Research Protocol for the Estimation of Recovery-stress States of Workers at the Manufacturing Site Using Wearables

This paper presents a research protocol for the estimation of workers’ recovery-stress states at a manufacturing site using wearable biosignal sensors and validated psychological assessments. Building on existing models of stress, recovery, and resilience, we propose the extension of an existing integrative framework — the Resilience Risk Stratification Model (RRSM; Paletta et al., 2024) — that captures both physiological strain and recovery dynamics over time. A field study of 2-4 weeks with 20 shop-floor workers will combine continuous biosignal monitoring using smart wearables — e.g., heart rate (HR), heart rate variability (HRV), motion, and sleep patterns via Garmin Vivosmart 5 — with repeated psychological testing (e.g., RESTQ-Work, NASA-TLX, PSS, RS-13). Wearable-derived features such as resting heart rate, HR recovery, HRV trends, and exponential recovery metrics (e.g., Time to Recovery and Area to Recovery) will be extracted. These features will be mapped onto psychological constructs via machine learning models, supporting early detection of stress overload and reduced resilience. The outcome will be a multidimensional, real-time estimate of resilience risk, suitable for feedback to both workers and supervisors. This methodology contributes to human-centered industrial innovation, offering a pathway toward adaptive support systems and sustainable well-being and performance at work.