{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:YJQVZB2RY63YAESQWLAR77XQFG","short_pith_number":"pith:YJQVZB2R","schema_version":"1.0","canonical_sha256":"c2615c8751c7b7801250b2c11ffef02980f846e7ee3616283ca6f34aeba5f667","source":{"kind":"arxiv","id":"1904.10829","version":1},"attestation_state":"computed","paper":{"title":"Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bernd Bischl, Franz MJ Pfister, Janek Thomas, Jann Goschenhofer, Kamer Ali Yuksel, Urban Fietzek","submitted_at":"2019-04-24T14:05:34Z","abstract_excerpt":"One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device. We find that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task. Furthermore, our results suggest that treating this problem as a regression instead of an ord"},"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":"1904.10829","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2019-04-24T14:05:34Z","cross_cats_sorted":["stat.AP","stat.ML"],"title_canon_sha256":"aeb7b0b97ecfc56dc80de245ddb03c6cd1be30ed47b1862d474b99495c6375bc","abstract_canon_sha256":"280879a89a41bf3ab29bce875cddb7f9de3f838e8314c43e04afe28f8ad73d68"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:48.025985Z","signature_b64":"ve2NvDQBOasBe67iMXkqmvhNxvD3+Z19uXv5ygEri0RthtATXYJUhKZ0oIOt4vh36Tm0p5djref26SQsHE12Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2615c8751c7b7801250b2c11ffef02980f846e7ee3616283ca6f34aeba5f667","last_reissued_at":"2026-05-17T23:47:48.025393Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:48.025393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["stat.AP","stat.ML"],"primary_cat":"cs.LG","authors_text":"Bernd Bischl, Franz MJ Pfister, Janek Thomas, Jann Goschenhofer, Kamer Ali Yuksel, Urban Fietzek","submitted_at":"2019-04-24T14:05:34Z","abstract_excerpt":"One major challenge in the medication of Parkinson's disease is that the severity of the disease, reflected in the patients' motor state, cannot be measured using accessible biomarkers. Therefore, we develop and examine a variety of statistical models to detect the motor state of such patients based on sensor data from a wearable device. We find that deep learning models consistently outperform a classical machine learning model applied on hand-crafted features in this time series classification task. Furthermore, our results suggest that treating this problem as a regression instead of an ord"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.10829","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":"1904.10829","created_at":"2026-05-17T23:47:48.025486+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.10829v1","created_at":"2026-05-17T23:47:48.025486+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.10829","created_at":"2026-05-17T23:47:48.025486+00:00"},{"alias_kind":"pith_short_12","alias_value":"YJQVZB2RY63Y","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_16","alias_value":"YJQVZB2RY63YAESQ","created_at":"2026-05-18T12:33:33.725879+00:00"},{"alias_kind":"pith_short_8","alias_value":"YJQVZB2R","created_at":"2026-05-18T12:33:33.725879+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/YJQVZB2RY63YAESQWLAR77XQFG","json":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG.json","graph_json":"https://pith.science/api/pith-number/YJQVZB2RY63YAESQWLAR77XQFG/graph.json","events_json":"https://pith.science/api/pith-number/YJQVZB2RY63YAESQWLAR77XQFG/events.json","paper":"https://pith.science/paper/YJQVZB2R"},"agent_actions":{"view_html":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG","download_json":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG.json","view_paper":"https://pith.science/paper/YJQVZB2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.10829&json=true","fetch_graph":"https://pith.science/api/pith-number/YJQVZB2RY63YAESQWLAR77XQFG/graph.json","fetch_events":"https://pith.science/api/pith-number/YJQVZB2RY63YAESQWLAR77XQFG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG/action/storage_attestation","attest_author":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG/action/author_attestation","sign_citation":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG/action/citation_signature","submit_replication":"https://pith.science/pith/YJQVZB2RY63YAESQWLAR77XQFG/action/replication_record"}},"created_at":"2026-05-17T23:47:48.025486+00:00","updated_at":"2026-05-17T23:47:48.025486+00:00"}