{"paper":{"title":"Lightweight Cross-Device Sleep Tracking on the WeBe Wearable Platform","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A simple pipeline on raw accelerometer signals tracks sleep across wearables with 27 to 42 minute error","cross_cats":[],"primary_cat":"cs.ET","authors_text":"Ehsan Kourkchi, Houman Homayoun, Krishi Prashant Shah, Setareh Rafatirad, Wei Shao, Zequan Liang","submitted_at":"2026-05-15T08:15:15Z","abstract_excerpt":"Wearable devices are widely used for continuous health monitoring, yet reliable sleep tracking on emerging platforms remains underexplored due to reliance on proprietary algorithms and device-specific activity representations. We present a lightweight and reproducible sleep tracking pipeline that operates directly on raw accelerometer signals. The method converts data into epoch-level activity features, applies temporal smoothing and normalized scoring, and performs sleep/wake classification using a globally calibrated threshold. We calibrate the model on the Multilevel Monitoring of Activity "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The lightweight pipeline achieves a mean absolute error of 41.6 minutes in Total Sleep Time on MMASH and 27.4 minutes on real-world WeBe data from three participants, demonstrating accurate and generalizable sleep tracking using a simple and reproducible pipeline.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a single globally calibrated threshold applied after normalized scoring on epoch-level activity features will generalize across different wearable hardware, participant populations, and real-world conditions without device-specific retraining or post-hoc adjustments.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Lightweight pipeline converts raw accelerometer data to epoch features, applies smoothing and normalized scoring, then uses a globally calibrated threshold for sleep/wake classification, reporting TST errors of 27-42 minutes on MMASH and WeBe data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A simple pipeline on raw accelerometer signals tracks sleep across wearables with 27 to 42 minute error","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"0ba7e9efe91857bbc0d9804207df1dbd11df9e63c03b417c0fe991f34b9f4e17"},"source":{"id":"2605.15719","kind":"arxiv","version":1},"verdict":{"id":"2ef3501f-46bc-4b6a-825e-7720ce5c2e06","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:15:08.890349Z","strongest_claim":"The lightweight pipeline achieves a mean absolute error of 41.6 minutes in Total Sleep Time on MMASH and 27.4 minutes on real-world WeBe data from three participants, demonstrating accurate and generalizable sleep tracking using a simple and reproducible pipeline.","one_line_summary":"Lightweight pipeline converts raw accelerometer data to epoch features, applies smoothing and normalized scoring, then uses a globally calibrated threshold for sleep/wake classification, reporting TST errors of 27-42 minutes on MMASH and WeBe data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a single globally calibrated threshold applied after normalized scoring on epoch-level activity features will generalize across different wearable hardware, participant populations, and real-world conditions without device-specific retraining or post-hoc adjustments.","pith_extraction_headline":"A simple pipeline on raw accelerometer signals tracks sleep across wearables with 27 to 42 minute error"},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15719/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:25.322034Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T18:31:18.796811Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:21:42.517339Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:21:56.009510Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"aafab9c14a306a32398557b65aaba9b58282c5dfa089ac9fec3282ee4b69fe9a"},"references":{"count":19,"sample":[{"doi":"","year":2006,"title":"Christine Acebo and Monique K LeBourgeois. 2006. Actigraphy.Respiratory care clinics of North America12, 1 (2006), 23–30","work_id":"6ca1677a-4896-4aaf-a2a6-ad74b25b9431","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2026,"title":"Ametris. 2026. ActiGraph LEAP | Ametris Wearable Devices. https://ametris. com/actigraph-leap. [Online; accessed May 2026]","work_id":"f52220e7-a36d-427b-a1a7-a5e9e93604d8","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2007,"title":"Greg Atkinson and Damien Davenne. 2007. Relationships between sleep, physical activity and human health.Physiology & behavior90, 2-3 (2007), 229–235","work_id":"6f6cd747-dcb6-4726-8652-9c9736c62d4f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1992,"title":"Roger J Cole, Daniel F Kripke, William Gruen, Daniel J Mullaney, and J Christian Gillin. 1992. Automatic sleep/wake identification from wrist activity.Sleep15, 5 (1992), 461–469","work_id":"d825c558-5520-493e-80b3-9a66aa4795e7","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Massimiliano De Zambotti, Nicola Cellini, Aimee Goldstone, Ian M Colrain, and Fiona C Baker. 2019. Wearable sleep technology in clinical and research settings. Medicine and science in sports and exerc","work_id":"1262a398-79fd-4b57-92d0-027a06286572","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":19,"snapshot_sha256":"03812347420215e1033f099eb7ebca947c6f40381a8b5930da99c9966777af7a","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"}