{"as_of":"2026-08-13T22:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0a10b31e0fd32a7c03dea09421e6eab7d3878fbf607ddd06e967ecc8d1ec8cb5","coverage":[{"denominator":51,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":51,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T04:48:11.800060Z","state":"measured"},{"denominator":51,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":51,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2412.01056/citation-record","integrity":"/paper/2412.01056/integrity","json":"/paper/2412.01056/citation-record.json","paper":"/paper/2412.01056"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1159/000456541","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.224391Z","title":"Association Between Various Brain Pathologies and Gait Disturbance","venue":null,"work_id":"bcd4db8c-7b89-40c0-a060-e9449886d200","year":2017},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.541392Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:295d678da3833f4ba1b7a6a18446db7884aea6d3d7f284ba110828973609f194","observation_id":"bf98886f-18e0-45ed-9d91-2a3afe172532","resolution":{"observed_at":"2026-08-12T04:48:12.229962Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0003-9993(94)90399-9","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.207574Z","title":"Procedures for gait analysis","venue":null,"work_id":"de519dc0-4a56-4ace-90c3-85e8e57e54bc","year":1994},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.547467Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:3d9050d2693d012c81acf518ccd039741d5989188c2da7399af4eedc5aaed58d","observation_id":"a65f08f9-5b47-4fa6-96a0-60478aa59236","resolution":{"observed_at":"2026-08-12T04:48:12.212886Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/s1356-689x(99)80003-4","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.191482Z","title":"Gait analysis in the therapeutic environment","venue":null,"work_id":"471e2729-39c0-4456-ab52-8cf13d014b55","year":1999},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.552770Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:534ea4e0f11f88cf2fbbbce2d03d8d7a0d1d5d66a7ad178c2ae0599fff867151","observation_id":"0fcb3f92-6067-46d6-af23-eca596e29ffa","resolution":{"observed_at":"2026-08-12T04:48:12.196499Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:13.230558Z","title":"Gait Analysis: Normal and Pathological Function","venue":null,"work_id":"4a6494a2-a75e-474e-a3e1-297ffd5fd36d","year":2010},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.558036Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:75357334f3cebfb05150deb82e080c24dea18bd10ff0adb9ba1643e8f5ba27db","observation_id":"82b08b75-b2f1-4acf-955d-c18fc8c39d1f","resolution":{"observed_at":"2026-08-12T04:48:13.235507Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.563304Z","title":"Reliability of observational kinematic gait analysis","venue":null,"work_id":null,"year":1985},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.563304Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:0c62afb78785ec5a445a6d092eabb2a7d20c1ca9ce748203b8442a9a92c49ad2","observation_id":"798547e6-f198-41c8-a69d-c7f0c5acd5ae","resolution":{"observed_at":"2026-08-12T04:48:11.563304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.568636Z","title":"Reliability of videotaped observational gait analysis in patients with orthopedic impairments","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.568636Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:6053b500ea282f4302df903a2e8800b18e35b2339261601e6f42eaec69ca2094","observation_id":"558f387f-09d2-434e-9150-95b55f4c406f","resolution":{"observed_at":"2026-08-12T04:48:11.568636Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:13.214460Z","title":"The Effect of Preoperative Gait Analysis on Orthopaedic Decision Making","venue":null,"work_id":"7d586512-9c8e-4ed0-bb50-7a4086628489","year":2000},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.574099Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:1e584697e1eb4ab07d1019764458ebcac5a6287df04cbb4aff681d2024c3387a","observation_id":"fa63a595-3401-4e19-87ad-725897fa6067","resolution":{"observed_at":"2026-08-12T04:48:13.219414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.gaitpost.2023.06.014","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.155000Z","title":"The impact of Three-Dimensional Gait Analysis in adults with pathological gait on management