{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4SAJ2CY6RPAU45LN3OKLXD7ZHR","short_pith_number":"pith:4SAJ2CY6","schema_version":"1.0","canonical_sha256":"e4809d0b1e8bc14e756ddb94bb8ff93c68692aa8db4f94a7f480f72ac95a00c2","source":{"kind":"arxiv","id":"2504.08877","version":1},"attestation_state":"computed","paper":{"title":"The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Andrea Arighi, Claudio Bettini, Daniela Galimberti, Gabriele Civitarese, Graziana Florio, Michele Fiori","submitted_at":"2025-04-11T15:36:08Z","abstract_excerpt":"Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functional loss. While MCI may progress to dementia, predicting this transition remains a clinical challenge due to limited and unreliable indicators. Behavioral changes, like in the execution of Activities of Daily Living (ADLs), can signal such progression. Sensorized smart homes and wearable devices offer an innovative solution for continuous, non-intrusive monitoring ADLs for MCI patients. However, current machine learning models for detecting behavio"},"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":"2504.08877","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-11T15:36:08Z","cross_cats_sorted":["cs.CY"],"title_canon_sha256":"948338bb2f2ef9423d2760217f23cebaf805e34c842a7abc0cc8b87a63e99f8c","abstract_canon_sha256":"e4d9da17d8f990f2d05f93afc5d8619e079b2183b0de10de985fdb6d8387dc70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:00.848751Z","signature_b64":"whMHZfa641t5axVxAfklMtZP+SO4YduGVUzurZ9i7WTxYW1d9+k3UB2OInewwStTU7o6IrnaDHiQDswPees9Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e4809d0b1e8bc14e756ddb94bb8ff93c68692aa8db4f94a7f480f72ac95a00c2","last_reissued_at":"2026-07-05T10:48:00.848286Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:00.848286Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The SERENADE project: Sensor-Based Explainable Detection of Cognitive Decline","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CY"],"primary_cat":"cs.LG","authors_text":"Andrea Arighi, Claudio Bettini, Daniela Galimberti, Gabriele Civitarese, Graziana Florio, Michele Fiori","submitted_at":"2025-04-11T15:36:08Z","abstract_excerpt":"Mild Cognitive Impairment (MCI) affects 12-18% of individuals over 60. MCI patients exhibit cognitive dysfunctions without significant daily functional loss. While MCI may progress to dementia, predicting this transition remains a clinical challenge due to limited and unreliable indicators. Behavioral changes, like in the execution of Activities of Daily Living (ADLs), can signal such progression. Sensorized smart homes and wearable devices offer an innovative solution for continuous, non-intrusive monitoring ADLs for MCI patients. However, current machine learning models for detecting behavio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.08877","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/2504.08877/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":"2504.08877","created_at":"2026-07-05T10:48:00.848338+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.08877v1","created_at":"2026-07-05T10:48:00.848338+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.08877","created_at":"2026-07-05T10:48:00.848338+00:00"},{"alias_kind":"pith_short_12","alias_value":"4SAJ2CY6RPAU","created_at":"2026-07-05T10:48:00.848338+00:00"},{"alias_kind":"pith_short_16","alias_value":"4SAJ2CY6RPAU45LN","created_at":"2026-07-05T10:48:00.848338+00:00"},{"alias_kind":"pith_short_8","alias_value":"4SAJ2CY6","created_at":"2026-07-05T10:48:00.848338+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.06174","citing_title":"X-BCD: Explainable Sensor-Based Behavioral Change Detection in Smart Home Environments","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR","json":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR.json","graph_json":"https://pith.science/api/pith-number/4SAJ2CY6RPAU45LN3OKLXD7ZHR/graph.json","events_json":"https://pith.science/api/pith-number/4SAJ2CY6RPAU45LN3OKLXD7ZHR/events.json","paper":"https://pith.science/paper/4SAJ2CY6"},"agent_actions":{"view_html":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR","download_json":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR.json","view_paper":"https://pith.science/paper/4SAJ2CY6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.08877&json=true","fetch_graph":"https://pith.science/api/pith-number/4SAJ2CY6RPAU45LN3OKLXD7ZHR/graph.json","fetch_events":"https://pith.science/api/pith-number/4SAJ2CY6RPAU45LN3OKLXD7ZHR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR/action/storage_attestation","attest_author":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR/action/author_attestation","sign_citation":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR/action/citation_signature","submit_replication":"https://pith.science/pith/4SAJ2CY6RPAU45LN3OKLXD7ZHR/action/replication_record"}},"created_at":"2026-07-05T10:48:00.848338+00:00","updated_at":"2026-07-05T10:48:00.848338+00:00"}