A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment

Yan, Xuke; Zhang, Linxi; Liu, Bo; Qu, Guangzhi

doi:10.1109/icmla55696.2022.00015

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) 2022

DOI: 10.1109/icmla55696.2022.00015

|View full text |Cite

A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment

Xuke Yan

Linxi Zhang

Bo Liu

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article1

Relationship

Self Cite1

Independent0

Authors

Journals

Cited by 1 publication

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey

Yan,

Liu,

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

Human pose estimation (HPE) is a crucial computer vision task with a wide range of applications in sports medicine, healthcare, virtual reality, and human-computer interaction. The demand for real-time HPE solutions necessitates the development of efficient deep-learning models that can be deployed on resource-constrained devices. While a few surveys exist in this area, none delve deeply into the critical intersection of efficiency and performance. This survey reviews the state-of-the-art efficient deep learning approaches for real-time HPE, focusing on strategies for improving efficiency without compromising accuracy. We discuss popular backbone networks for HPE, model compression techniques, network pruning and quantization, knowledge distillation, and neural architecture search methods. Furthermore, we critically analyze the existing works, highlighting their strengths, weaknesses, and applicability to different scenarios. We also present an overview of the evaluation datasets, metrics, and design for efficient HPE. Finally, we identify research gaps and challenges in the field, providing insights and recommendations for future research directions in developing efficient and scalable HPE solutions. INDEX TERMS survey, 2D human pose estimation, 3D human pose estimation, deep learning, efficiency I. INTRODUCTION A. MOTIVATION OF HUMAN POSE ESTIMATION

show abstract

Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey

Yan,

Liu,

2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Lightweight and Fast Approach for Upper Limb Range of Motion Assessment

Cited by 1 publication

References 17 publications

Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey

Efficient Monocular Human Pose Estimation Based on Deep Learning Methods: A Survey

Contact Info

Product

Resources

About