Spondon: A Low-Cost, Multi-Sensor Wearable for Understanding Stress in Resource-Constrained Countries

Abstract

Stress remains a significant health concern, yet commercial monitoring devices are often too expensive and complex for populations in developing countries. This research presents Spondon, a low-cost, multi-sensor wearable designed to detect stress in resource-constrained environments. Utilizing an ESP32 microcontroller with MAX30102, GSR, and temperature sensors, the device collects physiological data. We evaluated the system with 15 participants using the Stroop test to induce stress across baseline, task, and recovery phases. Statistical analysis confirmed a distinct 7.95% drop in HRV (RMSSD) during stress. The system achieved a 75.0% accuracy in binary acute stress detection (Baseline vs. Stress) using a Random Forest classifier with personalized baseline normalization. In addition, when evaluating continuous real-world monitoring across three physiological states (Baseline, Stress, and Recovery), Spondon achieved a 58.4% multi-class accuracy, successfully outperforming the 33.3% random-chance baseline. This work contributes a validated, accessible hardware platform and methodology, democratizing stress research for low-income communities and highlighting the importance of personalized, context-aware design in HCI.