# BTS: Bifold Teacher-Student in Semi-Supervised Learning for Indoor Two-Room Presence Detection Under Time-Varying CSI

arXiv: https://arxiv.org/abs/2212.10802 · HTML: https://mlsanity.com/paper/2212.10802/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 21 Dec 2022.

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Meta Pseudo Labels](https://mlsanity.com/paper/2003.10580/) (2020)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [CSI-based Fingerprinting for Indoor Localization: A Deep Learning Approach](https://mlsanity.com/paper/1603.07080/) (2016)
- [Five Facets of 6G: Research Challenges and Opportunities](https://mlsanity.com/paper/2212.07902/) (2022)
- [AF-DCGAN: Amplitude Feature Deep Convolutional GAN for Fingerprint Construction in Indoor Localization Systems](https://mlsanity.com/paper/1804.05347/) (2018)
- [Harvesting Ambient RF for Presence Detection Through Deep Learning](https://mlsanity.com/paper/2002.05770/) (2020)
- [CRONOS: Colorization and Contrastive Learning for Device-Free NLoS Human Presence Detection using Wi-Fi CSI](https://mlsanity.com/paper/2211.10354/) (2022)

## Built on by (most cited first, 2 of 2)

- [Context-Aware Predictive Coding: A Representation Learning Framework for WiFi Sensing](https://mlsanity.com/paper/2410.01825/) (2024)
- [SALC: Skeleton-Assisted Learning-Based Clustering for Time-Varying Indoor Localization](https://mlsanity.com/paper/2307.07650/) (2023)

## Abstract (arXiv)

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 accomplishes an averaged accuracy of around 98% after retraining the model with unlabeled data. BTS can sustain an accuracy of 93% under the changed layout and environments. Furthermore, BTS outperforms existing SSL-based models in terms of the highest detection accuracy of around 98% while achieving the asymptotic performance of SL-based methods.