Deep learning-based human motion recognition for predictive context-aware human-robot collaboration

CIRP Annals ◽  
2018 ◽  
Vol 67 (1) ◽  
pp. 17-20 ◽  
Author(s):  
Peng Wang ◽  
Hongyi Liu ◽  
Lihui Wang ◽  
Robert X. Gao
Keyword(s):  
Deep Learning ◽  
Human Motion ◽  
Context Aware ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
Human Robot Collaboration

scholarly journals RGB-D-based human motion recognition with deep learning: A survey

2018 ◽  
Vol 171 ◽  
pp. 118-139 ◽  
Author(s):  
Pichao Wang ◽  
Wanqing Li ◽  
Philip Ogunbona ◽  
Jun Wan ◽  
Sergio Escalera
Keyword(s):  
Deep Learning ◽  
Human Motion ◽  
Motion Recognition ◽  
Human Motion Recognition

A systematic Approach to Human Motion Recognition using Deep Learning

2021 ◽  
Vol 10 (3) ◽  
pp. 1996-2005
Keyword(s):  
Deep Learning ◽  
Motion Analysis ◽  
Systematic Approach ◽  
Performance Metrics ◽  
Modern Technology ◽  
Human Motion ◽  
Human Motion Analysis ◽  
Motion Recognition ◽  
Motion Data ◽  
Human Motion Recognition

The application of Human Motion Analysis (HMA) under Computer Vision (CV) is an emerging field which entails various applications such as gait analysis, behavioural cloning and animation of motion, intent detection, etc. For such motion analysis various open source datasets have been created that help analyze motion behaviour. Motion Capture (mocap) files have been used extensively to store motion data and analyze them. Although the weightage of these applications can be huge in modern technology, not much work on human motion recognition has been done using mocap datasets. In this paper, we propose a systematic approach to human motion recognition using software engineering, data analysis and deep learning algorithms. A Deep Learning (DL) model using Gated Recurrent Network (GRU) for the classification of human motion. CMU mocap dataset is used for analyzing motion data and modelling the DL framework. The trained algorithm is tested using accuracy and Mean Absolute Error (MAE) and a user live feed as performance metrics. A 90.1% validation accuracy is obtained on final evaluation.

scholarly journals Research on Sports Training Action Recognition Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Wang
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Human Motion ◽  
Medical Monitoring ◽  
Human Beings ◽  
Sports Training ◽  
Motion Recognition ◽  
Human Motion Recognition

With the rapid development of science and technology in today’s society, various industries are pursuing information digitization and intelligence, and pattern recognition and computer vision are also constantly carrying out technological innovation. Computer vision is to let computers, cameras, and other machines receive information like human beings, analyze and process their semantic information, and make coping strategies. As an important research direction in the field of computer vision, human motion recognition has new solutions with the gradual rise of deep learning. Human motion recognition technology has a high market value, and it has broad application prospects in the fields of intelligent monitoring, motion analysis, human-computer interaction, and medical monitoring. This paper mainly studies the recognition of sports training action based on deep learning algorithm. Experimental work has been carried out in order to show the validity of the proposed research.

scholarly journals Application of human motion recognition technology in extreme learning machine

2021 ◽  
Vol 18 (1) ◽  
pp. 172988142098321
Author(s):  
Anzhu Miao ◽  
Feiping Liu
Keyword(s):  
Extreme Learning Machine ◽  
Research Work ◽  
Human Motion ◽  
Feature Descriptor ◽  
Background Variable ◽  
Motion Recognition ◽  
Network Training ◽  
Other Information ◽  
Human Motion Recognition ◽  
Learning Machine

Human motion recognition is a branch of computer vision research and is widely used in fields like interactive entertainment. Most research work focuses on human motion recognition methods based on traditional video streams. Traditional RGB video contains rich colors, edges, and other information, but due to complex background, variable illumination, occlusion, viewing angle changes, and other factors, the accuracy of motion recognition algorithms is not high. For the problems, this article puts forward human motion recognition based on extreme learning machine (ELM). ELM uses the randomly calculated implicit network layer parameters for network training, which greatly reduces the time spent on network training and reduces computational complexity. In this article, the interframe difference method is used to detect the motion region, and then, the HOG3D feature descriptor is used for feature extraction. Finally, ELM is used for classification and recognition. The results imply that the method proposed here has achieved good results in human motion recognition.

A Projection-based Human Motion Recognition Algorithm based on Depth Sensors

2021 ◽  
pp. 1-1
Author(s):  
Mu-Chun Su ◽  
Pang-Ti Tai ◽  
Jieh-Haur Chen ◽  
Yi-Zeng Hsieh ◽  
Shu-Fang Lee ◽  
...  
Keyword(s):  
Recognition Algorithm ◽  
Human Motion ◽  
Motion Recognition ◽  
Depth Sensors ◽  
Human Motion Recognition
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