted data,
i.e., features of new datasets affected by time-varying effects and altered
from the original distribution. Experimental results demonstrate that the
proposed BTS system sustains asymptotic accuracy after retraining the model
with unlabeled data. Furthermore, BTS outperforms existing SSL-based models in
terms of the highest detection accuracy while achieving the asymptotic
performance of SL-based methods.
