{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T00:09:35Z","timestamp":1758931775465,"version":"3.44.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T00:00:00Z","timestamp":1758844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Large language models are capable of summarizing research, supporting clinical reasoning, and engaging in coherent conversations. However, their inputs are limited to user-generated text, which reflects subjective reports, delayed responses, and consciously filtered impressions. Integrating physiological signals provides a clear additional value, as it allows language models to consider real-time indicators of autonomic state alongside linguistic input, thereby enabling more adaptive and context-sensitive interactions in learning, decision-making, and healthcare. Therefore, we present a streamlined architecture for routing real-time heart rate variability data from a wearable sensor directly into a generative AI environment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using a validated heart rate variability sensor, we decoded Bluetooth-transmitted R-R intervals via a custom Python script and derived core heart rate variability metrics (HR, RMSSD, SDNN, LF\/HF ratio, pNN50) in real time. These values were published via REST and WebSocket endpoints through a FastAPI backend, making them continuously accessible to external applications\u2014including OpenAI's GPT models.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A live data pipeline from autonomic input to conversational output. A language model that does not just talk back, but responds to real-time physiological shifts in natural language. In multiple proof-of-concept scenarios, ChatGPT accessed real-time HRV data, performed descriptive analyses, generated visualizations, and adapted its feedback in response to autonomic shifts induced by low and high cognitive load.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This system represents an early prototype of bioadaptive AI, in which physiological signals are incorporated as part of the model's input context.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fdgth.2025.1670464","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T14:25:17Z","timestamp":1758896717000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Establishing a real-time biomarker-to-LLM interface: a modular pipeline for HRV signal acquisition, processing, and physiological state interpretation via generative AI"],"prefix":"10.3389","volume":"7","author":[{"given":"Morris","family":"Gellisch","sequence":"first","affiliation":[]},{"given":"Boris","family":"Burr","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,9,26]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"102821","DOI":"10.1016\/j.ijinfomgt.2024.102821","article-title":"ChatGPT usage in everyday life: a motivation-theoretic mixed-methods study","volume":"79","author":"Wolf","year":"2024","journal-title":"Int J Inf Manage"},{"key":"B2","doi-asserted-by":"publisher","first-page":"7743","DOI":"10.1080\/10447318.2023.2269006","article-title":"ChatGPT in the classroom. 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