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This study explores the integration of an embedded photoplethysmography (PPG) sensor within the AffectaPocket robot to enable continuous heart rate monitoring during tactile interactions, addressing the limitations of external wearables. By employing a two-stage filtering approach that combines Inertial Measurement Unit (IMU) data and a confidence-based smoothing algorithm, the authors effectively mitigate motion artifacts that typically hinder accurate readings in handheld devices. The results indicate a significant reduction in Mean Absolute Percentage Error, achieving statistical equivalence to traditional wrist-worn sensors, thus validating the feasibility of this approach for enhancing the robot's autonomy and usability in anxiety relief applications for children.
Heart rate monitoring in handheld robots can be as accurate as traditional wearables, thanks to innovative filtering techniques that tackle motion artifacts.
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.