recommendations","venue":null,"work_id":"6ad2d585-7e34-4101-aff4-22921c4ded7e","year":2023},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.578830Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:581041332e4b4a39279faf58eff93925daff21d68493e4eaab2318f38e27e4e4","observation_id":"44b48ac1-0fc7-45e0-bf9c-b9eb8edb3337","resolution":{"observed_at":"2026-08-12T04:48:12.159790Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3791/57063","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.138608Z","title":"Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder","venue":null,"work_id":"62c0e21a-abd0-4b75-a309-3b3012317afe","year":2018},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.583694Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:3ce21ae2095325c6824e0acff8078047d1ad7254766b8617b24a88282af5d526","observation_id":"1af9dc0b-15c6-4e30-b78d-9c3ff5273144","resolution":{"observed_at":"2026-08-12T04:48:12.144237Z","resolver_source":"doi_truncated","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.588835Z","title":"Present and future of gait assessment in clinical practice: Towards the application of novel trends and technologies","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.588835Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:c59c6716436ff70abfdb2f8f6ef1a9695134d325e0d0b359fedf4e08cbdb19e9","observation_id":"9d6ab272-84f9-4eb1-b630-775a2531947d","resolution":{"observed_at":"2026-08-12T04:48:11.588835Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pone.0223549","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.122546Z","title":"Quantifying normal and parkinsonian gait features from home movies: Practical application of a deep learning–based 2D pose estimator","venue":null,"work_id":"14116805-15f5-49f8-9713-73e3ebb06d39","year":2019},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.594576Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:bed182ff0244a6b47c47991cb645feba0674de78ec1d8aa350741515bbcaade5","observation_id":"7151c9e0-84c2-4834-bfc2-4333be4701b9","resolution":{"observed_at":"2026-08-12T04:48:12.127860Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.599947Z","title":"Two-dimensional video-based analysis of human gait using pose estimation","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.599947Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:2c478726bdb4f8eafe97c060e784de42241a5ebe10284d8a814d645df3f078ec","observation_id":"0528288f-8d5b-4a5b-893d-6a9504c11dda","resolution":{"observed_at":"2026-08-12T04:48:11.599947Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.10204","last_updated":"2020-06-17T23:52:46Z","snapshot_observed_at":"2026-08-07T03:35:23.462173Z","submitted_at":"2020-06-17T23:52:46Z","title":"BlazePose: On-device Real-time Body Pose tracking","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.10204","snapshot_observed_at":"2026-08-12T04:48:11.605603Z","title":"BlazePose: On-device Real-time Body Pose tracking; 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.605603Z"},"links":{"cited_paper":"/paper/2006.10204","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:9502b9cc6eed7b2c9cd07b336d2529c3c90de0b60db29e9849e64cb44bdb93f2","observation_id":"a7ea9f55-1159-4b93-94ac-9192f807ef5c","resolution":{"observed_at":"2026-08-12T04:48:11.605603Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.08008","last_updated":"2019-05-30T23:46:18Z","snapshot_observed_at":"2026-08-09T07:44:49.841044Z","submitted_at":"2018-12-18T18:50:33Z","title":"OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.08008","snapshot_observed_at":"2026-08-12T04:48:11.611621Z","title":"OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields; 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.611621Z"},"links":{"cited_paper":"/paper/1812.08008","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:264b6dc01102705ffa28db6b9375339cb8e62714b8dac6a370eca9c7861fa715","observation_id":"aeaefc88-fd2f-48d3-a4ca-c63cbfd33620","resolution":{"observed_at":"2026-08-12T04:48:11.611621Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.617182Z","title":"Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.617182Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:92efb83ce04d5f89e44d480fd541ae7483df0e531332dfa30e040392276cb36f","observation_id":"09273abb-7e00-4dd7-87eb-6fdd47c6137b","resolution":{"observed_at":"2026-08-12T04:48:11.617182Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-024-53217-7","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.085542Z","title":"Validation of portable in-clinic video-based gait analysis for prosthesis users","venue":null,"work_id":"d357b3f4-be8c-41f5-8c1b-12bb1745895d","year":2024},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.622332Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:5f0cfa190c86556744c29d2f34ff1cdc7fe35f62cc6d1b509bca10ee59646d4e","observation_id":"831a8d04-e9b1-4246-a1b5-dbb6451a7a7b","resolution":{"observed_at":"2026-08-12T04:48:12.090780Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.627965Z","title":"Deep