A Survey on Behavior Recognition Using WiFi Channel State Information

By Siamak Yousefi, Hirokazu Narui, Sankalp Dayal, Stefano Ermon, Shahrokh Valaee. IEEE Communications Magazine, 2017

558 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ab3a1407179e67f18f81497f915d1b010a552092

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jun Liu
2. Jinhong Yuan
3. Gang Zhou
4. Robert W. Heath
5. Rui Shu
6. Stefano Ermon
7. Yongsen Ma
8. Shuangquan Wang

In Electrical and Electronic Engineering

1. Jinhong Yuan
2. Jun Liu
3. Gang Zhou
4. Robert W. Heath
5. Y. Guo
6. Chen Chen
7. Stefano Ermon
8. Youfang Lin

In Signal Processing

1. Jun Liu
2. Jinhong Yuan
3. Qiuhong Ke
4. Bin Guo
5. Hung Hai Bui
6. Gang Zhou
7. Liu Ren
8. J. Andrew Zhang

Citations in the corpus

• Human Action Recognition From Various Data Modalities: A Review (2020, background): “Single modality-based HAR has been extensively investigated in the past decades [6], [17], [18], [19], [20], [21], [22].”
• Multibeam for Joint Communication and Radar Sensing Using Steerable Analog Antenna Arrays (2018, background): “Rooting from traditional radar technology, radio sensing is evolving with significantly expanded scope and applications [3].”
• Deep Learning for Sensor-based Human Activity Recognition (2020, background): “A more advanced WiFi signal metric of channel state information (CSI) has been recently widely explored for activity recognition from both amplitude and phase aspects [171].”
• Improve Unsupervised Domain Adaptation with Mixup Training (2020, methodology): “In building HAR system for fitness monitoring and assisted living [17, 18] We conduct experiments on the same WiFi activity recognition dataset [12] used in [6].”
• Cross-Domain WiFi Sensing with Channel State Information: A Survey (2022, background): “Yousei-2017 [108] ma-2020: CU: average accuracy 93%; FallDeFi [53]: CU: 83%; S.”
• WiFi CSI Based Passive Human Activity Recognition Using Attention Based BLSTM (2019, methodology): “In [14], the RF achieved a superior performance than SVM, logistic regression (LR) and decision tree (DT) for CSI based human activity recognition.”
• A DIRT-T Approach to Unsupervised Domain Adaptation (2018, methodology): “We report results for domain adaptation in digits classification (MNIST-M, MNIST, SYN DIGITS, SVHN), traffic sign classification (SYN SIGNS, GTSRB), general object classification (STL-10, CIFAR-10), and Wi-Fi activity recognition (Yousefi et al., 2017).”
• WiFi Sensing with Channel State Information (2019, background): “Each of the four papers [106, 114, 129, 156] gives a survey on CSI-based human behavior recognition with their specific emphasis: basics and applications [106], deep learning techniques [129], data-driven and model-based approaches [156], and pattern-based and model-based approaches [114].”