{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:N5BIMLZMZZIDUTRPIGXU6DMJNJ","short_pith_number":"pith:N5BIMLZM","schema_version":"1.0","canonical_sha256":"6f42862f2cce503a4e2f41af4f0d896a76375cd35e76d9450aab8e43d8801404","source":{"kind":"arxiv","id":"1610.07031","version":3},"attestation_state":"computed","paper":{"title":"Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dana Kuli\\'c, Terry Taewoong Um, Vahid Babakeshizadeh","submitted_at":"2016-10-22T10:46:01Z","abstract_excerpt":"The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifie"},"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":"1610.07031","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-22T10:46:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b3f7ec59677e3e41982969109e32b473afbda186a38f4144fc65cc150ef112bb","abstract_canon_sha256":"35d4c4d7e0af932e0c53fa85141086b1ef75e1f173932fb61c92ff5fb12deb8b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:47.312662Z","signature_b64":"vIsrRvXTSUHvXB5UL1+soiNyFFsuqBYgA9ZrlIVU37K5NkjWYda143XI9iIb0Yts8u+/O21z5PFauacR0mpDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f42862f2cce503a4e2f41af4f0d896a76375cd35e76d9450aab8e43d8801404","last_reissued_at":"2026-05-18T00:39:47.311849Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:47.311849Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exercise Motion Classification from Large-Scale Wearable Sensor Data Using Convolutional Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Dana Kuli\\'c, Terry Taewoong Um, Vahid Babakeshizadeh","submitted_at":"2016-10-22T10:46:01Z","abstract_excerpt":"The ability to accurately identify human activities is essential for developing automatic rehabilitation and sports training systems. In this paper, large-scale exercise motion data obtained from a forearm-worn wearable sensor are classified with a convolutional neural network (CNN). Time-series data consisting of accelerometer and orientation measurements are formatted as images, allowing the CNN to automatically extract discriminative features. A comparative study on the effects of image formatting and different CNN architectures is also presented. The best performing configuration classifie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.07031","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":"1610.07031","created_at":"2026-05-18T00:39:47.311986+00:00"},{"alias_kind":"arxiv_version","alias_value":"1610.07031v3","created_at":"2026-05-18T00:39:47.311986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.07031","created_at":"2026-05-18T00:39:47.311986+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5BIMLZMZZID","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5BIMLZMZZIDUTRP","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5BIMLZM","created_at":"2026-05-18T12:30:32.724797+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/N5BIMLZMZZIDUTRPIGXU6DMJNJ","json":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ.json","graph_json":"https://pith.science/api/pith-number/N5BIMLZMZZIDUTRPIGXU6DMJNJ/graph.json","events_json":"https://pith.science/api/pith-number/N5BIMLZMZZIDUTRPIGXU6DMJNJ/events.json","paper":"https://pith.science/paper/N5BIMLZM"},"agent_actions":{"view_html":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ","download_json":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ.json","view_paper":"https://pith.science/paper/N5BIMLZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1610.07031&json=true","fetch_graph":"https://pith.science/api/pith-number/N5BIMLZMZZIDUTRPIGXU6DMJNJ/graph.json","fetch_events":"https://pith.science/api/pith-number/N5BIMLZMZZIDUTRPIGXU6DMJNJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ/action/storage_attestation","attest_author":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ/action/author_attestation","sign_citation":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ/action/citation_signature","submit_replication":"https://pith.science/pith/N5BIMLZMZZIDUTRPIGXU6DMJNJ/action/replication_record"}},"created_at":"2026-05-18T00:39:47.311986+00:00","updated_at":"2026-05-18T00:39:47.311986+00:00"}