neural networks enable quantitative movement analysis using single-camera videos","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.627965Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:f4686e52b86146920f8f14f3470a26bfdc2f94d08486d5b3bcc20191276067a0","observation_id":"4cc2c769-bbdc-441e-ba8d-30ca20e9cd9e","resolution":{"observed_at":"2026-08-12T04:48:11.627965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.633139Z","title":"Automatic labeling of Parkinson’s Disease gait videos with weak supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.633139Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:90df58f75229562a3610bdeb6f16480a7918ba17fd346fe588b8be90052a16c5","observation_id":"9eb476a4-e7cb-49bb-baf0-2f1f31928402","resolution":{"observed_at":"2026-08-12T04:48:11.633139Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:13.197893Z","title":"https://www.ibm.com/reports/data-breach","venue":null,"work_id":"b56bcd4f-3779-4b15-a0b6-9dedb098a6c7","year":2024},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.638582Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:abd528e272726d51fff72b59dfabdf2f130c45486fdf0a25744c3c9ac091d066","observation_id":"5c04851c-92e6-4035-999b-feafd2a08b14","resolution":{"observed_at":"2026-08-12T04:48:13.202984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.08172","last_updated":"2019-06-14T05:49:22Z","snapshot_observed_at":"2026-08-05T19:15:35.517082Z","submitted_at":"2019-06-14T05:49:22Z","title":"MediaPipe: A Framework for Building Perception Pipelines","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.08172","snapshot_observed_at":"2026-08-12T04:48:11.643814Z","title":"MediaPipe: A Framework for Building Perception Pipelines; 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.643814Z"},"links":{"cited_paper":"/paper/1906.08172","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:0458fa29d84ce70cdbe53249f91b272a8468609b496647fb59e6e92739255fe8","observation_id":"bb46ce73-9340-4795-8e0e-b66e6a8777c4","resolution":{"observed_at":"2026-08-12T04:48:11.643814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.649522Z","title":"A tutorial on human activity recognition using body-worn inertial sensors","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.649522Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:8c094de0eac884e983b297f6ae0202af9fc91ccc394b12697ea430e204a4072a","observation_id":"4c93410e-213e-4283-bc79-40e15472a535","resolution":{"observed_at":"2026-08-12T04:48:11.649522Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.654472Z","title":"Permutation importance: a corrected feature importance measure","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.654472Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:92f2c4a258d25cc1d282c4ca04f0b2279b53a7c9c17a79dce9565300b5ba57f8","observation_id":"19bbe7de-9765-4a27-9098-ab85494536ae","resolution":{"observed_at":"2026-08-12T04:48:11.654472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.659537Z","title":"Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.659537Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:43ce5b462b1a1131c19f20ecbf2c5187bbf5913f679b666913c46996ac420668","observation_id":"53cae21e-8097-4ccf-9a7a-d4acd5ff70c6","resolution":{"observed_at":"2026-08-12T04:48:11.659537Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:13.181891Z","title":"On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines","venue":null,"work_id":"bd68ba12-1076-4ec2-90f8-4bfb3f2c2fd2","year":2001},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.664532Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:680abbe6759460b54b1ef24b87bd3b2601a106d88e231d8e08f82f1d1f0196c1","observation_id":"fc8b946d-9595-4f61-97ec-54383ef4971d","resolution":{"observed_at":"2026-08-12T04:48:13.186946Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.669982Z","title":"Random Forests","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.669982Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:f0b942361e56517874995ce4d6e287e395f4204fd52c5786e9aa594880f5626b","observation_id":"5e3cfe0b-5fe7-4658-a655-23ba5e78a5b4","resolution":{"observed_at":"2026-08-12T04:48:11.669982Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.675332Z","title":"XGBoost: A Scalable Tree Boosting System","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.675332Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:ce7ff64eed38b8f342ebbe6f69e8dd54db7eab67199eb6f89e1bb687ce169769","observation_id":"01b3a082-6671-429c-849a-218dba3f1df5","resolution":{"observed_at":"2026-08-12T04:48:11.675332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.680100Z","title":"On preserving statistical characteristics of accelerometry data using their empirical