{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DJKR5UC5WSF5SSDYG3IUWZT2LT","short_pith_number":"pith:DJKR5UC5","schema_version":"1.0","canonical_sha256":"1a551ed05db48bd9487836d14b667a5cd431d86ddc7829ed4f79378b5c755ad6","source":{"kind":"arxiv","id":"1908.08919","version":3},"attestation_state":"computed","paper":{"title":"In-bed Pressure-based Pose Estimation using Image Space Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Ali Etemad, Saeed Ghorbani, Vandad Davoodnia","submitted_at":"2019-08-21T01:52:54Z","abstract_excerpt":"Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data "},"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":"1908.08919","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-21T01:52:54Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"eead5532384f711a569ad7b1cede39edc64011427728a567c5b0730d0188f380","abstract_canon_sha256":"34f138114f79f20933bb4713e63a7af0c9a26b689357c30086e7ce4b01d7a870"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:41:26.742091Z","signature_b64":"tYUltuPjZs2KlMusiB5/FwhsNGXNU46vgZHclaetuG+ldNMhJAFeKmB5sh1Ntt7KwariDCNraQI9G8LVL29uCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a551ed05db48bd9487836d14b667a5cd431d86ddc7829ed4f79378b5c755ad6","last_reissued_at":"2026-07-05T02:41:26.741698Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:41:26.741698Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In-bed Pressure-based Pose Estimation using Image Space Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Ali Etemad, Saeed Ghorbani, Vandad Davoodnia","submitted_at":"2019-08-21T01:52:54Z","abstract_excerpt":"Recent advances in deep pose estimation models have proven to be effective in a wide range of applications such as health monitoring, sports, animations, and robotics. However, pose estimation models fail to generalize when facing images acquired from in-bed pressure sensing systems. In this paper, we address this challenge by presenting a novel end-to-end framework capable of accurately locating body parts from vague pressure data. Our method exploits the idea of equipping an off-the-shelf pose estimator with a deep trainable neural network, which pre-processes and prepares the pressure data "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.08919","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/1908.08919/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":"1908.08919","created_at":"2026-07-05T02:41:26.741753+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.08919v3","created_at":"2026-07-05T02:41:26.741753+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.08919","created_at":"2026-07-05T02:41:26.741753+00:00"},{"alias_kind":"pith_short_12","alias_value":"DJKR5UC5WSF5","created_at":"2026-07-05T02:41:26.741753+00:00"},{"alias_kind":"pith_short_16","alias_value":"DJKR5UC5WSF5SSDY","created_at":"2026-07-05T02:41:26.741753+00:00"},{"alias_kind":"pith_short_8","alias_value":"DJKR5UC5","created_at":"2026-07-05T02:41:26.741753+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.08919","citing_title":"In-bed Pressure-based Pose Estimation using Image Space Representation Learning","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT","json":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT.json","graph_json":"https://pith.science/api/pith-number/DJKR5UC5WSF5SSDYG3IUWZT2LT/graph.json","events_json":"https://pith.science/api/pith-number/DJKR5UC5WSF5SSDYG3IUWZT2LT/events.json","paper":"https://pith.science/paper/DJKR5UC5"},"agent_actions":{"view_html":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT","download_json":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT.json","view_paper":"https://pith.science/paper/DJKR5UC5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.08919&json=true","fetch_graph":"https://pith.science/api/pith-number/DJKR5UC5WSF5SSDYG3IUWZT2LT/graph.json","fetch_events":"https://pith.science/api/pith-number/DJKR5UC5WSF5SSDYG3IUWZT2LT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT/action/storage_attestation","attest_author":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT/action/author_attestation","sign_citation":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT/action/citation_signature","submit_replication":"https://pith.science/pith/DJKR5UC5WSF5SSDYG3IUWZT2LT/action/replication_record"}},"created_at":"2026-07-05T02:41:26.741753+00:00","updated_at":"2026-07-05T02:41:26.741753+00:00"}