Optimal Arm-Based Sensor Placement for Wearable ECG and EDA Monitoring in Real-World Environment

Authors: Bao Do, Bongmook Lee

SUNY Campus: SUNY Poly

Presentation Type: Poster

Location: Old Union Hall

Presentation #: 56

Timeslot: Session D 3:00-4:00 PM

Abstract: Continuous Electrocardiogram (ECG) and Electrodermal activity (EDA) monitoring are vital for personalized healthcare and mental health tracking. However, motion artifacts and singular measuring in current devices often reduce signal accuracy during daily activities. Additionally, simultaneous measurement of ECG and EDA offers an integrated perspective of autonomic nervous system dynamics, linking stress, emotion and physiology. As a result, a new compact, wearable system that integrates ECG and EDA sensors has been developed, enabling continuous, multimodal monitoring in real-world environments. This study identify optimal arm-based sensor placements for both ECG and EDA to maximize accuracy while minimize artifacts. ECG electrode placements were systematically evaluated across upper left arms region and evaluated against chest based data, processed via MATLAB algorithms. EDA was tested on fingers, palm, and wrist, assessing skin conductance and motion artifacts. Furthermore, ECG-derived heart rate (BPM) was cross-validated against the Apple Watch Series 6 (PPG), analyzing percentage differences. Deltoid-region ECG placement achieved optimal fidelity, with minimal artifacts and BPM consistency with the Apple Watch during activities. For EDA, the wrist provided the clearest readings. Skin conductance changes matched hand movements with minimal noise. Validated ECG and EDA placements enable reliable multimodal monitoring in real-world settings. The system’s artifact-resistant design supports novel applications scalable detection of arrhythmia or stress disorders.