{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:CIUTYBHYHHGF5DHDAS5FICYA4Y","short_pith_number":"pith:CIUTYBHY","schema_version":"1.0","canonical_sha256":"12293c04f839cc5e8ce304ba540b00e60e1a868fe71bd2f83f11c814292dc904","source":{"kind":"arxiv","id":"2501.01689","version":1},"attestation_state":"computed","paper":{"title":"Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hiep Dinh, Hieu Pham, Minh Ho, My Than, Nicolas Vuillerme, Son Le","submitted_at":"2025-01-03T08:10:08Z","abstract_excerpt":"Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly motion capture systems and specialized personnel, restricting its accessibility and broader application. Recent advancements in deep neural networks have enabled quantitative movement analysis using single-camera videos, offering an accessible alternative to conventional motion c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2501.01689","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-03T08:10:08Z","cross_cats_sorted":[],"title_canon_sha256":"83c25a3662b9ff0e9358ad189ed99f5fc700c7694556f6a38e0a3952751f629a","abstract_canon_sha256":"13efde30dcac805120c98c592b632c9216bc6a47a2336c9a44dbaca56e9b8779"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:39.532784Z","signature_b64":"9xwU4qnCLO/m9Q++6acdlHplbMwzfQ+FpzPj8C5acLorYjae9rtUpfAAjMk3q2YswVGnU8Id26jEo1OzVjZ5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12293c04f839cc5e8ce304ba540b00e60e1a868fe71bd2f83f11c814292dc904","last_reissued_at":"2026-07-05T09:56:39.532289Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:39.532289Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hiep Dinh, Hieu Pham, Minh Ho, My Than, Nicolas Vuillerme, Son Le","submitted_at":"2025-01-03T08:10:08Z","abstract_excerpt":"Gait and movement analysis have become a well-established clinical tool for diagnosing health conditions, monitoring disease progression for a wide spectrum of diseases, and to implement and assess treatment, surgery and or rehabilitation interventions. However, quantitative motion assessment remains limited to costly motion capture systems and specialized personnel, restricting its accessibility and broader application. Recent advancements in deep neural networks have enabled quantitative movement analysis using single-camera videos, offering an accessible alternative to conventional motion c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01689","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2501.01689/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2501.01689","created_at":"2026-07-05T09:56:39.532356+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.01689v1","created_at":"2026-07-05T09:56:39.532356+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01689","created_at":"2026-07-05T09:56:39.532356+00:00"},{"alias_kind":"pith_short_12","alias_value":"CIUTYBHYHHGF","created_at":"2026-07-05T09:56:39.532356+00:00"},{"alias_kind":"pith_short_16","alias_value":"CIUTYBHYHHGF5DHD","created_at":"2026-07-05T09:56:39.532356+00:00"},{"alias_kind":"pith_short_8","alias_value":"CIUTYBHY","created_at":"2026-07-05T09:56:39.532356+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.01689","citing_title":"Quantitative Gait Analysis from Single RGB Videos Using a Dual-Input Transformer-Based Network","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y","json":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y.json","graph_json":"https://pith.science/api/pith-number/CIUTYBHYHHGF5DHDAS5FICYA4Y/graph.json","events_json":"https://pith.science/api/pith-number/CIUTYBHYHHGF5DHDAS5FICYA4Y/events.json","paper":"https://pith.science/paper/CIUTYBHY"},"agent_actions":{"view_html":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y","download_json":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y.json","view_paper":"https://pith.science/paper/CIUTYBHY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.01689&json=true","fetch_graph":"https://pith.science/api/pith-number/CIUTYBHYHHGF5DHDAS5FICYA4Y/graph.json","fetch_events":"https://pith.science/api/pith-number/CIUTYBHYHHGF5DHDAS5FICYA4Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y/action/storage_attestation","attest_author":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y/action/author_attestation","sign_citation":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y/action/citation_signature","submit_replication":"https://pith.science/pith/CIUTYBHYHHGF5DHDAS5FICYA4Y/action/replication_record"}},"created_at":"2026-07-05T09:56:39.532356+00:00","updated_at":"2026-07-05T09:56:39.532356+00:00"}