{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:UOBGGWMP6FIOHIRCBOENVLRHQ2","short_pith_number":"pith:UOBGGWMP","schema_version":"1.0","canonical_sha256":"a38263598ff150e3a2220b88daae27869a73418daa43db1b7c6443c9737cad68","source":{"kind":"arxiv","id":"2406.11164","version":1},"attestation_state":"computed","paper":{"title":"Optimum signal duration for Human Activity Recognition based on Deep Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Arian Shajari, Darius Nahavandi, Farhad Nazari, Navid Mohajer","submitted_at":"2024-06-17T03:08:48Z","abstract_excerpt":"Human Activity Recognition (HAR) stands as a pivotal technique within pattern recognition, dedicated to deciphering human movements and actions utilizing one or multiple sensory inputs. Its significance extends across diverse applications, encompassing monitoring, security protocols, and the development of human-in-the-loop technologies. However, prevailing studies in HAR often overlook the integration of human-centered devices, wherein distinct parameters and criteria hold varying degrees of importance compared to other applications. Notably, within this realm, curtailing the sensor observati"},"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":"2406.11164","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2024-06-17T03:08:48Z","cross_cats_sorted":[],"title_canon_sha256":"2a62a254960b1445dec5145ea86a7cb0cac48942dd1526560583f9e1dff766fc","abstract_canon_sha256":"371b8ea87c63ffc14aa838511bb12f7225ec86ce094f6234ec69a071ddf7f7a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:49.684424Z","signature_b64":"OAXdlnlNm3lm0cupBVI92u9Zpi6dOhCuTqrc4TC+iFQQArAyfFgK5MosdlvzRmr2ocK/qJQVnyiOAjtQuHA2AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a38263598ff150e3a2220b88daae27869a73418daa43db1b7c6443c9737cad68","last_reissued_at":"2026-07-05T08:32:49.684003Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:49.684003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimum signal duration for Human Activity Recognition based on Deep Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Arian Shajari, Darius Nahavandi, Farhad Nazari, Navid Mohajer","submitted_at":"2024-06-17T03:08:48Z","abstract_excerpt":"Human Activity Recognition (HAR) stands as a pivotal technique within pattern recognition, dedicated to deciphering human movements and actions utilizing one or multiple sensory inputs. Its significance extends across diverse applications, encompassing monitoring, security protocols, and the development of human-in-the-loop technologies. However, prevailing studies in HAR often overlook the integration of human-centered devices, wherein distinct parameters and criteria hold varying degrees of importance compared to other applications. Notably, within this realm, curtailing the sensor observati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.11164","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/2406.11164/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":"2406.11164","created_at":"2026-07-05T08:32:49.684058+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.11164v1","created_at":"2026-07-05T08:32:49.684058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.11164","created_at":"2026-07-05T08:32:49.684058+00:00"},{"alias_kind":"pith_short_12","alias_value":"UOBGGWMP6FIO","created_at":"2026-07-05T08:32:49.684058+00:00"},{"alias_kind":"pith_short_16","alias_value":"UOBGGWMP6FIOHIRC","created_at":"2026-07-05T08:32:49.684058+00:00"},{"alias_kind":"pith_short_8","alias_value":"UOBGGWMP","created_at":"2026-07-05T08:32:49.684058+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/UOBGGWMP6FIOHIRCBOENVLRHQ2","json":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2.json","graph_json":"https://pith.science/api/pith-number/UOBGGWMP6FIOHIRCBOENVLRHQ2/graph.json","events_json":"https://pith.science/api/pith-number/UOBGGWMP6FIOHIRCBOENVLRHQ2/events.json","paper":"https://pith.science/paper/UOBGGWMP"},"agent_actions":{"view_html":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2","download_json":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2.json","view_paper":"https://pith.science/paper/UOBGGWMP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.11164&json=true","fetch_graph":"https://pith.science/api/pith-number/UOBGGWMP6FIOHIRCBOENVLRHQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/UOBGGWMP6FIOHIRCBOENVLRHQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2/action/storage_attestation","attest_author":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2/action/author_attestation","sign_citation":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2/action/citation_signature","submit_replication":"https://pith.science/pith/UOBGGWMP6FIOHIRCBOENVLRHQ2/action/replication_record"}},"created_at":"2026-07-05T08:32:49.684058+00:00","updated_at":"2026-07-05T08:32:49.684058+00:00"}