{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:WFAPZUFCBFIYBEGWO67X7KBRFJ","short_pith_number":"pith:WFAPZUFC","schema_version":"1.0","canonical_sha256":"b140fcd0a209518090d677bf7fa8312a63cd62527c95e99d7e9804012b447ef6","source":{"kind":"arxiv","id":"1609.03323","version":3},"attestation_state":"computed","paper":{"title":"Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bjoern M. Eskofier, Cristian F. Pasluosta, Jochen Klucken, Julius Hannink, Karl-G\\\"unter Ga{\\ss}mann, Thomas Kautz","submitted_at":"2016-09-12T09:33:57Z","abstract_excerpt":"Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of 8 spa"},"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":"1609.03323","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-09-12T09:33:57Z","cross_cats_sorted":[],"title_canon_sha256":"dcb280c639599bca53a75bee5923d1e82c63d28fef5dbc6eea5f63dc674efdc0","abstract_canon_sha256":"6c647ab7e4598c41954dc99e3f5eb349fb3cb363d99ea922ac935932c7a48258"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:55.138436Z","signature_b64":"0vr9jLev2x1DUswadJs09NyR9JeL/PlmSGfU+L5tQd0op2ste4a8eNazKImOgafnyDjy5NNe7V6Iql1X8XMyAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b140fcd0a209518090d677bf7fa8312a63cd62527c95e99d7e9804012b447ef6","last_reissued_at":"2026-05-18T00:52:55.137898Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:55.137898Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sensor-based Gait Parameter Extraction with Deep Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bjoern M. Eskofier, Cristian F. Pasluosta, Jochen Klucken, Julius Hannink, Karl-G\\\"unter Ga{\\ss}mann, Thomas Kautz","submitted_at":"2016-09-12T09:33:57Z","abstract_excerpt":"Measurement of stride-related, biomechanical parameters is the common rationale for objective gait impairment scoring. State-of-the-art double integration approaches to extract these parameters from inertial sensor data are, however, limited in their clinical applicability due to the underlying assumptions. To overcome this, we present a method to translate the abstract information provided by wearable sensors to context-related expert features based on deep convolutional neural networks. Regarding mobile gait analysis, this enables integration-free and data-driven extraction of a set of 8 spa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.03323","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":""},"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":"1609.03323","created_at":"2026-05-18T00:52:55.137969+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.03323v3","created_at":"2026-05-18T00:52:55.137969+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.03323","created_at":"2026-05-18T00:52:55.137969+00:00"},{"alias_kind":"pith_short_12","alias_value":"WFAPZUFCBFIY","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"WFAPZUFCBFIYBEGW","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"WFAPZUFC","created_at":"2026-05-18T12:30:48.956258+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/WFAPZUFCBFIYBEGWO67X7KBRFJ","json":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ.json","graph_json":"https://pith.science/api/pith-number/WFAPZUFCBFIYBEGWO67X7KBRFJ/graph.json","events_json":"https://pith.science/api/pith-number/WFAPZUFCBFIYBEGWO67X7KBRFJ/events.json","paper":"https://pith.science/paper/WFAPZUFC"},"agent_actions":{"view_html":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ","download_json":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ.json","view_paper":"https://pith.science/paper/WFAPZUFC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.03323&json=true","fetch_graph":"https://pith.science/api/pith-number/WFAPZUFCBFIYBEGWO67X7KBRFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/WFAPZUFCBFIYBEGWO67X7KBRFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ/action/storage_attestation","attest_author":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ/action/author_attestation","sign_citation":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ/action/citation_signature","submit_replication":"https://pith.science/pith/WFAPZUFCBFIYBEGWO67X7KBRFJ/action/replication_record"}},"created_at":"2026-05-18T00:52:55.137969+00:00","updated_at":"2026-05-18T00:52:55.137969+00:00"}