Physiological Sensor Technologies in Workload Estimation: A Review

Abstract

Workload estimation is essential for artificial systems designed to assist users across various domains. These systems can provide personalized support by continuously assessing the user’s state and optimizing intervention strategies. Physiological data acquisition through advanced sensors enables objective and real-time workload estimation, offering a more reliable alternative to self-reported measures. Despite the growing interest in workload estimation, existing literature reviews are often domain-specific or focus on cognitive workload only, without providing a comprehensive analysis of methodologies for estimating both physical and cognitive workload across different applications. To address this gap, this systematic review analyzes 35 studies on multimodal physiological monitoring, examining feature extraction methodologies and supervised learning models used for workload estimation. The review identifies key challenges, including the need for standardized protocols, improved generalization across real-world scenarios, and the integration of adaptive artificial intelligence models. It underscores the role of sensor-based workload estimation in healthcare, rehabilitation, and assistive technologies, positioning it as a fundamental component for developing intelligent, user-centered, and adaptive human–machine interaction systems.