cumulative distribution","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.680100Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:dff4dde719e9adcdeaa417265e6863292b096021ce6997491ecd4671b207461a","observation_id":"618fc86a-3200-425f-adac-195d8ce750aa","resolution":{"observed_at":"2026-08-12T04:48:11.680100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1610.07717","last_updated":"2017-05-19T21:20:18Z","snapshot_observed_at":"2026-07-06T05:15:53.421148Z","submitted_at":"2016-10-25T03:31:58Z","title":"Distributed and parallel time series feature extraction for industrial big data applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1610.07717","snapshot_observed_at":"2026-08-12T04:48:11.684950Z","title":"Distributed and parallel time series feature extraction for industrial big data applications; 2017","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.684950Z"},"links":{"cited_paper":"/paper/1610.07717","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:dc60834df62bcdb703a04a39a95cfc1cc220c0a30212988fe5bd63d9d9a37825","observation_id":"d3a9bd10-ead1-457c-b471-a18ed3d5af6d","resolution":{"observed_at":"2026-08-12T04:48:11.684950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.690140Z","title":"The control of the false discovery rate in multiple testing under dependency","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.690140Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:d6cc825a0f880c5916ab835ffc8ec7fd2e4e5553abed3576b173baff4d486166","observation_id":"2fa170d0-cf38-4734-b47f-e2beba352104","resolution":{"observed_at":"2026-08-12T04:48:11.690140Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.695266Z","title":"Editorial: special issue on learning from imbalanced data sets","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.695266Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:a5cc4cc55a258b2146a175d72766cbdfbbcd848e519a5f7b4bab845568800587","observation_id":"38f10b33-7e34-49b8-be8e-8c16c239c623","resolution":{"observed_at":"2026-08-12T04:48:11.695266Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.700020Z","title":"SMOTE: Synthetic Minority Over-sampling Technique","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.700020Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:d9bef116eacd5b3e1e9b17a043ffb57f41670bbc5eb9aa338802d4cde4df02c2","observation_id":"626c5095-4766-4a3b-a654-9c4755bf30a1","resolution":{"observed_at":"2026-08-12T04:48:11.700020Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s41598-021-91797-w","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.009358Z","title":"XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes","venue":null,"work_id":"bc3e03d6-c606-4e1d-9bd7-972403dd8022","year":2021},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.705671Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:58f4caa80cf3bdf59afde50d9f10065a0c18157cdc8cc4d7d93677c922fbb6f2","observation_id":"1d4200bd-eadf-4758-b593-89ee6cdd0bbc","resolution":{"observed_at":"2026-08-12T04:48:12.014247Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/s23198330","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.992044Z","title":"Classifying Tremor Dominant and Postural Instability and Gait Difficulty Subtypes of Parkinson’s Disease from Full-Body Kinematics","venue":null,"work_id":"0650e65f-2ec4-449a-a429-1030d7381dcb","year":2023},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.711419Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:9ac082918d925d31e8222ffe905d1d52c1cb014016942548359444b09c1b3960","observation_id":"267eb368-cd03-4e87-8221-363e118af673","resolution":{"observed_at":"2026-08-12T04:48:11.998099Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.716312Z","title":"Explainable Artificial Intelligence and Wearable Sensor-Based Gait Analysis to Identify Patients with Osteopenia and Sarcopenia in Daily Life","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.716312Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:3efde3ad5b555291bfdc5e6bbfb1f39b9c269d39efb85e1bf23488d4f0f5a8bd","observation_id":"281cb465-fd35-4674-bfc1-d0486a9f75a2","resolution":{"observed_at":"2026-08-12T04:48:11.716312Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.721709Z","title":"Nested cross-validation when selecting classifiers is overzealous for most practical applications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.721709Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:05759520553d6df1b80edfc1ec593d0d0335e2944bdf4d9c26665d4c7724fbf2","observation_id":"c7e1a43c-f1ff-4e60-a398-2b59eeb3fa75","resolution":{"observed_at":"2026-08-12T04:48:11.721709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bioinformatics/btaa046","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.965755Z","title":"Consensus