{"url":"/dataset/jigsaws","name":"JIGSAWS","full_name":"JHU-ISI Gesture and Skill Assessment Working Set","description_markdown":"The **JHU-ISI Gesture and Skill Assessment Working Set** (**JIGSAWS**) is a surgical activity dataset for human motion modeling. The data was collected through a collaboration between The Johns Hopkins University (JHU) and Intuitive Surgical, Inc. (Sunnyvale, CA. ISI) within an IRB-approved study. The release of this dataset has been approved by the Johns Hopkins University IRB.   The dataset was captured using the da Vinci Surgical System from eight surgeons with different levels of skill performing five repetitions of three elementary surgical tasks on a bench-top model: suturing, knot-tying and needle-passing, which are standard components of most surgical skills training curricula. The JIGSAWS dataset consists of three components:\r\n\r\n* kinematic data: Cartesian positions, orientations, velocities, angular velocities and gripper angle describing the motion of the manipulators.\r\n* video data: stereo video captured from the endoscopic camera. Sample videos of the JIGSAWS tasks can be downloaded from the official webpage.\r\n* manual annotations including:\r\n* gesture (atomic surgical activity segment labels).\r\n* skill (global rating score using modified objective structured assessments of technical skills).\r\n* experimental setup: a standardized cross-validation experimental setup that can be used to evaluate automatic surgical gesture recognition and skill assessment methods.\r\n\r\nSource: [https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release](https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release)\r\nImage Source: [https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release](https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release)","description_withheld":null,"homepage":"https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Jhu-isi gesture and skill assessment working set (jigsaws): A surgical activity dataset for human motion modeling","first_author":null,"url":"https://cirl.lcsr.jhu.edu/wp-content/uploads/2015/11/JIGSAWS.pdf"},"license":{"name":"Custom","url":"https://cirl.lcsr.jhu.edu/research/hmm/datasets/jigsaws_release/"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Action Segmentation","url":"/task/action-segmentation","datasets_with_task":"/datasets/task/action-segmentation"},{"name":"Action Quality Assessment","url":"/task/action-quality-assessment","datasets_with_task":"/datasets/task/action-quality-assessment"},{"name":"Surgical Skills Evaluation","url":"/task/surgical-skills-evaluation","datasets_with_task":"/datasets/task/surgical-skills-evaluation"}],"languages":[],"variants":["JIGSAWS"],"data_loaders":[],"num_papers_in_archive":105,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/action-segmentation-on-jigsaws","task":"Action Segmentation","dataset_variant":"JIGSAWS","rows":7,"metrics":["Edit Distance","Accuracy","F1@10","F1@25","F1@50"],"first_row_in_archive_order":{"model":"MRG-Net","paper":"/paper/relational-graph-learning-on-visual-and","metrics":{"Accuracy":"87.9±4.2","Edit Distance":"89.3±5.2"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/action-quality-assessment-on-jigsaws","task":"Action Quality Assessment","dataset_variant":"JIGSAWS","rows":5,"metrics":["Spearman Correlation"],"first_row_in_archive_order":{"model":"RICA^2","paper":"/paper/rica-2-rubric-informed-calibrated-assessment","metrics":{"Spearman Correlation":"0.92"},"code_links":[{"title":"abrarmajeedi/rica2_aqa","url":"https://github.com/abrarmajeedi/rica2_aqa"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/surgical-skills-evaluation-on-jigsaws","task":"Surgical Skills Evaluation","dataset_variant":"JIGSAWS","rows":2,"metrics":["Accuracy","Edit Distance"],"first_row_in_archive_order":{"model":"CNN","paper":"/paper/evaluating-surgical-skills-from-kinematic","metrics":{"Accuracy":"0.98"},"code_links":[{"title":"hfawaz/miccai18","url":"https://github.com/hfawaz/miccai18"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rica-2-rubric-informed-calibrated-assessment","title":"RICA2: Rubric-Informed, Calibrated Assessment of Actions","date":"2024-08-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/auto-encoding-score-distribution-regression","title":"Auto-Encoding Score Distribution Regression for Action Quality Assessment","date":"2021-11-22","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/relational-graph-learning-on-visual-and","title":"Relational Graph Learning on Visual and Kinematics Embeddings for Accurate Gesture Recognition in Robotic Surgery","date":"2020-11-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/automatic-gesture-recognition-in-robot","title":"Automatic Gesture Recognition in Robot-assisted Surgery with Reinforcement Learning and Tree Search","date":"2020-02-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-reinforcement-learning-for-surgical","title":"Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification","date":"2018-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/evaluating-surgical-skills-from-kinematic","title":"Evaluating surgical skills from kinematic data using convolutional neural networks","date":"2018-06-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/end-to-end-fine-grained-action-segmentation","title":"End-to-End Fine-Grained Action Segmentation and Recognition Using Conditional Random Field Models and Discriminative Sparse Coding","date":"2018-01-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/tricornet-a-hybrid-temporal-convolutional-and","title":"TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation","date":"2017-05-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/temporal-convolutional-networks-a-unified","title":"Temporal Convolutional Networks: A Unified Approach to Action Segmentation","date":"2016-08-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/recognizing-surgical-activities-with","title":"Recognizing Surgical Activities with Recurrent Neural Networks","date":"2016-06-20","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/segmental-spatiotemporal-cnns-for-fine","title":"Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation","date":"2016-02-09","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}