Mingzhuo Ma

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.

To address the challenge of mapping tactile patterns to language in early Braille education, we propose BrailleRhythm, an interactive system that overlays a flexible piezoresistive sensor sheet onto customized Braille cards. Real‐time auditory cues delivered via the Web Speech API vocalize detected characters and assembled words, reinforcing phonological associations during both sliding exploration and pressing confirmation. Unlike translation‐focused or robotic sensing tools, BrailleRhythm is optimized specifically for human learners on a low‐cost, extensible platform. The design supports rapid prototyping of embedded on‐device processing and leverages tangible word construction to deepen sensorimotor engagement. Future work will include formal usability evaluations and integration of multilingual Braille standards to broaden accessibility and educational impact.

Fetal heart rate (FHR) monitoring is critical for prenatal care, yet current methods rely on expensive, specialized equipment and trained personnel, limiting accessibility in low-resource and at-home settings. We present DopFone, a novel approach leveraging the built-in speaker and microphone of commodity smartphones to non-invasively estimate FHR via Doppler-based audio sensing. Our system emits an 18 kHz low-pitched ultrasound from the smartphone speaker and analyzes reflected signals recorded by the microphone to detect abdominal surface vibrations caused by fetal cardiac activity. Combining Doppler sensing with an AdaBoost regression model (validated via Leave-One-Out Cross-Validation on 23 pregnant participants), DopFone achieved a mean absolute error of 2.1±1.3 BPM compared to a reference-standard medical Doppler device. The 95% limits of agreement (±4.90 BPM) fall well within the clinically acceptable threshold of ±8 BPM. The system demonstrated robustness across gestational ages (19-39 weeks), maternal BMI (23-67 kg/m2), and variations in phone positioning. Our results establish that smartphones can deliver clinically reliable FHR estimation without external hardware, gel, or probes, bridging the gap between clinical monitoring and accessible at-home assessment.