{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:54:03Z","timestamp":1781546043724,"version":"3.54.5"},"reference-count":81,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T00:00:00Z","timestamp":1781481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2026,6,15]]},"abstract":"<jats:p>Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiogram (BCG), and Bodyseismography (BSG), is fundamental for cardiovascular monitoring but is often hindered by artifacts and signal variability. Conventional algorithms are typically engineered with expert knowledge for a single signal modality, limiting their generalizability. Conversely, deep learning-based methods often lack interpretability, limiting transparency for expert verification and hindering expert-computer interaction. To address these limitations, we introduce Peak-Detector, a novel framework that leverages instruction-tuned Large Language Models (LLMs) for robust, cross-modal, and explainable peak detection. A core innovation of our framework is a \u201cpeak-representation\u201d technique that transforms time-series data into a condensed format, preserving critical event information while significantly reducing signal length. This representation provides a crucial inductive bias, guiding the LLM to reason over physiologically meaningful events rather than raw, noisy data. The model is optimized through a two-stage process: supervised fine-tuning (SFT) followed by reinforcement learning (RL) with a multi-objective reward function. The model's self-explanation capabilities are cultivated by fine-tuning on a custom-built Peak-Explanation dataset. Across four modalities\u2014ECG, PPG, BCG, and BSG\u2014spanning seven datasets (six public benchmarks plus one real-world cohort), Peak-Detector demonstrates strong cross-modal performance, achieving best or tied-best detection under clinically relevant temporal tolerance. Beyond accuracy, the generated rationales surface failure modes and support verification and error analysis. Together, these results indicate a transparent and generalizable framework for trustworthy peak analysis and cardiovascular metric extraction.<\/jats:p>","DOI":"10.1145\/3810224","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T17:06:41Z","timestamp":1781543201000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Signal"],"prefix":"10.1145","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5129-0107","authenticated-orcid":false,"given":"Jiahui","family":"Li","sequence":"first","affiliation":[{"name":"School of Computing, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5555-3778","authenticated-orcid":false,"given":"Yida","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7051-7421","authenticated-orcid":false,"given":"Zixuan","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0904-4114","authenticated-orcid":false,"given":"Jiayu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, The University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5601-4465","authenticated-orcid":false,"given":"Yingjian","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2247-8042","authenticated-orcid":false,"given":"Yin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, Yixing People's Hospital, Yixing, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4463-9246","authenticated-orcid":false,"given":"Nishan","family":"Dong","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, Yixing People's Hospital, Yixing, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7547-6378","authenticated-orcid":false,"given":"Junjie","family":"Lu","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, Yixing People's Hospital, Yixing, Jiangsu Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8152-9170","authenticated-orcid":false,"given":"Younghoon","family":"Kwon","sequence":"additional","affiliation":[{"name":"School of Medicine, University of Washington, Seattle, Washington, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5097-2113","authenticated-orcid":false,"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of North Carolina (UNC) at Charlotte, Charlotte, North Carolina, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1356-0202","authenticated-orcid":false,"given":"Jin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computing, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8174-1772","authenticated-orcid":false,"given":"Wenzhan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-8616","authenticated-orcid":false,"given":"Fei","family":"Dou","sequence":"additional","affiliation":[{"name":"School of Computing, University of Georgia, Athens, Georgia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,15]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"crossref","first-page":"13693","DOI":"10.1109\/ACCESS.2019.2894115","article-title":"Motion artifact detection and reduction in bed-based ballistocardiogram","volume":"7","author":"Alivar Alaleh","year":"2019","unstructured":"Alaleh Alivar, Charles Carlson, Ahmad Suliman, Steve Warren, Punit Prakash, David E Thompson, and Balasubramaniam Natarajan. 2019. 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