This repository is a collection of CSI data generated using commercial-off-the-shelf hardware, such as the Intel IWL5300 and supported Atheros and Broadcom (Nexmon) chipsets.

• External

  • Activity Recognition
  • Communication
  • Fall Detection
  • Gesture Recognition
  • Human Identification
  • Presence Detection

• Internal

  • Atheros
    • Example
  • Broadcom
    • Example
  • Intel
    • Activity
    • Example
    • Heart Rate
    • Respiration

[1] Baha’A, A., Almazari, M.M., Alazrai, R. and Daoud, M.I., 2020. A dataset for Wi-Fi-based human activity recognition in line-of-sight and non-line-of-sight indoor environments. Data in Brief, 33, p.106534.\

[2] Fang, S., Islam, T., Munir, S. and Nirjon, S., 2020, May. EyeFi: Fast Human Identification Through Vision and WiFi-based Trajectory Matching. In 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS) (pp. 59-68). IEEE.\

[3] Guo, X., He, Y., Zheng, X., Yu, L. and Gnawali, O., 2020. Zigfi: Harnessing channel state information for cross-technology communication. IEEE/ACM Transactions on Networking, 28(1), pp.301-311.\

[4] Klein Brinke, Jeroen (2019): Channel state information (WiFi traces) for 6 activities. 4TU.ResearchData. Dataset. https://doi.org/10.4121/uuid:42bffa4c-113c-46eb-84a1-c87b6a31a99f\

[5] L. Guo et al., "A novel benchmark on human activity recognition using WiFi signals," 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), Dalian, 2017, pp. 1-6, doi: 10.1109/HealthCom.2017.8210783\

[6] Palipana, Sameera & Rojas, David & Agrawal, Piyush & Pesch, Dirk. (2018). FallDeFi: Ubiquitous Fall Detection using Commodity Wi-Fi Devices. PACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT). 1. 10.1145/3161183.\

[7] S. Yousefi, H. Narui, S. Dayal, S. Ermon and S. Valaee, "A Survey on Behavior Recognition Using WiFi Channel State Information," in IEEE Communications Magazine, vol. 55, no. 10, pp. 98-104, Oct. 2017, doi: 10.1109/MCOM.2017.1700082.\

[8] Wang, Z., Gu, Z., Yin, J., Chen, Z. and Xu, Y., 2018, October. Syncope detection in toilet environments using Wi-Fi channel state information. In Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers (pp. 287-290).\

[9] Yongsen Ma, Gang Zhou, Shuangquan Wang, Hongyang Zhao, and Woosub Jung. 2018. SignFi: Sign Language Recognition Using WiFi. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 2, 1, Article 23 (March 2018), 21 pages. DOI: https://doi.org/10.1145/3191755

[10] Anonymous Author. (2020). A Wi-Fi Channel State Information (CSI) and Received Signal Strength (RSS) data-set for human presence and movement detection (Version 0.2.2) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3677366\