Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables
This work addresses the need for continuous, cost-efficient stress monitoring for users of wearable devices, though it is incremental as it builds on existing flexible electronics research.
The paper tackled the challenge of implementing machine learning classifiers in flexible electronics for continuous stress monitoring, resulting in a design space exploration of over 1200 classifiers that achieve higher accuracy than current methods while being low-power and compact.
Conventional stress monitoring relies on episodic, symptom-focused interventions, missing the need for continuous, accessible, and cost-efficient solutions. State-of-the-art approaches use rigid, silicon-based wearables, which, though capable of multitasking, are not optimized for lightweight, flexible wear, limiting their practicality for continuous monitoring. In contrast, flexible electronics (FE) offer flexibility and low manufacturing costs, enabling real-time stress monitoring circuits. However, implementing complex circuits like machine learning (ML) classifiers in FE is challenging due to integration and power constraints. Previous research has explored flexible biosensors and ADCs, but classifier design for stress detection remains underexplored. This work presents the first comprehensive design space exploration of low-power, flexible stress classifiers. We cover various ML classifiers, feature selection, and neural simplification algorithms, with over 1200 flexible classifiers. To optimize hardware efficiency, fully customized circuits with low-precision arithmetic are designed in each case. Our exploration provides insights into designing real-time stress classifiers that offer higher accuracy than current methods, while being low-cost, conformable, and ensuring low power and compact size.