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A Real-time Device-free Head Motion Recognition Framework for Family Care Robots

Published: 18 October 2019 Publication History

Abstract

In this paper, we propose a practical framework to realize accurate, real-time head motion recognition without wearing a device on user's head, which is essential for interaction between human and family care robot system. Most of the previous work has focused on the estimation of head poses rather than the identification of specific head motions, which are more useful in human-robot interaction. Based on the results of the head pose estimation, we define eight head motions which contain most of the head movements we use in daily life and design a finite state machine(FSM) to identify these motions. Our framework shows excellent accuracy and low latency in real-world test. Besides, we set a group of thresholds that can be easily changed for the duration and intensity of different motion definition, so that it can be adjusted according to the needs in future applications. This framework is robust with great potential for domestic caring in an ageless aging society.

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cover image ACM Other conferences
ICCSE'19: Proceedings of the 4th International Conference on Crowd Science and Engineering
October 2019
246 pages
ISBN:9781450376402
DOI:10.1145/3371238
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 October 2019

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Author Tags

  1. accurate
  2. device-free
  3. finite state machine
  4. head motion recognition
  5. real-time

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  • Refereed limited

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ICCSE'19

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ICCSE'19 Paper Acceptance Rate 35 of 92 submissions, 38%;
Overall Acceptance Rate 92 of 247 submissions, 37%

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