{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:OXR3SQNUUFASHSEIAR3X2G5G7B","short_pith_number":"pith:OXR3SQNU","canonical_record":{"source":{"id":"2308.01088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-02T11:44:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5e29dd334762d028a32483b50fe5d5bfaf88a931adcd9fb8023a96c059e4ef4d","abstract_canon_sha256":"1cff1847b14878c9a927b2cb68a79235e929edd051f046ab3ac5805a691dc4dd"},"schema_version":"1.0"},"canonical_sha256":"75e3b941b4a14123c88804777d1ba6f86cf262bdad0fe5ad8827f378bdee3975","source":{"kind":"arxiv","id":"2308.01088","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01088","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01088v1","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01088","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_12","alias_value":"OXR3SQNUUFAS","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_16","alias_value":"OXR3SQNUUFASHSEI","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_8","alias_value":"OXR3SQNU","created_at":"2026-07-05T06:37:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:OXR3SQNUUFASHSEIAR3X2G5G7B","target":"record","payload":{"canonical_record":{"source":{"id":"2308.01088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-02T11:44:49Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5e29dd334762d028a32483b50fe5d5bfaf88a931adcd9fb8023a96c059e4ef4d","abstract_canon_sha256":"1cff1847b14878c9a927b2cb68a79235e929edd051f046ab3ac5805a691dc4dd"},"schema_version":"1.0"},"canonical_sha256":"75e3b941b4a14123c88804777d1ba6f86cf262bdad0fe5ad8827f378bdee3975","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:05.299471Z","signature_b64":"qAU8G0NzgwU8nmNeLR0WHNMzMJWwf8NLmuafjV7iY70+kcqlBbHKhIZL3QYKwz6ixShuk5+5KgrMkew+cYeWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"75e3b941b4a14123c88804777d1ba6f86cf262bdad0fe5ad8827f378bdee3975","last_reissued_at":"2026-07-05T06:37:05.298992Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:05.298992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2308.01088","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:37:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bsKXhY3QVIAO+yoWk1DslVevgL/gL/QV4iCEH99no9x7Cy0PmZW96xVu/p2i82ljGGARkSWqW59H2AlIY47YAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T17:02:25.868871Z"},"content_sha256":"557552fa5a4fb5c5657e52860b1f0aa588317cbf5f3f3af2cbfd6546f38d12ca","schema_version":"1.0","event_id":"sha256:557552fa5a4fb5c5657e52860b1f0aa588317cbf5f3f3af2cbfd6546f38d12ca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:OXR3SQNUUFASHSEIAR3X2G5G7B","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Claudia Ferraris, Gabriella Olmo, Gianluca Amprimo, Giulia Masi, Giuseppe Pettiti, Lorenzo Priano","submitted_at":"2023-08-02T11:44:49Z","abstract_excerpt":"Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human-computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01088","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/2308.01088/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:37:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"r+4tvivYKewq3ovyjz+AYBDNLyumv3vM5UpI/sXkrZdZwbXUkOTtfeXk/1FJdPmAxatPkSRBmQgjbB97jCgJCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T17:02:25.869368Z"},"content_sha256":"2235f9dedd477f9289999bc23985e85635f5acb6328f4af687e3b2314cbe5e03","schema_version":"1.0","event_id":"sha256:2235f9dedd477f9289999bc23985e85635f5acb6328f4af687e3b2314cbe5e03"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/bundle.json","state_url":"https://pith.science/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-02T17:02:25Z","links":{"resolver":"https://pith.science/pith/OXR3SQNUUFASHSEIAR3X2G5G7B","bundle":"https://pith.science/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/bundle.json","state":"https://pith.science/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OXR3SQNUUFASHSEIAR3X2G5G7B/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:OXR3SQNUUFASHSEIAR3X2G5G7B","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1cff1847b14878c9a927b2cb68a79235e929edd051f046ab3ac5805a691dc4dd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-02T11:44:49Z","title_canon_sha256":"5e29dd334762d028a32483b50fe5d5bfaf88a931adcd9fb8023a96c059e4ef4d"},"schema_version":"1.0","source":{"id":"2308.01088","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.01088","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"arxiv_version","alias_value":"2308.01088v1","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.01088","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_12","alias_value":"OXR3SQNUUFAS","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_16","alias_value":"OXR3SQNUUFASHSEI","created_at":"2026-07-05T06:37:05Z"},{"alias_kind":"pith_short_8","alias_value":"OXR3SQNU","created_at":"2026-07-05T06:37:05Z"}],"graph_snapshots":[{"event_id":"sha256:2235f9dedd477f9289999bc23985e85635f5acb6328f4af687e3b2314cbe5e03","target":"graph","created_at":"2026-07-05T06:37:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2308.01088/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate 3D tracking of hand and fingers movements poses significant challenges in computer vision. The potential applications span across multiple domains, including human-computer interaction, virtual reality, industry, and medicine. While gesture recognition has achieved remarkable accuracy, quantifying fine movements remains a hurdle, particularly in clinical applications where the assessment of hand dysfunctions and rehabilitation training outcomes necessitate precise measurements. Several novel and lightweight frameworks based on Deep Learning have emerged to address this issue; however,","authors_text":"Claudia Ferraris, Gabriella Olmo, Gianluca Amprimo, Giulia Masi, Giuseppe Pettiti, Lorenzo Priano","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-02T11:44:49Z","title":"Hand tracking for clinical applications: validation of the Google MediaPipe Hand (GMH) and the depth-enhanced GMH-D frameworks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.01088","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:557552fa5a4fb5c5657e52860b1f0aa588317cbf5f3f3af2cbfd6546f38d12ca","target":"record","created_at":"2026-07-05T06:37:05Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1cff1847b14878c9a927b2cb68a79235e929edd051f046ab3ac5805a691dc4dd","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-02T11:44:49Z","title_canon_sha256":"5e29dd334762d028a32483b50fe5d5bfaf88a931adcd9fb8023a96c059e4ef4d"},"schema_version":"1.0","source":{"id":"2308.01088","kind":"arxiv","version":1}},"canonical_sha256":"75e3b941b4a14123c88804777d1ba6f86cf262bdad0fe5ad8827f378bdee3975","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75e3b941b4a14123c88804777d1ba6f86cf262bdad0fe5ad8827f378bdee3975","first_computed_at":"2026-07-05T06:37:05.298992Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:37:05.298992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qAU8G0NzgwU8nmNeLR0WHNMzMJWwf8NLmuafjV7iY70+kcqlBbHKhIZL3QYKwz6ixShuk5+5KgrMkew+cYeWCw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:37:05.299471Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.01088","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:557552fa5a4fb5c5657e52860b1f0aa588317cbf5f3f3af2cbfd6546f38d12ca","sha256:2235f9dedd477f9289999bc23985e85635f5acb6328f4af687e3b2314cbe5e03"],"state_sha256":"6dfd4a07111be93d05a61a28ae645d0475c81fc422be4d940e24c03b1ecd0213"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VftCB5Vfh5pr0lkpKSXuCsTnRfr2SFCS0aMVuisvc2daBFAx0iSJZfyrUJWWhNhatlrRc52B/gaqSju58fxDBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T17:02:25.872868Z","bundle_sha256":"507b55c54132283b871d6225e30bb9ead67b009872cedc1a1e3de858cd92f7cc"}}