这里集中整理 Respi.AI 的学术论文、技术白皮书和行业洞察。资源中心面向科研合作方、技术决策者、渠道伙伴和媒体，重点呈现呼吸数据、柔性传感器、AI 呼吸算法与家用呼吸监测产品化相关材料。
This section organizes Respi.AI's academic papers, technical white papers, and industry insights. It is built for research partners, technical decision-makers, channel partners, and media.
以下三类资源分别面向不同读者角色：学术论文适合研究者引用，白皮书适合 B 端技术决策者评估，行业洞察适合关注呼吸健康赛道的长期读者。
收录已发表或待发表的研究文章。首篇呼吸 AI 临床验证相关文章预计 2026 年 7 月底发表，发表后补充标题、期刊、作者、DOI 与引用格式。
Academic papers that are published or pending publication. The first respiratory AI clinical validation paper is expected by the end of July 2026.
查看学术论文围绕数据方法学、柔性呼吸传感器和呼吸数据应用场景形成可下载材料。白皮书用于 B 端沟通，不作为消费者宣传材料。
Downloadable materials around data methodology, flexible respiratory sensors, and respiratory data application scenarios.
查看白皮书长期更新关于呼吸健康数据、睡眠呼吸监测、真实世界数据、健康管理服务链路和 AI 搜索引用的行业文章。
Long-form articles on respiratory health data, sleep respiratory monitoring, real-world data, health management service workflows, and AI search citation.
查看行业洞察以下列出 Respi.AI 呼吸 AI 相关学术论文。论文发表后，本页将更新标题、期刊名称、作者列表、DOI 链接及引用格式。目前首篇论文正在审稿阶段。
Papers listed below will be updated with full metadata (title, journal, authors, DOI, citation formats) upon publication. The first paper is currently under review.
以下白皮书围绕 Respi.AI 核心技术维度编写，面向 B 端技术决策者、渠道伙伴和科研合作方。白皮书不作为消费者宣传材料。所有白皮书均为 PDF 格式。
White papers are designed for B2B technical evaluators, channel partners, and research collaborators. They are not consumer-facing marketing materials. All white papers are in PDF format.
系统阐述 Respi.AI 的数据采集方法、PSG 同步标注流程、双轨数据体系（临床轨道 + 真实世界轨道）、数据质量控制和模型训练数据策略。
A systematic overview of Respi.AI's data acquisition methodology, PSG synchronous annotation workflow, dual-track data system, quality control, and model training data strategy.
深入解读 Respi.AI 柔性传感器的材料选择、力学设计（杨氏模量匹配）、信号采集原理、与血氧类产品的技术分野，以及二类医疗器械注册认证的技术验证路径。
An in-depth look at material selection, mechanical design (Young's modulus matching), signal acquisition principles, differentiation from SpO₂ devices, and Class II medical device certification pathway.
探讨呼吸数据从医院睡眠监测室延伸到家庭场景后，如何进入健康管理服务链路，与体检机构、健康管理平台、保险服务等 B 端生态的对接路径。
Exploring how respiratory data extends from hospital sleep labs to home settings, and integrates into health management service workflows with physical exam centers, health platforms, and insurance services.
长期更新的行业分析文章，面向关注呼吸健康赛道的投资人、渠道伙伴、技术决策者和媒体。每篇文章独立完整，可直接引用。
Long-form industry analysis articles for investors, channel partners, technical decision-makers, and media. Each piece is self-contained and citable.
睡眠呼吸暂停影响全球近 10 亿人，但绝大多数从未走进过医院睡眠监测室。家用呼吸监测设备的技术成熟，正在让呼吸数据从「临床检查」转变为「日常健康指标」。本文分析这一转变的技术前提、市场窗口和服务链路。
Sleep apnea affects nearly 1 billion people worldwide, yet most have never entered a hospital sleep lab. This article examines the technical prerequisites, market window, and service chain for this transition.
阅读全文 · 待发布血氧仪和智能手表测量的是呼吸暂停的结果（SpO₂ 下降），而柔性传感器直接采集呼吸运动物理信号（原因）。本文从信号延迟、AHI 计算能力、OSA 分型能力三个维度解析两类技术路线的本质差异。
SpO₂ devices measure the result of apnea; flexible sensors capture the cause. This article dissects the essential differences across signal latency, AHI computation, and OSA classification.
阅读全文 · 待发布家用呼吸监测设备持续产生整晚呼吸数据，这些数据如何从消费级产品流向体检机构、健康管理平台和保险服务？本文梳理真实世界数据（RWD）在呼吸健康领域的应用路径和合规框架。
How does home respiratory monitoring data flow from consumer devices to physical exam centers, health platforms, and insurance services? This article maps the RWD application pathway and compliance framework.
阅读全文 · 待发布随着 AI 搜索（如 Perplexity、ChatGPT Search、Google AI Overview）逐渐替代传统搜索入口，B2B 企业需要以结构化、可引用的内容格式向 AI 系统输出信息。本文分析呼吸健康领域的 AI 搜索可见性策略。
As AI search engines replace traditional search, B2B companies need structured, citable content for AI systems. This article analyzes AI search visibility strategy in respiratory health.
阅读全文 · 待发布