发表机构
The IT-University of Copenhagen（哥本哈根信息技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文验证了将PPG传感器集成到口袋型陪伴机器人AffectaPocket中，通过IMU与置信度滤波减少运动伪影以实现心率监测的可行性，其性能与腕戴式传感器相当。
AI 中文摘要
无处不在的陪伴机器人为儿童即时缓解焦虑提供了有前景的途径，但其有效性依赖于持续且无干扰地监测生理状态的能力。当前解决方案常依赖外部可穿戴设备，这带来了使用障碍并限制了机器人的自主性。本文研究将嵌入式光体积描记术（PPG）传感器直接集成到口袋型陪伴机器人AffectaPocket中，以在触觉交互期间实现独立的心率监测。我们解决了手持使用中固有的运动伪影这一重大挑战，方法是实施两阶段滤波流程，利用机载惯性测量单元（IMU）拒绝高方差片段，并使用基于置信度的平滑算法处理恢复阶段。我们在包含26名参与者的被试内研究中，将该系统与常用的腕戴式传感器进行了评估。结果表明，该滤波策略显著降低了平均绝对百分比误差，并达到了与真实测量值的统计等价性（p<0.05）。对短时间交互的分析显示，该传感器需要更长时间的稳定性才能收敛。
英文摘要
Ubiquitous companion robots offer a promising avenue for immediate anxiety relief in children, yet their effectiveness relies on the ability to monitor physiological states continuously and unobtrusively. Current solutions often depend on external wearables, which impose usability barriers and limit the robot's autonomy. This paper investigates the integration of an embedded photoplethysmography (PPG) sensor directly into a pocket-sized companion robot, AffectaPocket, to enable self-contained heart rate monitoring during tactile interaction. We address the significant challenge of motion artifacts inherent in handheld usage by implementing a two-stage filtering pipeline that utilizes an onboard Inertial Measurement Unit (IMU) to reject high-variance segments and a confidence-based smoothing algorithm for recovery periods. We evaluated the system against a commonly used wrist worn sensor in a Within-Subjects Study with 26 participants. Our results demonstrate that the filtering strategy significantly reduced the Mean Absolute Percentage Error and achieved statistical equivalence to the ground truth measurements (p<0.05). Analysis of short-duration interactions shows that the sensor requires stability over longer periods to converge.
Comments8 pages, 3 figures, 4 tables
