Human Motion Tracking Using 3D Image Features with a Long Short-Term Memory Mechanism Model-An Example of Forward Reaching

Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to t...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2021-12, Vol.22 (1), p.292
Main Authors: Chen, Kai-Yu, Chou, Li-Wei, Lee, Hui-Min, Young, Shuenn-Tsong, Lin, Cheng-Hung, Zhou, Yi-Shu, Tang, Shih-Tsang, Lai, Ying-Hui
Format: Article
Language:English
Subjects:
Accuracy
Cameras
Color imagery
Deep learning
depth image
Human mechanics
Human motion
human motion tracking
Humans
Imaging, Three-Dimensional
Inertial platforms
Information processing
Memory, Short-Term
Microelectromechanical systems
Motion
Motion capture
Neural networks
Older people
Rehabilitation
rehabilitation application
Sensors
Three dimensional imaging
Three dimensional motion
time-of-flight camera
Two dimensional models
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Summary:Human motion tracking is widely applied to rehabilitation tasks, and inertial measurement unit (IMU) sensors are a well-known approach for recording motion behavior. IMU sensors can provide accurate information regarding three-dimensional (3D) human motion. However, IMU sensors must be attached to the body, which can be inconvenient or uncomfortable for users. To alleviate this issue, a visual-based tracking system from two-dimensional (2D) RGB images has been studied extensively in recent years and proven to have a suitable performance for human motion tracking. However, the 2D image system has its limitations. Specifically, human motion consists of spatial changes, and the 3D motion features predicted from the 2D images have limitations. In this study, we propose a deep learning (DL) human motion tracking technology using 3D image features with a deep bidirectional long short-term memory (DBLSTM) mechanism model. The experimental results show that, compared with the traditional 2D image system, the proposed system provides improved human motion tracking ability with RMSE in acceleration less than 0.5 (m/s ) X, Y, and Z directions. These findings suggest that the proposed model is a viable approach for future human motion tracking applications.
ISSN:1424-8220
1424-8220
DOI:10.3390/s22010292