International Journal of Science, Engineering and TechnologyAuthors: Neal Bheda, Dr.Asha Durafe, Neeil Mahadik, Heet Thakkar, Jeel Mange, Dhruv Trivedi, Aayushi Bhanushali
Abstract: Wearable health monitoring systems (WHMS) rep-resent a transformative paradigm for continuous, non-invasive physiological surveillance, enabling early detection of life-threatening conditions outside traditional clinical settings. This paper presents the design, implementation, and clinical eval-uation of a compact five-modality WHMS that concurrently acquires electrocardiographic (ECG) waveforms, photoplethys-mographic (PPG) oxygen saturation (SpO), infrared skin-surface temperature, barometric-pressure-derived blood pressure esti-mates, and six-axis inertial motion data. An on-board Raspberry Pi Zero 2W edge processor executes real-time QRS detection, four-class LSTM arrhythmia classification, adaptive-filter SpO computation, and random-forest fall detection before forwarding compressed feature vectors via Bluetooth Low Energy 5.0 to a paired smartphone gateway and thence to a HIPAA-compliant cloud backend. Clinical validation across 22 volunteers against gold-standard reference instruments yielded ECG heart-rate MAE of 0.7 bpm, SpO MAE of 1.1%, systolic blood-pressure MAE of 4.8 mmHg (meeting AAMI SP-10), arrhythmia classifi-cation macro-F1 of 95.4%, fall-detection sensitivity of 97.3%, and an operational runtime of 11.2 hours under a realistic mixed-activity duty-cycle profile. These results demonstrate that deeply integrated edge-cloud WHMS architectures are capable of meeting clinical accuracy standards within a consumer-wearable form factor.
