User confusion during human-machine interaction is a leading cause of task abandonment in self-service kiosks (SSK) used within industrial control systems. Existing systems, however, cannot detect struggles until the user explicitly asks for help, which is often too late for effective intervention. This challenge is relevant to life-cycle engineering because of its impact on human-centric manufacturing, where unaddressed operator confusion can significantly degrade operational performance. This pilot study investigated the unobservable formation of help-seeking intentions by drawing on psychological frameworks that conceptualize help-seeking as a staged process. We explored the feasibility of modelling this internal process using multimodal data to design a proactive Just-In-Time Assistance Systems (JITAS). Nine participants completed tasks under manipulated ambiguity levels, while we recorded their EEG and facial expressions. Our key methodology employs retrospective interviews with video playback to elicit participant-validated timestamps as ground-truth labels, segmenting continuous physiological signals into distinct psychological states. Preliminary analysis confirmed that high ambiguity reliably induced confusion, thus validating our experimental paradigm. Facial expression variability, rather than static expression, has emerged as a promising indicator of confusion, whereas EEG patterns revealed heterogeneous stress responses. Rather than detecting precise moments of intention formation, our approach establishes a foundation for designing intervention strategies that respond to sustained struggles. These findings demonstrate the methodological feasibility of detection-based assistance systems that complement the interface design in both service and manufacturing contexts.
Li et al. (Thu,) studied this question.