{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NGYXL7HI4QSDDTSZHGL3XGLHC6","short_pith_number":"pith:NGYXL7HI","schema_version":"1.0","canonical_sha256":"69b175fce8e42431ce593997bb99671798bed8e6669f4c25890a383bafa2f7d9","source":{"kind":"arxiv","id":"1901.10435","version":3},"attestation_state":"computed","paper":{"title":"A Deep Learning Framework for Assessing Physical Rehabilitation Exercises","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"A. Vakanski, M. Xian (University of Idaho, USA), Y. Liao","submitted_at":"2019-01-29T18:16:08Z","abstract_excerpt":"Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quant"},"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":"1901.10435","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-01-29T18:16:08Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"5e3d14775561d6b712b521c006be003a202a880c58cd6062b4ae3444551a8309","abstract_canon_sha256":"dc46534c93f88be6121bf77b28e01028c020461bec0a21de4a8f783451fb3aec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:35:09.390922Z","signature_b64":"bGSiZLZRsqOAiM+U+XrAqQUfaytYhG/FhseY0HbQKJjlIm0/JPViH10q4xVL7KKP967kKim1SzJzon1v39d0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"69b175fce8e42431ce593997bb99671798bed8e6669f4c25890a383bafa2f7d9","last_reissued_at":"2026-07-05T00:35:09.390454Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:35:09.390454Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Deep Learning Framework for Assessing Physical Rehabilitation Exercises","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"A. Vakanski, M. Xian (University of Idaho, USA), Y. Liao","submitted_at":"2019-01-29T18:16:08Z","abstract_excerpt":"Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quant"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.10435","kind":"arxiv","version":3},"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/1901.10435/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":"1901.10435","created_at":"2026-07-05T00:35:09.390512+00:00"},{"alias_kind":"arxiv_version","alias_value":"1901.10435v3","created_at":"2026-07-05T00:35:09.390512+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.10435","created_at":"2026-07-05T00:35:09.390512+00:00"},{"alias_kind":"pith_short_12","alias_value":"NGYXL7HI4QSD","created_at":"2026-07-05T00:35:09.390512+00:00"},{"alias_kind":"pith_short_16","alias_value":"NGYXL7HI4QSDDTSZ","created_at":"2026-07-05T00:35:09.390512+00:00"},{"alias_kind":"pith_short_8","alias_value":"NGYXL7HI","created_at":"2026-07-05T00:35:09.390512+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/NGYXL7HI4QSDDTSZHGL3XGLHC6","json":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6.json","graph_json":"https://pith.science/api/pith-number/NGYXL7HI4QSDDTSZHGL3XGLHC6/graph.json","events_json":"https://pith.science/api/pith-number/NGYXL7HI4QSDDTSZHGL3XGLHC6/events.json","paper":"https://pith.science/paper/NGYXL7HI"},"agent_actions":{"view_html":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6","download_json":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6.json","view_paper":"https://pith.science/paper/NGYXL7HI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1901.10435&json=true","fetch_graph":"https://pith.science/api/pith-number/NGYXL7HI4QSDDTSZHGL3XGLHC6/graph.json","fetch_events":"https://pith.science/api/pith-number/NGYXL7HI4QSDDTSZHGL3XGLHC6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6/action/storage_attestation","attest_author":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6/action/author_attestation","sign_citation":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6/action/citation_signature","submit_replication":"https://pith.science/pith/NGYXL7HI4QSDDTSZHGL3XGLHC6/action/replication_record"}},"created_at":"2026-07-05T00:35:09.390512+00:00","updated_at":"2026-07-05T00:35:09.390512+00:00"}