Bio-State Band

Electrodermal activity (EDA) is a well-established physiological signal used to evaluate autonomic nervous system responses associated with stress, emotional arousal, and cognitive workload. Traditionally, EDA measurements have been performed in laboratory environments using benchtop instrumentation and proprietary commercial systems. While these platforms provide accurate and validated data, their high cost, limited portability, and closed hardware and software architectures restrict accessibility for student researchers, wearable applications, and real-world physiological monitoring. The rapid growth of wearable health technologies has highlighted a persistent tradeoff in physiological research between usability and transparency. Research-grade systems often rely on expensive, black-box software such as BIOPAC AcqKnowledge, limiting insight into signal processing and algorithmic methods. In contrast, many hobbyist biosensor projects emphasize simplicity at the expense of rigor and educational value. This gap underscores the need for an integrated hardware–software platform that bridges professional physiological research tools and student-focused embedded systems while preserving analytical reliability. This project presents the design and implementation of a wearable electrodermal activity monitoring system using a microcontroller-based architecture with custom analog front-end circuitry, paired with an open and transparent software platform. The system captures EDA, heart rate variability, skin temperature, and gyroscope data from a wearable glove and processes signals using Python and NeuroKit2 to decompose tonic and phasic EDA components while mitigating motion artifacts. Results are displayed through an intuitive PC-based interface, providing an affordable, research-grade, and educational tool for physiological data analysis and future multimodal biosensing applications.