{"url":"/dataset/wallhack1-8k","name":"Wallhack1.8k","full_name":null,"description_markdown":"The Wallhack1.8k dataset comprises 1,806 CSI amplitude spectrograms (and raw WiFi packet time series) corresponding to three activity classes: \"no presence,\" \"walking,\" and \"walking + arm-waving.\" WiFi packets were transmitted at a frequency of 100 Hz, and each spectrogram captures a temporal context of approximately 4 seconds (400 WiFi packets).\r\n\r\nTo assess cross-scenario and cross-system generalization, WiFi packet sequences were collected in LoS and through-wall (NLoS) scenarios, utilizing two different WiFi systems (BQ: biquad antenna and PIFA: printed inverted-F antenna). The dataset is structured accordingly:\r\n\r\nLOS/BQ/ <- WiFi packets collected in the LoS scenario using the BQ system\r\nLOS/PIFA/ <- WiFi packets collected in the LoS scenario using the PIFA system\r\nNLOS/BQ/ <- WiFi packets collected in the NLoS scenario using the BQ system\r\nNLOS/PIFA/ <- WiFi packets collected in the NLoS scenario using the PIFA system\r\n\r\nThese directories contain the raw WiFi packet time series (see Table 1). Each row represents a single WiFi packet with the complex CSI vector H being stored in the \"data\" field and the class label being stored in the \"class\" field. H is of the form [I, R, I, R, ..., I, R], where two consecutive entries represent imaginary and real parts of complex numbers (the Channel Frequency Responses of subcarriers). Taking the absolute value of H (e.g., via numpy.abs(H)) yields the subcarrier amplitudes A.","description_withheld":null,"homepage":"https://zenodo.org/records/13950918","introduced_date":"2024-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/data-augmentation-techniques-for-cross-domain","title":"Data Augmentation Techniques for Cross-Domain WiFi CSI-based Human Activity Recognition","first_author":"Julian Strohmayer","url":null},"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Human Activity Recognition","url":"/task/human-activity-recognition","datasets_with_task":"/datasets/task/human-activity-recognition"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Wallhack1.8k"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}