A Closed-Loop CPR Training Glove with Integrated Tactile Sensing and Haptic Feedback
jaeyoung moon, Mingzhuo Ma, Qifeng Yang, Youjin Choi, Seokhyun Hwang, Samuel Burden, Kyung-Joong Kim, Yiyue Luo
AI summary
Problem
Conventional CPR training relies on expensive, non-portable manikins and audio-visual cues that cause distraction and fail to adapt to rescuer variability or monitor critical metrics like hand pose.
Approach
The authors designed a lightweight wearable glove integrating a high-density tactile sensor array and vibrotactile actuators to continuously monitor compression metrics and deliver immediate, localized haptic feedback for self-directed practice.
Key results
- Tactile sensor achieves >92% accuracy for force and pose estimation with sub-millisecond inference latency
- Sensor demonstrates wide-range sensitivity (~0.85 over 0-600 N) and stable performance over 300 cycles
- Haptic feedback reduces visual distraction compared to audio-visual cues, though simplified patterns are required for reliable perception
- System costs only $64.195, offering a highly portable and adaptive alternative to commercial training manikins
Why it matters
Provides an accessible, low-cost tool for self-directed CPR practice that improves training accuracy and focus, potentially increasing public resuscitation success rates.
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.