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The advent of industrial robotics and autonomous systems endow human-robotcollaboration in a massive scale. However, current industrial robots arerestrained in co-working with human in close proximity due to inability ofinterpreting human agents' attention. Human attention study is non-trivialsince it involves multiple aspects of the mind: perception, memory, problemsolving, and consciousness. Human attention lapses are particularly problematicand potentially catastrophic in industrial workplace, from assemblingelectronics to operating machines. Attention is indeed complex and cannot beeasily measured with single-modality sensors. Eye state, head pose, posture,and manifold environment stimulus could all play a part in attention lapses. Tothis end, we propose a pipeline to annotate multimodal dataset of humanattention tracking, including eye tracking, fixation detection, third-personsurveillance camera, and sound. We produce a pilot dataset containing two fullyannotated phone assembly sequences in a realistic manufacturing environment. Weevaluate existing fatigue and drowsiness prediction methods for attention lapsedetection. Experimental results show that human attention lapses in productionscenarios are more subtle and imperceptible than well-studied fatigue anddrowsiness.

Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study