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Inertial Motion Tracking on Mobile and Wearable Devices:Recent Advancements and Challenges 被引量:1

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摘要 Motion tracking via Inertial Measurement Units(IMUs)on mobile and wearable devices has attracted significant interest in recent years.High-accuracy IMU-tracking can be applied in various applications,such as indoor navigation,gesture recognition,text input,etc.Many efforts have been devoted to improving IMU-based motion tracking in the last two decades,from early calibration techniques on ships or airplanes,to recent arm motion models used on wearable smart devices.In this paper,we present a comprehensive survey on IMU-tracking techniques on mobile and wearable devices.We also reveal the key challenges in IMU-based motion tracking on mobile and wearable devices and possible directions to address these challenges.
出处 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2021年第5期692-705,共14页 清华大学学报(自然科学版(英文版)
基金 part supported by the National Key R&D Program of China(No.2018YFB1004800) the National Natural Science Foundation of China(No.61932013)。
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  • 1T. Ralston, G. Charvat, and J. Peabody, Real-time Through-wall Imaging using an Ultrawideband Multiple?Input Multiple-Output (MIMO) phased array radar system, in Proc. of 4th IEEE Int. Symposium on Phased Array Systems and Technology, Boston, USA, 2010, pp. 551- 558.
  • 2M. Youssef, M. Mah, and A. Agrawala, Challenges: Device-free passive localization for wireless environments, in Proc. of 13th ACM Annual Int. Conl on Mobile Computing and Networking, Montreal, Canada, 2007, pp. 222-229.
  • 31. Wilson and N. Patwari, Radio tomographic imaging with wireless networks, IEEE Trans. Mobile Comput., vol. 9, no. 5,pp. 621-632,2010.
  • 4Z. Zhou, Z. Yang, C. Wu, L. Shangguan, and Y. Liu, Towards omnidirectional passive human detection, in Proc. of 32nd IEEE Int. Conl on Computer Communications, Turin, Italy, 2013, pp. 3057-3065.
  • 5W. Xi, J. Zhao, X.- Y. Li, K. Zhao, S. Tang, X. Liu, and Z. Jiang, Electronic frog eye: Counting crowd using WiFi, in Proc. of 33rd IEEE Int. Conl on Computer Communications, Toronto, Canada, 2014, pp. 361-369.
  • 6Q. Pu, S. Gupta, S. Gollakota, and S. Patel, Whole-home gesture recognition using wireless signals, in Proc. oj' 19th ACM Annual Int. Conl on Mobile Computing and Networking, Miami, USA, 2013, pp. 27-38.
  • 7Y. Wang, J. Liu, Y. Chen, M. Gruteser, J. Yang, and H. Liu, E-eyes: Device-free location-oriented activity identification using fine-grained WiFi, in Proc. of 20th ACM Annual Int. Conf. on Mobile Computing and Networking, Maui, USA, 2014, pp. 617-628.
  • 8G. Wang, Y. Zou, Z. Zhou, K. Wu, and L. M. Ni, We can hear you with Wi-Fil in Proc. of 20th ACM Annual Int. Conf. on Mobile Computing and Networking, Maui, USA, 2014, pp. 593-604.
  • 9P. Melgarejo, X. Zhang, P. Ramanathan, and D. Chu, Leveraging directional antenna capabilities for fine?grained gesture recognition, in Proc. of 2014 ACM Int. Joint Conl on Pervasive and Ubiquitous Computing, Seattle, USA, 2014, pp. 541-551.
  • 10X. Liu, J. Cao, S. Tang, and 1. Wen, Wi-Sleep: Contactless sleep monitoring via WiFi signals, in Proc. of 35th IEEE Real-Time Systems Symposium, Rome, Italy, 2014.

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