AI 中文总结
研究针对毫米波人体姿态估计的不可靠问题，提出PRISM框架，通过三个核心组件实现可调度边缘人体姿态估计，在多数据集评估中显著降低延迟、零错过率且精度最优。
AI 中文摘要
毫米波（mmWave）是一种有前景的模态，适用于隐私要求高、资源有限的移动部署场景中的人体姿态估计（HPE），例如浴室中的跌倒检测或卧室中的活动监测，这些场景不允许使用相机，且计算密集型处理不可行。尽管毫米波信号自然地将人体反射限制在紧凑的、受物理约束的区域内，但现有系统的算法基础无法提供确定性执行和精度保证：它们要么均匀处理全频谱，导致不同场景下延迟不可预测，要么应用有损压缩，丢弃重要的姿态结构。为解决此问题，我们提出PRISM框架，该框架利用射频反射的空间集中度实现可调度的边缘人体姿态估计。PRISM包含三个核心组件：1）受物理约束的积分处理（PBIP），通过常数时间积分查询限制计算；2）受物理自适应的实例提案（PAIP），将涉及多人的场景分解为有界的局部子问题；3）感知截止时间的操作轮廓（DAOP），为运行时质量-延迟权衡提供离线验证的最坏情况边界。我们在涵盖不同雷达配置的四个公共数据集上评估PRISM，报告该套件的物理边界和姿态精度测量结果，并检查多人记录以及额外单人数据集上的感知截止时间调度情况。在单线程隔离执行下，与错过截止时间的基线相比，PRISM将第99百分位延迟降低了24%至58%，在评估的轨迹上记录了0.0%的错过率，并在可满足截止时间的配置中实现了最高的姿态精度，为移动边缘硬件上的可调度毫米波感知提供了实用途径。
英文摘要
Millimeter-wave (mmWave) is a promising modality for human pose estimation (HPE) in mobile deployments with strong privacy requirements and limited resources, such as fall detection in bathrooms or activity monitoring in bedrooms, where cameras are inadmissible and computationally demanding processing is infeasible. Although mmWave signals naturally confine human reflections to compact, physically bounded regions, the algorithmic foundations of existing systems fail to provide deterministic execution and accuracy guarantees. They either process the full spectrum uniformly, resulting in unpredictable latency that varies across different scenes, or apply lossy compression that discards vital pose structures. To address this, we present PRISM, a framework that exploits the spatial concentration of RF reflections to achieve schedulable edge HPE. PRISM introduces three core components: 1) Physics-Bounded Integral Processing (PBIP), which restricts computation via constant-time integral queries; 2) Physics-Adaptive Instance Proposal (PAIP), which decomposes scenes involving multiple people into bounded local subproblems; and 3) Deadline-Aware Operation Profiles (DAOP), which provide offline-verified worst-case bounds for runtime quality-latency trade-offs. We evaluate PRISM on four public datasets spanning diverse radar configurations, reporting physical-bound and pose-accuracy measurements across this suite and examining deadline-aware scheduling on multi-person recordings together with an additional single-person set. Under single-threaded isolated execution, PRISM reduces 99th-percentile latency by 24\%--58\% relative to baselines that miss the deadline, records a 0.0\% miss rate on the evaluated traces, and attains the highest pose accuracy among deadline-feasible configurations, providing a practical route toward schedulable mmWave sensing on mobile edge hardware.
