Key result
ESP32-S3 wearable demonstrates synchronized multimodal data acquisition in an engineering proof of concept without human subjects.
Why the study?
Continuous remote physiological monitoring requires low-cost sensing nodes that preserve timing fidelity while maintaining secure connectivity and interpretable escalation.
An ESP32-S3-based multimodal wearable platform demonstrates engineering feasibility for remote vital-sign acquisition, though quantitative physiological validation in humans is still required.
Engineering feasibility for multimodal wearables shown; human validation of accuracy and clinical utility remains essential.
Continuous remote physiological monitoring requires low-cost sensing nodes to preserve timing fidelity while maintaining secure connectivity and interpretable escalation. This study presents an engineering proof-of-concept platform centered on an ESP32-S3 with AD8232 ECG, MAX30102 PPG/SpO2, and MAX30205 contact-temperature sensing. FreeRTOS separates nominal 100 Hz ECG/PPG acquisition from network communication, and the ECG R-wave-to-peripheral-PPG-foot interval is treated as pulse-arrival time (PAT). Telemetry is transmitted over authenticated HTTPS to a Laravel platform, while a separate FastAPI service provides human-in-the-loop normal/warning/critical functional triage. The current evidence is limited to bench-top engineering operation and synthetically constructed triage scenarios; no human-participant measurements are analyzed or reported. An ethics-approved rest-exertion-recovery protocol has been prepared for a future validation stage. The present results support system integration and engineering feasibility, while physiological accuracy, PAT-to-blood-pressure calibration, respiratory-rate agreement, classifier performance, timing reliability, and battery/power characteristics remain to be established quantitatively.
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Torad et al. (2026) studied Remote patient monitoring. ESP32-S3-Based Multimodal Wearable was evaluated on System-level engineering verification. The ESP32-S3-based multimodal wearable demonstrated synchronized multimodal acquisition and functional propagation of synthetic critical scenarios in an engineering proof of concept without human participants.
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