{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:G7HRNLRW2NNRDREEXEVG4FNSNZ","short_pith_number":"pith:G7HRNLRW","schema_version":"1.0","canonical_sha256":"37cf16ae36d35b11c484b92a6e15b26e660d2ca9f28488c0054483a8510ce8a2","source":{"kind":"arxiv","id":"2607.25710","version":1},"attestation_state":"computed","paper":{"title":"Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohammed Hindawi, Tresor Y. Koffi, Yohan Dupuis, Youssef Mourchid","submitted_at":"2026-07-28T13:33:57Z","abstract_excerpt":"Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joints skeleton data treated as a graph using Spatio-Temporal Graph Convolut"},"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":"2607.25710","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T13:33:57Z","cross_cats_sorted":[],"title_canon_sha256":"23a9da8795d20fbb5c7be458fcdae4a87e454e3aaf8e4253c4f6f15030ff089b","abstract_canon_sha256":"39b730ebaa3380f4d548793ed568eef73d5e2d0965e7bae9d2c6b7ff4966cfbd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:58.088662Z","signature_b64":"HdFstJHKZfu7soblwHLIR5BqXz7eS2aKUA2bXhVAR9h5ROHBa067BYCBLLYLNFgAu0a47jcIt2EkHaD7jp6+Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"37cf16ae36d35b11c484b92a6e15b26e660d2ca9f28488c0054483a8510ce8a2","last_reissued_at":"2026-07-29T01:25:58.087758Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:58.087758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Impact Detection in Fall Events: Leveraging Spatio-Temporal Graph Convolutional Networks and Recurrent Neural Networks Using 3D Skeletons Data","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mohammed Hindawi, Tresor Y. Koffi, Yohan Dupuis, Youssef Mourchid","submitted_at":"2026-07-28T13:33:57Z","abstract_excerpt":"Fall represents a significant risk of accidental death among individuals aged over 65, presenting a global health concern. A fall is defined as any event where a person loses balance and moves to an off-position, which may or may not result in an impact where the person hits the ground. While fall detection systems have achieved good results in general, impact detection within falls remains challenging. This study proposes an efficient methodology for accurately detecting impacts within fall events by incorporating 3D joints skeleton data treated as a graph using Spatio-Temporal Graph Convolut"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25710","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/2607.25710/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":"2607.25710","created_at":"2026-07-29T01:25:58.088193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25710v1","created_at":"2026-07-29T01:25:58.088193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25710","created_at":"2026-07-29T01:25:58.088193+00:00"},{"alias_kind":"pith_short_12","alias_value":"G7HRNLRW2NNR","created_at":"2026-07-29T01:25:58.088193+00:00"},{"alias_kind":"pith_short_16","alias_value":"G7HRNLRW2NNRDREE","created_at":"2026-07-29T01:25:58.088193+00:00"},{"alias_kind":"pith_short_8","alias_value":"G7HRNLRW","created_at":"2026-07-29T01:25:58.088193+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ","json":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ.json","graph_json":"https://pith.science/api/pith-number/G7HRNLRW2NNRDREEXEVG4FNSNZ/graph.json","events_json":"https://pith.science/api/pith-number/G7HRNLRW2NNRDREEXEVG4FNSNZ/events.json","paper":"https://pith.science/paper/G7HRNLRW"},"agent_actions":{"view_html":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ","download_json":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ.json","view_paper":"https://pith.science/paper/G7HRNLRW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25710&json=true","fetch_graph":"https://pith.science/api/pith-number/G7HRNLRW2NNRDREEXEVG4FNSNZ/graph.json","fetch_events":"https://pith.science/api/pith-number/G7HRNLRW2NNRDREEXEVG4FNSNZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ/action/storage_attestation","attest_author":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ/action/author_attestation","sign_citation":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ/action/citation_signature","submit_replication":"https://pith.science/pith/G7HRNLRW2NNRDREEXEVG4FNSNZ/action/replication_record"}},"created_at":"2026-07-29T01:25:58.088193+00:00","updated_at":"2026-07-29T01:25:58.088193+00:00"}