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Open AccessArticle

Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory

1
Electronics Engineering Technology, College of Industrial Technology, King Mongkut’s University of Technology North Bangkok, 1518 Pracharad 1 Rd., Wongsawang, Bangsue, Bangkok 10800, Thailand
2
Division of Robotics, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Korea
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Department of Control and Instrumentation Engineering, Kwangwoon University, 20 Gwangun-ro, Nowon-gu, Seoul 01897, Korea
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Center of Human-centered Interaction for Coexistence, 5, Hwarang-ro 14-gil, Seongbuk-gu, CHIC, Seoul 02792, Korea
5
Mechatronics Technology Convergence R&D Group, Korea Institute of Industrial Technology, 320 Techno Sunhwan-ro, Yuga-eup, Dalseong-gun, Daegu 42994, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(23), 5331; https://doi.org/10.3390/s19235331
Received: 4 October 2019 / Revised: 23 November 2019 / Accepted: 29 November 2019 / Published: 3 December 2019
(This article belongs to the Section Intelligent Sensors)
Attention capability is an essential component of human–robot interaction. Several robot attention models have been proposed which aim to enable a robot to identify the attentiveness of the humans with which it communicates and gives them its attention accordingly. However, previous proposed models are often susceptible to noisy observations and result in the robot’s frequent and undesired shifts in attention. Furthermore, most approaches have difficulty adapting to change in the number of participants. To address these limitations, a novel attentiveness determination algorithm is proposed for determining the most attentive person, as well as prioritizing people based on attentiveness. The proposed algorithm, which is based on relevance theory, is named the Scalable Hidden Markov Model (Scalable HMM). The Scalable HMM allows effective computation and contributes an adaptation approach for human attentiveness; unlike conventional HMMs, Scalable HMM has a scalable number of states and observations and online adaptability for state transition probabilities, in terms of changes in the current number of states, i.e., the number of participants in a robot’s view. The proposed approach was successfully tested on image sequences (7567 frames) of individuals exhibiting a variety of actions (speaking, walking, turning head, and entering or leaving a robot’s view). From these experimental results, Scalable HMM showed a detection rate of 76% in determining the most attentive person and over 75% in prioritizing people’s attention with variation in the number of participants. Compared to recent attention approaches, Scalable HMM’s performance in people attention prioritization presents an approximately 20% improvement. View Full-Text
Keywords: human–robot interaction; attention model; measure of attentiveness; relevance theory; Scalable Hidden Markov Model human–robot interaction; attention model; measure of attentiveness; relevance theory; Scalable Hidden Markov Model
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Tiawongsombat, P.; Jeong, M.-H.; Pirayawaraporn, A.; Lee, J.-J.; Yun, J.-S. Vision-Based Attentiveness Determination Using Scalable HMM Based on Relevance Theory. Sensors 2019, 19, 5331.

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