{"url":"/dataset/har","name":"HAR","full_name":"Human Activity Recognition Using Smartphones","description_markdown":"The Human Activity Recognition Dataset has been collected from 30 subjects performing six different activities (Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). It consists of inertial sensor data that was collected using a smartphone carried by the subjects.\r\n\r\nSource: [http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones](http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones)\r\nImage Source: [https://www.youtube.com/watch?v=XOEN9W05_4A](https://www.youtube.com/watch?v=XOEN9W05_4A)","description_withheld":null,"homepage":"http://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Public Domain Dataset for Human Activity Recognition using Smartphones","first_author":null,"url":"http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-84.pdf"},"license":{"name":"Public domain","url":null},"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Human Activity Recognition","url":"/task/human-activity-recognition","datasets_with_task":"/datasets/task/human-activity-recognition"},{"name":"Recognizing And Localizing Human Actions","url":"/task/recognizing-and-localizing-human-actions","datasets_with_task":"/datasets/task/recognizing-and-localizing-human-actions"}],"languages":[],"variants":["HAR"],"data_loaders":[],"num_papers_in_archive":307,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-clustering-on-har","task":"Image Clustering","dataset_variant":"HAR","rows":3,"metrics":["Accuracy","NMI"],"first_row_in_archive_order":{"model":"FCMI","paper":"/paper/deep-fair-clustering-via-maximizing-and","metrics":{"Accuracy":"0.882","NMI":"0.807"},"code_links":[{"title":"PengxinZeng/2023-CVPR-FCMI","url":"https://github.com/PengxinZeng/2023-CVPR-FCMI"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/human-activity-recognition-on-har","task":"Human Activity Recognition","dataset_variant":"HAR","rows":2,"metrics":["Accuracy","F1 Macro","Macro-F1"],"first_row_in_archive_order":{"model":"LMSS","paper":"/paper/leveraging-lda-feature-extraction-to-augment","metrics":{"Accuracy":"0.9952","F1 Macro":"0.9954"},"code_links":[{"title":"miladvazan/LDA-MLP-SVM-SGD","url":"https://github.com/miladvazan/LDA-MLP-SVM-SGD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/recognizing-and-localizing-human-actions-on","task":"Recognizing And Localizing Human Actions","dataset_variant":"HAR","rows":1,"metrics":["1:1 Accuracy"],"first_row_in_archive_order":{"model":"TS-TCC","paper":"/paper/time-series-representation-learning-via","metrics":{"1:1 Accuracy":"90.37"},"code_links":[{"title":"emadeldeen24/TS-TCC","url":"https://github.com/emadeldeen24/TS-TCC"},{"title":"etna-team/etna","url":"https://github.com/etna-team/etna"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/leveraging-lda-feature-extraction-to-augment","title":"Leveraging LDA Feature Extraction to Augment Human Activity Recognition Accuracy","date":"2024-06-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/virtual-fusion-with-contrastive-learning-for","title":"Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition","date":"2023-12-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-fair-clustering-via-maximizing-and","title":"Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric","date":"2022-09-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficient-deep-clustering-of-human-activities","title":"Efficient Deep Clustering of Human Activities and How to Improve Evaluation","date":"2022-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/time-series-representation-learning-via","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","date":"2021-06-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/n2dnot-too-deep-clustering-via-clustering-the","title":"N2D: (Not Too) Deep Clustering via Clustering the Local Manifold of an Autoencoded Embedding","date":"2019-08-16","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":14,"samples_ran":4,"samples_unverified":10,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":1,"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."}