Handwriting Trajectory Reconstruction Using Low-Cost IMU

In this paper, we propose a trajectory reconstruction method based on low-cost Inertial Measurement Unit (IMU) in smartphones. The IMU used in our work consists of a three-axis accelerometer and a three-axis gyroscope, which can record information of acceleration and rotation, respectively. Since in...

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Bibliographic Details
Published in:IEEE transactions on emerging topics in computational intelligence 2019-06, Vol.3 (3), p.261-270
Main Authors: Pan, Tse-Yu, Kuo, Chih-Hsuan, Liu, Hou-Tim, Hu, Min-Chun
Format: Article
Language:English
Subjects:
Acceleration
Accelerometers
complementary filter
convolutional neural network
Discriminant analysis
Feature extraction
Handwriting recognition
handwritten letter recognition
Image reconstruction
Inertial measurement unit
Inertial platforms
inertial sensor
linear discriminant analysis
Low cost
Motion perception
Random noise
Reconstruction
Reconstruction algorithms
Sensitivity
Smart phones
Smartphones
Three axis
Trajectories
Trajectory
trajectory reconstruction
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Summary:In this paper, we propose a trajectory reconstruction method based on low-cost Inertial Measurement Unit (IMU) in smartphones. The IMU used in our work consists of a three-axis accelerometer and a three-axis gyroscope, which can record information of acceleration and rotation, respectively. Since intrinsic bias and random noise usually cause unreliable IMU signals, filtering methods are utilized to reduce high- or low-frequency noises of the signals. In addition, to more accurately detect whether the smartphone is moving or not, we extract multiple features from IMU signals and train a movement detection model based on linear discriminant analysis (LDA). Then, a "reset switch" mechanism is applied when the smartphone is detected as in a static state. The "reset switch" mechanism can effectively restrain the accumulated error of displacement calculation. Finally, the trajectory reconstruction results are applied to handwritten letter recognition for English alphabet, and the experimental results show that our proposed trajectory reconstruction method is reliable.
ISSN:2471-285X
2471-285X
DOI:10.1109/TETCI.2018.2803777