SuperSenseDoctor：一种用于健康追踪的多模态非接触式智能体
SuperSenseDoctor: A Multimodal and Contactless Agent for Health Tracking
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中文总结 AI 辅助
针对老年人居家健康监测的隐私与依从性问题，提出多模态非接触式智能体SuperSenseDoctor，融合WiFi、毫米波雷达和温度数据，实现高精度心率、呼吸监测及跌倒识别，并具备可审计的决策循环。
中文摘要 AI 辅助
人口老龄化日益加剧，使得在家庭环境中安全、独立地监测老年人的需求不断增加。然而，摄像头、可穿戴设备以及人工检查常常带来隐私、依从性和注意力方面的负担，从而阻碍了持续的健康监测。本文提出了SuperSenseDoctor，一种用于长期家庭健康追踪的多模态非接触式智能体架构。该系统将WiFi、毫米波雷达和表面温度转换为持续的人体健康状态。系统依靠固定的决策规则进行连续的日常监测，并对预定义的危险做出响应。当出现异常信号时，事件驱动的推理仅分析标准化证据，以生成可追溯的护理支持措施。通过这种方式，SuperSenseDoctor将感知、时间状态、推理和行动整合到一个统一且可审计的循环中。经过校准的多模态流程在心率方面达到了1.994次/分钟的平均绝对误差（MAE）和3.142次/分钟的均方根偏差（RMSD），在呼吸频率方面达到了0.197次/分钟的MAE和0.263次/分钟的RMSD，以及96.5%的跌倒识别准确率。评估还涵盖了9个时间区间内的2686个一秒状态，并达到了96.7%的标准级智能体检查表通过率。这些结果证明了用于长期家庭健康监测的有状态非接触式感知到行动架构的可行性。
英文摘要
Population aging is increasing the need to monitor older adults safely and independently at home. However, cameras, wearables, and manual checks often introduce privacy, adherence, and attention burdens that hinder sustained health monitoring. This paper presents SuperSenseDoctor, a multimodal contactless agent architecture for long-term home health tracking. The system transforms WiFi, mmWave radar, and surface temperature into a persistent human health state. The system relies on fixed decision rules to conduct continuous daily monitoring and respond to pre-defined hazards. When abnormal signals appear, event-driven reasoning analyzes only standardized evidence to produce traceable care-support measures. In this manner, SuperSenseDoctor integrates sensing, temporal state, reasoning, and action into a unified and auditable loop. The calibrated multimodal pipeline achieves 1.994 bpm mean absolute error (MAE) and 3.142 bpm root mean square deviation (RMSD) for heart rate, 0.197 bpm MAE and 0.263 bpm RMSD for respiratory rate, and 96.5% fall-recognition accuracy. The evaluation also covers 2686 one-second states across 9 chronological intervals and reaches a 96.7% criterion-level Agent checklist pass rate. These results demonstrate the feasibility of a stateful contactless sensing-to-action architecture for long-term home health monitoring.
发表机构
- Nanjing University of Posts and Telecommunications（南京邮电大学）
机构由 AI 辅助整理，请以论文原文为准。
