Wi-Fi Sensing Recommended Reading List

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  1. Guolin Yin, Junqing Zhang*, Guanxiong Shen, and Yingying Chen, “FewSense, Towards a Scalable and Cross-Domain Wi-Fi Sensing System Using Few-Shot Learning”, IEEE Transactions on Mobile Computing, accepted arXiv link,[IEEE], [code]

    This paper applies few-shot learning to Wi-Fi sensing. The source code is available online.

  2. Zhang, Jie, et al. “CrossSense: Towards cross-site and large-scale Wi-Fi sensing.” Proceedings of the 24th annual international conference on mobile computing and networking. 2018. [ACM]

    CrossSense: Introduces a system enhancing Wi-Fi sensing in new environments by using machine learning for synthetic training sample generation and a mixture-of-experts approach. It significantly boosts accuracy in applications like gait and gesture recognition​​.

  3. Sameera Palipana, David Rojas, Piyush Agrawal, and Dirk Pesch. 2018. FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 155 (December 2017), 25 pages.[ACM][Code]

    FallDeFi: Focuses on fall detection among the elderly using Wi-Fi CSI. It employs time-frequency analysis and feature selection for high-accuracy fall detection, showing substantial improvements over previous methods, especially in changing environments​​.

  4. Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using Wi-Fi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018).[Code][ACM]

    SignFi: Proposes using Wi-Fi CSI and a CNN for sign language gesture recognition. Unlike previous methods, SignFi can recognize a broad range of gestures with high accuracy in different settings, representing a significant advancement in Wi-Fi-based gesture recognition​​.

  5. W. Wang, A. X. Liu, M. Shahzad, K. Ling and S. Lu, “Device-Free Human Activity Recognition Using Commercial Wi-Fi Devices,” in IEEE Journal on Selected Areas in Communications, vol. 35, no. 5, pp. 1118-1131, May 2017, doi: 10.1109/JSAC.2017.2679658.[IEEE]

    CARM: Develops a CSI-based human activity recognition and monitoring system. It introduces a CSI-speed and CSI-activity model to quantitatively correlate CSI dynamics with human activities, achieving high accuracy in various environments​​.

  6. Hong Li, Wei Yang, Jianxin Wang, Yang Xu, and Liusheng Huang. 2016. WiFinger: talk to your smart devices with finger-grained gesture. In Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ‘16). [ACM]

    WiFinger: Presents a system for number text input in Wi-Fi devices using finger-grained gestures. It captures unique CSI patterns generated by finger movements, achieving high classification accuracy for continuous text input without requiring wearable sensors​​.

  7. Y. Zhang et al., “Widar3.0: Zero-Effort Cross-Domain Gesture Recognition With Wi-Fi,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 11, pp. 8671-8688, 1 Nov. 2022, doi: 10.1109/TPAMI.2021.3105387. [IEEE]. [Code and Dataset]

    Widar3.0: Offers a zero-effort cross-domain gesture recognition system using Wi-Fi, focusing on domain-independent features at a lower signal level. It requires only one-time training and adapts to different data domains, significantly outperforming existing solutions in gesture recognition accuracy across various environments​​.

  8. F. Meneghello, D. Garlisi, N. D. Fabbro, I. Tinnirello and M. Rossi, “SHARP: Environment and Person Independent Activity Recognition With Commodity IEEE 802.11 Access Points,” in IEEE Transactions on Mobile Computing, vol. 22, no. 10, pp. 6160-6175, 1 Oct. 2023, doi: 10.1109/TMC.2022.3185681.[IEEE].[SHARP]

    SHARP: This study presents SHARP, a technique for human activity recognition (HAR) using commercial Wi-Fi devices. SHARP processes the Wi-Fi channel’s frequency response phase to estimate Doppler shifts, identifying movements with high accuracy in varying conditions, environments, and individuals​​.

  9. Yongsen Ma, Gang Zhou, and Shuangquan Wang. 2019. Wi-Fi Sensing with Channel State Information: A Survey. ACM Comput. Surv. 52, 3, Article 46 (May 2020). [ACM]

    Wi-Fi Sensing with CSI: This paper reviews Wi-Fi sensing technologies using Channel State Information (CSI), categorizing applications into detection, recognition, and estimation. Key challenges include robustness, privacy, and coexistence with networking. Future trends involve cross-layer, cross-device, and cross-sensor Wi-Fi sensing​​.

  10. S. Yousefi, H. Narui, S. Dayal, S. Ermon and S. Valaee, “A Survey on Behavior Recognition Using Wi-Fi Channel State Information,” in IEEE Communications Magazine, vol. 55, no. 10, pp. 98-104, Oct. 2017.[IEEE]

    Behavior Recognition using Wi-Fi CSI: This survey discusses human behavior recognition using Wi-Fi CSI. Techniques include histogram-based methods and deep learning approaches, particularly LSTM for feature extraction and activity recognition. Challenges include using CSI phase information, robustness in dynamic environments, and multi-user behavior identification​​.

  11. F. Meneghello, C. Chen, C. Cordeiro and F. Restuccia, “Toward Integrated Sensing and Communications in IEEE 802.11bf Wi-Fi Networks,” in IEEE Communications Magazine, vol. 61, no. 7, pp. 128-133, July 2023 [IEEE]

    Integrated Sensing and Communications in IE: This paper explores the integration of sensing into Wi-Fi networks, focusing on the IEEE 802.11bf Task Group’s efforts. It discusses the impact of communication parameters on sensing performance and outlines main research challenges in the field​​.

  12. Yang, Zheng, Yi Zhang, Guoxuan Chi, and Guidong Zhang. “Hands-on wireless sensing with wi-fi: A tutorial.” arXiv preprint arXiv:2206.09532 (2022).[ARXIV]

    Wireless Sensing with Wi-Fi: This tutorial introduces wireless sensing using Wi-Fi, covering theoretical principles and practical code implementation. It highlights deep learning models for wireless sensing applications in various fields, including health care, smart homes, and security surveillance​​.