让口袋机器人自己测心跳，用手势干扰也能稳准
Feasibility of Embedded Photoplethysmography Sensing in Short-Duration Tactile Interactions With Pocket-Sized Robots Using IMU- and Confidence-Based Filtering
- 用陀螺仪+置信度算法过滤手抖干扰
- 误差比传统手环低，结果与真实值无显著差异
- 适合做儿童情绪陪伴的自持式机器人
普及型陪伴机器人有望为儿童提供即时焦虑缓解，但其效果依赖于持续、无感的生理状态监测。现有方案多依赖外部可穿戴设备，带来使用障碍并限制机器人自主性。本文研究将嵌入式光电容积脉搏波（PPG）传感器直接集成于口袋大小的陪伴机器人AffectaPocket中，实现触觉交互时的独立心率监测。针对手持使用带来的运动伪影问题，提出两级滤波流程：利用机载惯性测量单元（IMU）剔除高波动片段，再通过置信度平滑算法恢复稳定期数据。在26名受试者的组内实验中，系统与常用腕戴传感器对比，显著降低平均绝对百分比误差，统计上等同于基准测量（p<0.05）。短时交互分析表明，传感器需更长时间稳定才能收敛。
原文摘要 · Abstract (English)
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.
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