{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:ZGJGZZVVEARLJ2NSCLH6A46YL6","short_pith_number":"pith:ZGJGZZVV","schema_version":"1.0","canonical_sha256":"c9926ce6b52022b4e9b212cfe073d85f96820fe149d8e6ec679762973af0503a","source":{"kind":"arxiv","id":"2007.03615","version":1},"attestation_state":"computed","paper":{"title":"Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","stat.ML"],"primary_cat":"cs.CY","authors_text":"Ian Craddock, James Selwood, Liz Coulthard, Niall Twomey, Rafael Poyiadzi, Raul Santos-Rodriguez, Weisong Yang, Yoav Ben-Shlomo","submitted_at":"2020-07-03T18:46:49Z","abstract_excerpt":"There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \\textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset o"},"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":"2007.03615","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CY","submitted_at":"2020-07-03T18:46:49Z","cross_cats_sorted":["cs.LG","eess.SP","stat.ML"],"title_canon_sha256":"f6b10406f53518d442efb00cd2e5e9d54d73106f49e00943fbaef7dd9563f1f0","abstract_canon_sha256":"6b4c6eaef24d849d7f7c89f8cb087a2248bedcba7f2eba971eff2546d6f050cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:17:04.780330Z","signature_b64":"ZWcJSEhqfsLrsCOVPfR9FEn1L9cx/fgRkASCnXRZDQU3BavxH+WYI/a+o/qGd83QRk1zAHwy4ffwzVwFcGQjCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c9926ce6b52022b4e9b212cfe073d85f96820fe149d8e6ec679762973af0503a","last_reissued_at":"2026-07-05T01:17:04.779906Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:17:04.779906Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Detecting Signatures of Early-stage Dementia with Behavioural Models Derived from Sensor Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","stat.ML"],"primary_cat":"cs.CY","authors_text":"Ian Craddock, James Selwood, Liz Coulthard, Niall Twomey, Rafael Poyiadzi, Raul Santos-Rodriguez, Weisong Yang, Yoav Ben-Shlomo","submitted_at":"2020-07-03T18:46:49Z","abstract_excerpt":"There is a pressing need to automatically understand the state and progression of chronic neurological diseases such as dementia. The emergence of state-of-the-art sensing platforms offers unprecedented opportunities for indirect and automatic evaluation of disease state through the lens of behavioural monitoring. This paper specifically seeks to characterise behavioural signatures of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in the \\textit{early} stages of the disease. We introduce bespoke behavioural models and analyses of key symptoms and deploy these on a novel dataset o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.03615","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/2007.03615/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":"2007.03615","created_at":"2026-07-05T01:17:04.779967+00:00"},{"alias_kind":"arxiv_version","alias_value":"2007.03615v1","created_at":"2026-07-05T01:17:04.779967+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.03615","created_at":"2026-07-05T01:17:04.779967+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZGJGZZVVEARL","created_at":"2026-07-05T01:17:04.779967+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZGJGZZVVEARLJ2NS","created_at":"2026-07-05T01:17:04.779967+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZGJGZZVV","created_at":"2026-07-05T01:17:04.779967+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.09289","citing_title":"Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6","json":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6.json","graph_json":"https://pith.science/api/pith-number/ZGJGZZVVEARLJ2NSCLH6A46YL6/graph.json","events_json":"https://pith.science/api/pith-number/ZGJGZZVVEARLJ2NSCLH6A46YL6/events.json","paper":"https://pith.science/paper/ZGJGZZVV"},"agent_actions":{"view_html":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6","download_json":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6.json","view_paper":"https://pith.science/paper/ZGJGZZVV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2007.03615&json=true","fetch_graph":"https://pith.science/api/pith-number/ZGJGZZVVEARLJ2NSCLH6A46YL6/graph.json","fetch_events":"https://pith.science/api/pith-number/ZGJGZZVVEARLJ2NSCLH6A46YL6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6/action/storage_attestation","attest_author":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6/action/author_attestation","sign_citation":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6/action/citation_signature","submit_replication":"https://pith.science/pith/ZGJGZZVVEARLJ2NSCLH6A46YL6/action/replication_record"}},"created_at":"2026-07-05T01:17:04.779967+00:00","updated_at":"2026-07-05T01:17:04.779967+00:00"}