ArticleCyborg and bionic systems (Washington, D.C.)2024
A Survey on 3D Skeleton-Based Action Recognition Using Learning Method.
Article in Cyborg and bionic systems (Washington, D.C.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
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- Event-Driven Multimodal Sensing and Computing for Context-Aware Home Monitoring Using Stereo Vision and Dietary Event Anchoring.Sensors (Basel, Switzerland) · 2026Article
- Spatiotemporal attention-based dance motion recognition and intelligent correction system using 3D motion capture.Scientific reports · 2026Article
- Automatic generation of labanotation based on a hybrid transformer-LSTM network with multi-scale spatio-temporal features.Scientific reports · 2026Article
- A Vision-Based Deep Learning Framework for Monitoring and Recognition of Chemical Laboratory Operations.Sensors (Basel, Switzerland) · 2026Article
- Interpretable skeleton-based movement profiles for formative feedback in school physical activity education.Frontiers in public health · 2026Article
- Skeleton-based rehabilitation movement quality classification: a leakage-controlled benchmark on IntelliRehabDS.Frontiers in medicine · 2026Article
- Bridging the visual-to-physical gap: physically aligned representations for fall risk analysis.Frontiers in medicine · 2026Article
- Clustered Federated Spatio-Temporal Graph Attention Networks for Skeleton-Based Action Recognition.Sensors (Basel, Switzerland) · 2025Article
- Predictive power of the HEMPA risk assessment method for musculoskeletal disorders in nurses and caregivers: insights and implications.BMC nursing · 2025Article
- Construction of intelligent gymnastics teaching model based on neural network and artificial intelligence.Scientific reports · 2025Article
- Machine Learning for Human Activity Recognition: State-of-the-Art Techniques and Emerging Trends.Journal of imaging · 2025Review
- Design of tennis auxiliary teaching system based on reinforcement learning and multi-feature fusion.PeerJ. Computer science · 2025Article
- BioCompNet: A Deep Learning Workflow Enabling Automated Body Composition Analysis toward Precision Management of Cardiometabolic Disorders.Cyborg and bionic systems (Washington, D.C.) · 2025Article
- Lightweight graph convolutional network with multi-attention mechanisms for intelligent action recognition in online physical education.PeerJ. Computer science · 2025Article
- Human motion similarity evaluation based on deep metric learning.Scientific reports · 2024Article
- Improved Generalizability in Medical Computer Vision: Hyperbolic Deep Learning in Multi-Modality Neuroimaging.Journal of imaging · 2024Article
- Multi-Stream Fusion Network for Skeleton-Based Construction Worker Action Recognition.Sensors (Basel, Switzerland) · 2023Article
- Multi-Camera-Based Human Activity Recognition for Human-Robot Collaboration in Construction.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning for Human Activity Recognition on 3D Human Skeleton: Survey and Comparative Study.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional skeleton-based action recognition (3D SAR) has gained important attention within the computer vision community, owing to the inherent advantages offered by skeleton data. As a result, a plethora of impressive works, including those based on conventional handcrafted features and learned feature extraction methods, have been conducted over the years. However, prior surveys on action recognition have primarily focused on video or red-green-blue (RGB) data-dominated approaches, with limited coverage of reviews related to skeleton data. Furthermore, despite the extensive application of deep learning methods in this field, there has been a notable absence of research that provides an introductory or comprehensive review from the perspective of deep learning architectures. To address these limitations, this survey first underscores the importance of action recognition and emphasizes the significance of 3-dimensional (3D) skeleton data as a valuable modality. Subsequently, we provide a comprehensive introduction to mainstream action recognition techniques based on 4 fundamental deep architectures, i.e., recurrent neural networks, convolutional neural networks, graph convolutional network, and Transformers. All methods with the corresponding architectures are then presented in a data-driven manner with detailed discussion. Finally, we offer insights into the current largest 3D skeleton dataset, NTU-RGB+D, and its new edition, NTU-RGB+D 120, along with an overview of several top-performing algorithms on these datasets. To the best of our knowledge, this research represents the first comprehensive discussion of deep learning-based action recognition using 3D skeleton data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.