A Closed-Loop CPR Training Glove with Integrated Tactile Sensing and Haptic Feedback

TL;DR

This paper introduces a novel CPR training glove with integrated tactile sensors and haptic feedback, enabling real-time performance monitoring and guidance for self-directed CPR practice, reducing reliance on external cues.

Contribution

The paper presents a closed-loop CPR training glove with high-resolution tactile sensing and haptic feedback, providing real-time estimation and guidance, which is a new approach in CPR training tools.

Findings

Tactile sensors achieved ~0.85 sensitivity over 0-600 N

Force estimation accuracy >92% with sub-millisecond inference

Haptic feedback reduced visual distraction in user study

Abstract

Cardiopulmonary resuscitation (CPR) is a critical life-saving procedure, and effective training benefits from self-directed practice beyond instructor-led sessions. In this paper, we propose a closed-loop CPR training glove that integrates a high-resolution tactile sensing array and vibrotactile actuators for self-directed practice. The tactile sensing array measures distributed pressures across the palm and dorsum to enable real-time estimation of compression rate, force, and hand pose. Based on these estimations, the glove delivers immediate haptic feedback to guide the user for proper CPR, reducing reliance on external audio-visual displays. We quantified the tactile sensor performance by measuring wide-range sensitivity (~0.85 over 0-600 N), computing hysteresis (56.04%), testing stability (11.05% drift over 300 cycles), and estimating global signal-to-noise ratio (18.90 +/- 2.41 dB at 600 N). Our closed-loop pipeline provides continuous modeling and feedback of key performance metrics essential for high-quality CPR. Our lightweight statistical models achieves >92% accuracy for force estimation and hand pose classification within sub-millisecond inference time. Our user study (N=8) showed that haptic feedback reduced visual distraction compared to audio-visual cues, though simplified patterns were required for reliable perception under dynamic load. These results highlight the feasibility of the proposed system and offer design insights for future haptic CPR self-training system.