arXiv cs.AI· Behnam Moradi·· 14 小时前AI 评分31
EpiCoach：用智能体贝叶斯实验设计，从消费级可穿戴设备做个体化生理建模
The Athlete as a Scientist of One: Agentic Bayesian Experimental Design for Individualized Physiology from Consumer Wearables
AI 导读
EpiCoach 是一个智能体系统，把一名越野山地车运动员作为可问责 N-of-1 科学的研究对象，其关于运动员的假设须在赛前给出概率预测，并仅通过记录的统计拟合证据更新信念。
正文 · 原文
Abstract:Large language model (LLM) agents for personal health read wearable data and recommend, but do not learn about the individual in a way that can be audited or falsified. We present EpiCoach, an agentic system that treats one athlete, a cross-country mountain biker, as the subject of accountable N-of-1 science. Claims about the athlete live in an evidence-gated hypothesis registry: each must issue a probabilistic prediction about a future session before it occurs, and its belief moves only through computed evidence from a logged statistical fit. Predictions come from a mechanistic digital twin inferred from heart rate, GPS and barometric altitude: a lagged heart-rate response model, a fitness-fatigue model with learned time constants, and a latent technical-descending skill state, measured on discovered trail segments. The agent prescribes the next training week as a constrained experiment maximizing information gain subject to load and safety constraints, around two instrument rides. We evaluate the system two ways over eighteen months of data. A rolling-origin replay benchmark shows the twin beats standard forecasters on heart rate but not segment time, and the agentic loop matches a static model. A live prospective phase runs the weekly cycle: in its first three weeks the ledger sealed 20 predictions, scored 14, the descent forecast calibrated after one correction, the climb forecast pessimistic as the athlete improved, and the safety officer vetoed the agent twice. Every correction is preserved in the registry, a template for auditable, falsifiable personalization in digital health. We release the registry, benchmark and protocol.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.40370 [cs.RO] |
| (or arXiv:2610.40370v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.40370 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Behnam Moradi [view email]
[v1]
Thu, 29 Oct 2026 16:20:24 UTC (1,357 KB)
来源：arXiv cs.AI · arxiv.org