An Interactive Mobile Human Resource Management Platform Based on Affective Computing and Dynamic Causal Modeling

Abstract

Traditional human resource management approaches rely on low-frequency, questionnairebased methods to capture employee emotional states, whereby elevated risks of privacy leakage are introduced and the relationships between emotion and performance are statistically modeled. Such limitations hinder adaptation to the evolving demands of mobile-enabled management environments. Leveraging the portability of mobile devices and the heterogeneity of embedded sensors, an interactive mobile human resource management platform based on affective computing was proposed. To address the requirements of mobile scenarios, an integrated technical framework combining edge–cloud collaboration and dynamic causal inference was constructed. Three critical challenges were addressed, including the lightweight deployment of affective computing models on edge devices, privacy-preserving mechanisms for sensitive data, and the dynamic modeling of causal relationships among employee emotional responses, work engagement, and performance outcomes. A robust technical paradigm is thus provided for the intelligent upgrading of mobile human resource management systems, while new research directions are established for the interdisciplinary integration of affective computing and mobile office systems.