Wearable Motion Capture: Reconstructing and Predicting 3D Human Poses From Wearable Sensors-Reference-Cited by-同舟云学术

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Wearable Motion Capture: Reconstructing and Predicting 3D Human Poses From Wearable Sensors

Published:2023-11 Issue:11 Volume:27 Page:5345-5356
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Moniruzzaman Md¹^ORCID,Yin Zhaozheng²^ORCID,Hossain Md Sanzid Bin³^ORCID,Choi Hwan⁴^ORCID,Guo Zhishan⁵^ORCID

Affiliation:

1. Department of Computer Science, Stony Brook University, Stony Brook, NY, USA

2. Department of Computer Science and Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA

3. Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, USA

4. Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL, USA

5. Department of Computer Science, North Carolina State University, Raleigh, NC, USA

Funder

NSF

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

https://ieeexplore.ieee.org/ielam/6221020/10311342/10238692-aam.pdf

Reference55 articles.

1. Deep inertial poser

2. Transformer Inertial Poser: Real-time Human Motion Reconstruction from Sparse IMUs with Simultaneous Terrain Generation

3. Inertial-sensor modules.,0

4. Stacked Hourglass Networks for Human Pose Estimation

5. OpenSim: Open-Source Software to Create and Analyze Dynamic Simulations of Movement

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