{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:N5HPB5CNHTKDBND2IIHX4GIJIT","short_pith_number":"pith:N5HPB5CN","schema_version":"1.0","canonical_sha256":"6f4ef0f44d3cd430b47a420f7e190944d49bcb0a2ac91b8ae8cf2ec3f16d09d9","source":{"kind":"arxiv","id":"2103.05267","version":1},"attestation_state":"computed","paper":{"title":"Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Andy Zhou, Jan Rabaey, Rikky Muller","submitted_at":"2021-03-09T07:31:00Z","abstract_excerpt":"Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change. One method to address this is dual-stage classification, in which the limb position is first determined using additional sensors to select between multiple position-specific gesture classifiers. While improving performance, this also increases model complexity and memory footprint, making a dual-stage classifier difficult to implement in a wearable device with limited res"},"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":"2103.05267","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2021-03-09T07:31:00Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"f8e3048ac4be310dc6bd9e2e2abbcf2010076cc77d90fd71a423596bda1646bb","abstract_canon_sha256":"4f70a466abad6fd8955f1bd746956ad1417dc0de936ce3605584a83de4c42a8f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:21:24.348221Z","signature_b64":"OIk2Y/PY5FBWcI74QhdLfy5r8XHGk7QAhXPP0hyHgYgvlfFeylS6+lDr/uHJ/ne4A4wfYrortzxfNA4+aoQfAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f4ef0f44d3cd430b47a420f7e190944d49bcb0a2ac91b8ae8cf2ec3f16d09d9","last_reissued_at":"2026-07-05T02:21:24.347492Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:21:24.347492Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Memory-Efficient, Limb Position-Aware Hand Gesture Recognition using Hyperdimensional Computing","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Andy Zhou, Jan Rabaey, Rikky Muller","submitted_at":"2021-03-09T07:31:00Z","abstract_excerpt":"Electromyogram (EMG) pattern recognition can be used to classify hand gestures and movements for human-machine interface and prosthetics applications, but it often faces reliability issues resulting from limb position change. One method to address this is dual-stage classification, in which the limb position is first determined using additional sensors to select between multiple position-specific gesture classifiers. While improving performance, this also increases model complexity and memory footprint, making a dual-stage classifier difficult to implement in a wearable device with limited res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.05267","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.05267/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":"2103.05267","created_at":"2026-07-05T02:21:24.347553+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.05267v1","created_at":"2026-07-05T02:21:24.347553+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.05267","created_at":"2026-07-05T02:21:24.347553+00:00"},{"alias_kind":"pith_short_12","alias_value":"N5HPB5CNHTKD","created_at":"2026-07-05T02:21:24.347553+00:00"},{"alias_kind":"pith_short_16","alias_value":"N5HPB5CNHTKDBND2","created_at":"2026-07-05T02:21:24.347553+00:00"},{"alias_kind":"pith_short_8","alias_value":"N5HPB5CN","created_at":"2026-07-05T02:21:24.347553+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/N5HPB5CNHTKDBND2IIHX4GIJIT","json":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT.json","graph_json":"https://pith.science/api/pith-number/N5HPB5CNHTKDBND2IIHX4GIJIT/graph.json","events_json":"https://pith.science/api/pith-number/N5HPB5CNHTKDBND2IIHX4GIJIT/events.json","paper":"https://pith.science/paper/N5HPB5CN"},"agent_actions":{"view_html":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT","download_json":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT.json","view_paper":"https://pith.science/paper/N5HPB5CN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.05267&json=true","fetch_graph":"https://pith.science/api/pith-number/N5HPB5CNHTKDBND2IIHX4GIJIT/graph.json","fetch_events":"https://pith.science/api/pith-number/N5HPB5CNHTKDBND2IIHX4GIJIT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT/action/storage_attestation","attest_author":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT/action/author_attestation","sign_citation":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT/action/citation_signature","submit_replication":"https://pith.science/pith/N5HPB5CNHTKDBND2IIHX4GIJIT/action/replication_record"}},"created_at":"2026-07-05T02:21:24.347553+00:00","updated_at":"2026-07-05T02:21:24.347553+00:00"}