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 CSI-HC: A WiFi-Based Indoor Complex Human Motion Recognition Method

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Zhanjun Hao ◽  
Yu Duan ◽  
Xiaochao Dang ◽  
Tong Zhang
Keyword(s):  
Real Time ◽  
Human Motion ◽  
Detection Accuracy ◽  
Behavior Recognition ◽  
Motion Recognition ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Motion Data ◽  
Indoor Scenes ◽  
Human Motion Recognition

WiFi indoor personnel behavior recognition has become the core technology of wireless network perception. However, the existing human behavior recognition methods have great challenges in terms of detection accuracy, intrusion, and complexity of operations. In this paper, we firstly analyze and summarize the existing human motion recognition schemes, and due to the existence of the problems in them, we propose a noninvasive, highly robust complex human motion recognition scheme based on Channel State Information (CSI), that is, CSI-HC, and the traditional Chinese martial art XingYiQuan is verified as a complex motion background. CSI-HC is divided into two phases: offline and online. In the offline phase, the human motion data are collected on the commercial Atheros NIC and a powerful denoising method is constructed by using the Butterworth low-pass filter and wavelet function to filter the outliers in the motion data. Then, through Restricted Boltzmann Machine (RBM) training and classification, we establish offline fingerprint information. In the online phase, SoftMax regression is used to correct the RBM classification to process the motion data collected in real time and the processed real-time data are matched with the offline fingerprint information. On this basis, the recognition of a complex human motion is realized. Finally, through repeated experiments in three classical indoor scenes, the parameter setting and user diversity affecting the accuracy of motion recognition are analyzed and the robustness of CSI-HC is detected. In addition, the performance of the proposed method is compared with that of the existing motion recognition methods. The experimental results show that the average motion recognition rate of CSI-HC in three classic indoor scenes reaches 85.4%, in terms of motion complexity and indoor recognition accuracy. Compared with other algorithms, it has higher stability and robustness.

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

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.

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