Falls are one of the leading causes of injury and hospitalization among elderly individuals, making reliable fall awareness an essential capability for safety monitoring in residential environments. However, existing fall detection systems often rely on wearable devices or fixed sensing installations, which may suffer from low user compliance, limited spatial coverage, or degraded performance under occlusion and poor lighting conditions. In this work, we propose \textbf{EM-Fall}, an embodied fall detection framework deployed on a mobile humanoid robot. The system integrates millimeter-wave (mmWave) sensing with robotic mobility, allowing the robot to actively adjust its sensing viewpoint and maintain target observability across rooms and under occlusion. To address interference in complex residential environments, including pet motion and multipath artifacts, we design a human-centered perception pipeline combined with lightweight temporal modeling to capture motion evolution before, during, and after fall events. We evaluate the proposed system across eight real indoor environments with four participants and construct an in-home mmWave fall detection dataset. Experimental results show that the embodied mobile sensing paradigm improves monitoring continuity and maintains robust fall detection performance under diverse environmental conditions. The proposed framework provides a practical solution for robot-assisted safety monitoring in home environments.

翻译：跌倒事件是导致老年人受伤和住院的主要原因之一，使得可靠的跌倒感知能力成为居住环境安全监测的关键需求。然而，现有跌倒检测系统通常依赖可穿戴设备或固定传感装置，存在用户依从性低、空间覆盖受限、或遮挡与不良光照条件下性能下降等问题。本文提出EM-Fall，一种部署于移动人形机器人的具身跌倒检测框架。该系统融合毫米波感知与机器人移动能力，使机器人能够主动调整传感视角，在多房间及遮挡条件下维持目标可观测性。为应对复杂居住环境中的干扰因素（包括宠物运动和多径伪影），我们设计了以人为中心的感知管线，结合轻量级时序建模以捕捉跌倒事件发生前、中、后的运动演化过程。通过八类真实室内环境、四名参与者采集数据，构建了家庭场景毫米波跌倒检测数据集。实验结果表明，具身移动感知范式能提升监测连续性，并在多样化环境条件下保持稳健的跌倒检测性能。所提框架为家庭环境中的机器人辅助安全监测提供了实用解决方案。