features nested cross-validation","venue":null,"work_id":"6d25cf26-2f35-4834-aa23-faed9e85920f","year":2020},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.726484Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:e22ece6bf4cde506c0ed99200e92163763ab8701da92f5d5a0d1c815a1c0604d","observation_id":"c6c0ee6b-2093-4ca9-9092-eff9f517191e","resolution":{"observed_at":"2026-08-12T04:48:11.970763Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.731604Z","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.731604Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:858f08e8cc88ddd99447a4ea80b180c8f32c48444ec20975a63c4157055ce405","observation_id":"e2335c45-27c3-4dd4-92c2-cdc05655561d","resolution":{"observed_at":"2026-08-12T04:48:11.731604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:13.164895Z","title":"Available from: https://en.wikipedia.org/w/index.php?title=Binomial_proportion_ confidence_interval&oldid=1223672356","venue":null,"work_id":"c17852ab-ad36-467a-9520-879562895f07","year":2024},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.736661Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:6978210fefa91d81ff96dc25daeb92dc7cac7f55d272199e82b3eacbbac50e46","observation_id":"2908e6ef-f908-4eac-8fe9-6b62aa8cf895","resolution":{"observed_at":"2026-08-12T04:48:13.170465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.741399Z","title":"A comparative analysis of gradient boosting algorithms","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.741399Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:da1e9aee4f431dd82c98b37f16bb3d27d5b16d2c16f7ddaaa27a29075273cedb","observation_id":"3d61582b-7331-401e-95cf-7ed36de2a0b8","resolution":{"observed_at":"2026-08-12T04:48:11.741399Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2934.15159","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.475451Z","title":"Pictorial Structures for Object Recognition","venue":null,"work_id":"c3680e87-9c29-4407-8abb-c7803691d231","year":2005},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.746139Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:39606d5ae458b7f70631deb21681be63e5c0e964160e5549bbc41a3309d60e52","observation_id":"421b7968-d663-4e2e-b539-fb76324bdb69","resolution":{"observed_at":"2026-08-12T04:48:12.483360Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/6909610","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:12.386604Z","title":"DeepPose: Human Pose Estimation via Deep Neural Networks","venue":null,"work_id":"e9d73278-02e4-4c26-8f71-bb9271f40d95","year":2014},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.750946Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:911ab63d5a546086f00dafa9d5ea744735658531481075941e3e7b2ebb2d7877","observation_id":"ad043daf-b64c-4be0-9760-ea5b2b45b770","resolution":{"observed_at":"2026-08-12T04:48:12.394780Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pcbi.1011556","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.939915Z","title":"Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics","venue":null,"work_id":"59175524-46eb-4efd-bf73-fdfa5e127611","year":2023},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.756335Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:d1f80823be2e453e93457af4514ffeabb89b0d76b1122943d4c8dd1a23db5ac4","observation_id":"4d1eda15-92c8-412d-b18b-731ab4092da5","resolution":{"observed_at":"2026-08-12T04:48:11.945082Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1101/2024.05.01.591976v1","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.923718Z","title":"Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences; 2024","venue":null,"work_id":"6cacf28b-e032-4ccc-b65c-4d1d3322b467","year":2024},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.761404Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:9165c08efcf4ab666b91bae77feb933a56bc7655f01dcc67ea10c51b47010185","observation_id":"922e93bc-5095-42ec-b5cf-ed55007ee5d1","resolution":{"observed_at":"2026-08-12T04:48:11.928950Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10439-013-0852-0","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.907009Z","title":"New Perspectives in Human Movement Variability","venue":null,"work_id":"48a0f6c4-f7cc-4a1f-8c90-fdc90a6fa843","year":2013},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.766455Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:73ae658cd4569218f9a4493c7e63e081d6c885f19f07518619a9f96694ad68ed","observation_id":"e40ed3d3-3c90-47ac-863e-e9999a212765","resolution":{"observed_at":"2026-08-12T04:48:11.912200Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.771249Z","title":"Human movement variability, nonlinear dynamics, and pathology: is there a