{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:U6XUVDCGRA5CA4F5UFC7MUJ2V5","short_pith_number":"pith:U6XUVDCG","schema_version":"1.0","canonical_sha256":"a7af4a8c46883a2070bda145f6513aaf48b153761220f3ece055b58a9a966ba3","source":{"kind":"arxiv","id":"1708.06026","version":1},"attestation_state":"computed","paper":{"title":"DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.med-ph"],"primary_cat":"cs.HC","authors_text":"Nadia Bianchi-Berthouze, Simon J. Julier, Youngjun Cho","submitted_at":"2017-08-20T21:36:30Z","abstract_excerpt":"We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating hand-crafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexi"},"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":"1708.06026","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2017-08-20T21:36:30Z","cross_cats_sorted":["cs.CV","physics.med-ph"],"title_canon_sha256":"926c3aa311fe8066de63a5718b753402c0164f178e1e4e8502e974582e41df23","abstract_canon_sha256":"21a2975957b3859dbfd5d027e56f9ff6053108f7ae3aced2fd38c91e2309a52e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:20:16.854732Z","signature_b64":"fC8nwL02SGvA6vTiyhnLH6tHRFiZwwXFXK0EuyJv3ZTnXq7Sk9zDqdB91Sl5wfNH62U6ZwXAAfOcoNOzgS6mAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7af4a8c46883a2070bda145f6513aaf48b153761220f3ece055b58a9a966ba3","last_reissued_at":"2026-05-18T00:20:16.854250Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:20:16.854250Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DeepBreath: Deep Learning of Breathing Patterns for Automatic Stress Recognition using Low-Cost Thermal Imaging in Unconstrained Settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","physics.med-ph"],"primary_cat":"cs.HC","authors_text":"Nadia Bianchi-Berthouze, Simon J. Julier, Youngjun Cho","submitted_at":"2017-08-20T21:36:30Z","abstract_excerpt":"We propose DeepBreath, a deep learning model which automatically recognises people's psychological stress level (mental overload) from their breathing patterns. Using a low cost thermal camera, we track a person's breathing patterns as temperature changes around his/her nostril. The paper's technical contribution is threefold. First of all, instead of creating hand-crafted features to capture aspects of the breathing patterns, we transform the uni-dimensional breathing signals into two dimensional respiration variability spectrogram (RVS) sequences. The spectrograms easily capture the complexi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.06026","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":"1708.06026","created_at":"2026-05-18T00:20:16.854327+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.06026v1","created_at":"2026-05-18T00:20:16.854327+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.06026","created_at":"2026-05-18T00:20:16.854327+00:00"},{"alias_kind":"pith_short_12","alias_value":"U6XUVDCGRA5C","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"U6XUVDCGRA5CA4F5","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"U6XUVDCG","created_at":"2026-05-18T12:31:46.661854+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/U6XUVDCGRA5CA4F5UFC7MUJ2V5","json":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5.json","graph_json":"https://pith.science/api/pith-number/U6XUVDCGRA5CA4F5UFC7MUJ2V5/graph.json","events_json":"https://pith.science/api/pith-number/U6XUVDCGRA5CA4F5UFC7MUJ2V5/events.json","paper":"https://pith.science/paper/U6XUVDCG"},"agent_actions":{"view_html":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5","download_json":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5.json","view_paper":"https://pith.science/paper/U6XUVDCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.06026&json=true","fetch_graph":"https://pith.science/api/pith-number/U6XUVDCGRA5CA4F5UFC7MUJ2V5/graph.json","fetch_events":"https://pith.science/api/pith-number/U6XUVDCGRA5CA4F5UFC7MUJ2V5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5/action/storage_attestation","attest_author":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5/action/author_attestation","sign_citation":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5/action/citation_signature","submit_replication":"https://pith.science/pith/U6XUVDCGRA5CA4F5UFC7MUJ2V5/action/replication_record"}},"created_at":"2026-05-18T00:20:16.854327+00:00","updated_at":"2026-05-18T00:20:16.854327+00:00"}