A noise reduction method for IMU and its application on handwriting trajectory reconstruction

Tse Yu Pan, Chih Hsuan Kuo, Min Chun Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In this paper, we propose a trajectory reconstruction method based on a low-cost IMU (Inertial Measurement Unit), which is usually equipped in smartphones. The IMU used in our work consists of a 3-axis accelerometer and a 3-axis gyroscope, which can record information of acceleration and rotation, respectively. However, intrinsic bias and random noise cause unreliable IMU signals. Thus, to improve the accuracy of the reconstructed trajectory, we apply filtering methods to reduce high or low frequency noises of the signal. Moreover, the machine learning technique is utilized to detect the movement state of the smartphone. Also, instead of a simple threshold to detect the smartphone movement as implemented in previous related works, we extract multiple features from IMU signals and train a movement detection model based on the linear discriminant analysis (LDA) to increase the robustness of the system. Finally, a 'reset switch' mechanism is used to more effectively restrain the accumulated error of the accelerometer.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509015528
DOIs
Publication statusPublished - 2016 Sep 22
Event2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016 - Seattle, United States
Duration: 2016 Jul 112016 Jul 15

Publication series

Name2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016

Other

Other2016 IEEE International Conference on Multimedia and Expo Workshop, ICMEW 2016
CountryUnited States
CitySeattle
Period16-07-1116-07-15

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition

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