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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

TL;DR

A closed-loop CPR training glove that integrates a high-resolution tactile sensing array and vibrotactile actuators for self-directed practice shows that haptic feedback reduced visual distraction compared to audio-visual cues, though simplified patterns were required for reliable perception under dynamic load.

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.

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

TL;DR

A closed-loop CPR training glove that integrates a high-resolution tactile sensing array and vibrotactile actuators for self-directed practice shows that haptic feedback reduced visual distraction compared to audio-visual cues, though simplified patterns were required for reliable perception under dynamic load.

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.
Paper Structure (31 sections, 1 equation, 10 figures, 3 tables)

This paper contains 31 sections, 1 equation, 10 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Training Glove Design and Fabrication
  3. Tactile Sensors
  4. Vibrotactile Haptics
  5. Haptic Placement and Pattern
  6. Haptic Intensity Range
  7. Control Circuit
  8. CPR Performance Modeling
  9. Task Formulation
  10. Rate
  11. Force
  12. Pose
  13. Data Collection
  14. Data Preprocessing
  15. Offset correction
  16. ...and 16 more sections

Figures (10)

  • Figure 1: Closed-Loop CPR Self-training System Overview.
  • Figure 2: (a) CPR self-training glove with integrated tactile sensors, vibrotactile haptics, control circuit and FPCB connectors. (b) The tactile sensing array is made of flexible polyimide sheets with copper traces and a Velostat layer. (c) Close-up of the custom control board with sensing and haptic circuits.
  • Figure 3: Haptic Intensity Range Validation Results. Confidence-weighted proportion of each intensity being recognized as low, mid, and high.
  • Figure 4: Examples of the 3-stage compression force data. Stage 1: Applying and releasing pressure in 10kg increments; Stage 2: Gradually increasing and decreasing pressure; Stage 3: Simulating random compressions.
  • Figure 5: Heatmap of average pressure distribution on the sensor for each hand pose case. Brighter areas represent higher pressure.
  • ...and 5 more figures