connection? Human Movement Science","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.771249Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:d856941341805708fa1d849010a75101f7c7852b61ef4024e0d8e8bf5f50c139","observation_id":"5ddbf099-6128-432f-8ae4-eb508cf38051","resolution":{"observed_at":"2026-08-12T04:48:11.771249Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.clinbiomech.2014.03.013","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.879953Z","title":"Frontal plane compensatory strategies associated with self-selected walking speed in individuals post-stroke","venue":null,"work_id":"fab4f632-ab6c-458b-b1ac-67a351a4f58f","year":2014},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.776103Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:aaece56eda1b308c430d5c7a599e7685a885bca32ab4e82d3905dc2b08a20c63","observation_id":"d50ca3bd-fca3-4d65-8451-53dc836a465c","resolution":{"observed_at":"2026-08-12T04:48:11.885025Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.2522/ptj.20090425","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.864035Z","title":"Influence of systematic increases in treadmill walking speed on gait kinematics after stroke","venue":null,"work_id":"aa66f20b-2a44-424f-a9c6-8b4ad7f52fa9","year":2011},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.780885Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:5e2d89325f77a73d6e637d52127d21d26d9db34bdf03436b68639cb8fd76027c","observation_id":"318e8778-2c55-4a03-9149-db96b77e6ba4","resolution":{"observed_at":"2026-08-12T04:48:11.869156Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.gaitpost.2013.02.006","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.845507Z","title":"The how and why of arm swing during human walking","venue":null,"work_id":"0d827362-eadb-46c7-a204-0ea1121442eb","year":2013},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.785684Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:04504f883855c2ecc93397cd5833cc49dce825c8f99e332a3a09bd88bf96809f","observation_id":"e86d607b-ebc7-4ef7-8697-f603064d2ede","resolution":{"observed_at":"2026-08-12T04:48:11.852612Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T04:48:11.790484Z","title":"Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.790484Z"},"links":{"citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:40c94bff4963e60ae0a327eb2f897e0737ba335a1815e2f413f4b9892fa8d8c8","observation_id":"b97df498-4eaa-448f-8578-46d632d4879d","resolution":{"observed_at":"2026-08-12T04:48:11.790484Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2005.00698","last_updated":"2020-12-18T03:08:37Z","snapshot_observed_at":"2026-08-12T03:39:18.711599Z","submitted_at":"2020-05-02T04:30:31Z","title":"Deep ConvLSTM with self-attention for human activity decoding using wearables","version":2},"cited_work":{"arxiv_id":"2005.00698","doi":null,"metadata_source":"pith","pith_arxiv_id":"2005.00698","snapshot_observed_at":"2026-08-12T04:48:12.265846Z","title":"Deep ConvLSTM with self-attention for human activity decoding using wearables","venue":"cs.HC","work_id":"9e807cfa-9700-4b40-ab00-d7307bdf1f9a","year":2020},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.795122Z"},"links":{"cited_paper":"/paper/2005.00698","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:a96a55f761920efa9b5b3831907cac8fe7e7e7f2b88f26dfa22b4e79711c9259","observation_id":"0fb1bb3a-09f6-4fb6-8bfc-819e3383f2bd","resolution":{"observed_at":"2026-08-12T04:48:12.270943Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.12484","last_updated":"2022-10-13T01:53:23Z","snapshot_observed_at":"2026-08-13T15:55:03.846321Z","submitted_at":"2022-04-26T17:55:04Z","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.12484","snapshot_observed_at":"2026-08-12T04:48:11.800060Z","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation; 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-12T04:48:11.800060Z"},"links":{"cited_paper":"/paper/2204.12484","citing_paper":"/paper/2412.01056"},"observation_digest":"sha256:2a00895be7901504d821849c6808fa9c8fcbc92aab48db914e5e6a53caae03cb","observation_id":"0ffbc15a-98f1-48d5-a836-fac799003063","resolution":{"observed_at":"2026-08-12T04:48:11.800060Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.01056","last_updated":"2024-12-02T02:35:40Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T04:41:57.996843Z","submitted_at":"2024-12-02T02:35:40Z","title":"Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos"},"reference_resolution":{"displayed":51,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":27,"verified_exact":18,"verified_fuzzy":5},"total_outbound_references":51},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 0 inbound Pith citation observations for arXiv:2412.01056."}