将临床级睡眠健康带入日常生活Bringing clinical-grade sleep care into everyday life
| 指标Metric | 五季监测垫5S Sleep Mat | Apple Watch | WatchPAT | Nox-T3 |
|---|---|---|---|---|
| 睡眠分期SLEEP STAGING | ||||
| 准确率Accuracy | 85% | 71.2% | 63% | — |
| Kappa | 0.772 | 0.63 | 0.418 | — |
| OSA 检测 (AHI≥15)OSA DETECTION (AHI≥15) | ||||
| 灵敏度Sensitivity | 92.1% | 66.3% | 88% | 91.5% |
| 特异度Specificity | 94.4% | 98.5% | 63% | 76.5% |
| 易用性USABILITY | ||||
| 穿戴方式Wearing | 无需穿戴Contactless | 腕戴Wrist | 指戴/腕戴Finger/Wrist | 鼻导管/胸带Nasal/Chest |
通过多中心临床验证的医疗级生理参数监测，精准覆盖核心睡眠维度Clinically validated, medical-grade physiological monitoring across all core sleep dimensions
内置环境传感器，持续监测卧室环境参数，为睡眠质量优化提供数据支撑Built-in environmental sensors continuously track bedroom conditions to support sleep quality optimization
AI驱动的睡眠健康助手，主动分析数据趋势，发现潜在风险，提供个性化改善建议与持续追踪An AI-powered sleep health assistant that proactively analyzes trends, identifies risks, and delivers personalized recommendations with ongoing follow-up
基于深度学习从睡眠信号中提取的新型数字生物标志物，开辟睡眠医学的全新维度Novel digital biomarkers derived from deep learning analysis of sleep signals, opening new frontiers in sleep medicine
零样本BCG睡眠分期模型，在大规模PSG数据上训练后零迁移到心冲击图信号，实现居家无感睡眠监测Enables accurate sleep staging from contactless signals, without the need for wearable devices
基于心冲击图信号的深度学习框架，实现非接触式睡眠分期与呼吸事件检测，多中心临床验证A deep learning framework for contactless sleep staging and respiratory event detection, validated across multiple clinical centers
睡眠基座大模型，从低维生理信号生成高维PSG通道，实现跨模态信号复原与疾病预测A foundation model that reconstructs clinical-grade sleep signals from simple sensors, enabling scalable disease prediction
睡眠信号统一跨模态对齐框架，将异构夜间生理信号映射到共享向量空间，支持下游多任务学习Aligns multiple sleep signals into a unified model, improving accuracy across sleep analysis tasks
无论是科研合作、临床验证还是产业协同，我们期待与您携手前行Join us in bringing AI-powered sleep healthcare from the clinic to every home.
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