{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:KLCV4ZASCBLPHKC5ORAJ4TSDVB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"f6724c966549fd51bbb608b5cdf75d26c3e86f07472f00fd8417149fb97f083c","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-12T15:55:06Z","title_canon_sha256":"b537d86e807e784994d805ee801d6c3c116d67b53a99335f77d039ca630cc6bc"},"schema_version":"1.0","source":{"id":"1610.03761","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1610.03761","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"arxiv_version","alias_value":"1610.03761v3","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1610.03761","created_at":"2026-05-18T00:24:22Z"},{"alias_kind":"pith_short_12","alias_value":"KLCV4ZASCBLP","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_16","alias_value":"KLCV4ZASCBLPHKC5","created_at":"2026-05-18T12:30:25Z"},{"alias_kind":"pith_short_8","alias_value":"KLCV4ZAS","created_at":"2026-05-18T12:30:25Z"}],"graph_snapshots":[{"event_id":"sha256:a5b501a7e8d20b631b7c5519484b071bcef982532d04b20c5b5db30cf689dcfc","target":"graph","created_at":"2026-05-18T00:24:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A fall is an abnormal activity that occurs rarely, so it is hard to collect real data for falls. It is, therefore, difficult to use supervised learning methods to automatically detect falls. Another challenge in using machine learning methods to automatically detect falls is the choice of engineered features. In this paper, we propose to use an ensemble of autoencoders to extract features from different channels of wearable sensor data trained only on normal activities. We show that the traditional approach of choosing a threshold as the maximum of the reconstruction error on the training norm","authors_text":"Babak Taati, Shehroz S. Khan","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-12T15:55:06Z","title":"Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1610.03761","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:63c37b6808878c98ea9c9911a4dc882150dd99e263888a707c8b710f2d6e82f6","target":"record","created_at":"2026-05-18T00:24:22Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"f6724c966549fd51bbb608b5cdf75d26c3e86f07472f00fd8417149fb97f083c","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-10-12T15:55:06Z","title_canon_sha256":"b537d86e807e784994d805ee801d6c3c116d67b53a99335f77d039ca630cc6bc"},"schema_version":"1.0","source":{"id":"1610.03761","kind":"arxiv","version":3}},"canonical_sha256":"52c55e64121056f3a85d74409e4e43a842c8eaa14cbc62f4bce3f7d4c17c643a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"52c55e64121056f3a85d74409e4e43a842c8eaa14cbc62f4bce3f7d4c17c643a","first_computed_at":"2026-05-18T00:24:22.773453Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:22.773453Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LwEXode3PL1fe8L7Ba/XgYmyh40WJDRH9ay9+wPRv+wVY69hDpUPXj0dVs+vxZ7DkEGC4WmQIraexRF67ArPDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:22.773986Z","signed_message":"canonical_sha256_bytes"},"source_id":"1610.03761","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:63c37b6808878c98ea9c9911a4dc882150dd99e263888a707c8b710f2d6e82f6","sha256:a5b501a7e8d20b631b7c5519484b071bcef982532d04b20c5b5db30cf689dcfc"],"state_sha256":"f89ecf3732ca0fd5a925a24a2189a85a2cac297ee25e748d3c37244abcd5ff6d"}