Abstract:

Abstract Service robots are prevailing in many industries to assist humans in conducting repetitive tasks, which require a natural interaction called Human Robot Interaction (HRI). In particular, nonverbal HRI plays an important role in social interactions, which highlights the need to accurately detect the subject's attention by evaluating the programmed cues. In this paper, a conceptual attentiveness model algorithm called Attentive Recognition Model (ARM) is proposed to recognize a person’s attentiveness, which improves the accuracy of detection and subjective experience during nonverbal HRI using three combined detection models: face tracking, iris tracking and eye blinking. The face tracking model was trained using a Long Short-Term Memory (LSTM) neural network, which is based on deep learning. Meanwhile, the iris tracking and eye blinking use a mathematical model. The eye blinking model uses a random face landmark point to calculate the Eye Aspect Ratio (EAR), which is much more reliable compared to the prior method, which could detect a person blinking at a further distance even if the person was not blinking. The conducted experiments for face and iris tracking were able to detect direction up to 2 meters. Meanwhile, the tested eye blinking model gave an accuracy of 83.33% at up to 2 meters. The overall attentive accuracy of ARM was up to 85.7%. The experiments showed that the service robot was able to understand the programmed cues and hence perform certain tasks, such as approaching the interested person.

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