{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LL7DBKOUQXUVZGZTS3KAFBM6CO","short_pith_number":"pith:LL7DBKOU","schema_version":"1.0","canonical_sha256":"5afe30a9d485e95c9b3396d402859e138ab4d59d46b0c4937d40c17d7d037139","source":{"kind":"arxiv","id":"1808.04760","version":1},"attestation_state":"computed","paper":{"title":"Parallel Statistical and Machine Learning Methods for Estimation of Physical Load","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Gang Peng, Nikita Gordienko, Oleg Alienin, Oleksandr Rokovyi, Sergii Stirenko, Wei Zeng, Yuri Gordienko","submitted_at":"2018-08-14T15:47:32Z","abstract_excerpt":"Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions. The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution c"},"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":"1808.04760","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-08-14T15:47:32Z","cross_cats_sorted":["cs.HC","stat.ML"],"title_canon_sha256":"918a6483e51fdc2a474b300e8d816f76cccaee168bb3bbb9a830462772789bda","abstract_canon_sha256":"7ced4e2ad7ef921592fc0cb49fe957666bc7cbae260d2c4e8f25cccff5736741"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:47.685907Z","signature_b64":"+fFMyLr4CGReccYhnrDlrG8Ls6cbr2WQMhJ9LRagQNoqNkdWxtrFKu8x26WbnPQ0hkz6F8xSG4P6AbwNgYTXCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5afe30a9d485e95c9b3396d402859e138ab4d59d46b0c4937d40c17d7d037139","last_reissued_at":"2026-05-17T23:58:47.685451Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:47.685451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Parallel Statistical and Machine Learning Methods for Estimation of Physical Load","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.HC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Gang Peng, Nikita Gordienko, Oleg Alienin, Oleksandr Rokovyi, Sergii Stirenko, Wei Zeng, Yuri Gordienko","submitted_at":"2018-08-14T15:47:32Z","abstract_excerpt":"Several statistical and machine learning methods are proposed to estimate the type and intensity of physical load and accumulated fatigue . They are based on the statistical analysis of accumulated and moving window data subsets with construction of a kurtosis-skewness diagram. This approach was applied to the data gathered by the wearable heart monitor for various types and levels of physical activities, and for people with various physical conditions. The different levels of physical activities, loads, and fitness can be distinguished from the kurtosis-skewness diagram, and their evolution c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.04760","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":""},"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":"1808.04760","created_at":"2026-05-17T23:58:47.685528+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.04760v1","created_at":"2026-05-17T23:58:47.685528+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.04760","created_at":"2026-05-17T23:58:47.685528+00:00"},{"alias_kind":"pith_short_12","alias_value":"LL7DBKOUQXUV","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LL7DBKOUQXUVZGZT","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LL7DBKOU","created_at":"2026-05-18T12:32:37.024351+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/LL7DBKOUQXUVZGZTS3KAFBM6CO","json":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO.json","graph_json":"https://pith.science/api/pith-number/LL7DBKOUQXUVZGZTS3KAFBM6CO/graph.json","events_json":"https://pith.science/api/pith-number/LL7DBKOUQXUVZGZTS3KAFBM6CO/events.json","paper":"https://pith.science/paper/LL7DBKOU"},"agent_actions":{"view_html":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO","download_json":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO.json","view_paper":"https://pith.science/paper/LL7DBKOU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.04760&json=true","fetch_graph":"https://pith.science/api/pith-number/LL7DBKOUQXUVZGZTS3KAFBM6CO/graph.json","fetch_events":"https://pith.science/api/pith-number/LL7DBKOUQXUVZGZTS3KAFBM6CO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO/action/storage_attestation","attest_author":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO/action/author_attestation","sign_citation":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO/action/citation_signature","submit_replication":"https://pith.science/pith/LL7DBKOUQXUVZGZTS3KAFBM6CO/action/replication_record"}},"created_at":"2026-05-17T23:58:47.685528+00:00","updated_at":"2026-05-17T23:58:47.685528+00:00"}