scholarly journals Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiangkun Li ◽  
Guoqing Sun ◽  
Yifei Li
Keyword(s):  
Pattern Recognition ◽  
Virtual Reality ◽  
Human Computer Interaction ◽  
Human Behavior ◽  
Fuzzy Theory ◽  
Human Motion ◽  
Two Dimensional ◽  
Motion Pattern ◽  
Motion Recognition ◽  
Computer Interaction

With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

scholarly journals Computer Vision for Human-Computer Interaction Using Noninvasive Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Janarthanan Ramadoss ◽  
J. Venkatesh ◽  
Shubham Joshi ◽  
Piyush Kumar Shukla ◽  
Sajjad Shaukat Jamal ◽  
...  
Keyword(s):  
Computer Vision ◽  
Human Computer Interaction ◽  
Target Identification ◽  
Body Movement ◽  
Human Motion ◽  
Human Hand ◽  
Motion Recognition ◽  
Human Motion Recognition ◽  
The Impact ◽  
Computer Interaction

Computer vision is a significant component of human-computer interaction (HCI) processes in interactive control systems. In general, the interaction between humans and computers relies on the flexibility of the interactive visualization system. Electromyography (EMG) is a bioelectric signal used in HCI that can be captured noninvasively by placing electrodes on the human hand. Due to the impact of complex background, accurate recognition and analysis of human motion in real-time multitarget scenarios are considered challenging in HCI. Further, EMG signals of human hand motions are exceedingly nonlinear, and it is important to utilize a dynamic approach to address the noise problem in EMG signals. Hence, in this paper, the Optimized Noninvasive Human-Computer Interaction (ONIHCI) model has been proposed to predict human motion recognition. Average Intrinsic Mode Function (AIMF) has been used to reduce the noise factor in EMG signals. Furthermore, this paper introduces spatial thermographic imaging to overcome the conventional sensor problem, such as gesture recognition and human target identification in multitarget scenarios. The human motion behavior in spatial thermographic images is examined by target trajectory, and body movement kinematics is employed to classify human targets and objects. The experimental findings demonstrate that the proposed method reduces noise by 7.2% and improves accuracy by 97.2% in human motion recognition and human target identification.

“There is Nothing New in Cyberspace” a Motion for Debate

1992 ◽  
Vol 36 (14) ◽  
pp. 1049-1049 ◽  
Author(s):  
Maxwell J. Wells
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Virtual Reality ◽  
Human Computer Interaction ◽  
Cold Fusion ◽  
Major Advance ◽  
Junk Bonds ◽  
Exciting Development ◽  
Computer Interaction ◽  
The Way

Cyberspace is the environment created during the experience of virtual reality. Therefore, to assert that there is nothing new in cyberspace alludes to there being nothing new about virtual reality. Is this assertion correct? Is virtual reality an exciting development in human-computer interaction, or is it simply another example of effective simulation? Does current media interest herald a major advance in information technology, or will virtual reality go the way of artificial intelligence, cold fusion and junk bonds? Is virtual reality the best thing since sliced bread, or is it last week's buns in a new wrapper?

Synthesis of 2D Ti3C2Tx MXene for Flexible Strain and Pressure Sensors

2021 ◽  
Author(s):  
Yuping Zeng ◽  
Wei Wu
Keyword(s):  
Human Computer Interaction ◽  
Pressure Sensors ◽  
Human Motion ◽  
Flexible Sensors ◽  
Microelectronic Devices ◽  
Human Motion Monitoring ◽  
Computer Interaction

As an important device in flexible and wearable microelectronic devices, flexible sensors have engaged a lot of attention due to their wide application in human motion monitoring, human-computer interaction and...

scholarly journals Effects of Different Feature Parameters of sEMG on Human Motion Pattern Recognition Using Multilayer Perceptrons and LSTM Neural Networks

2020 ◽  
Vol 10 (10) ◽  
pp. 3358 ◽  
Author(s):  
Jiyuan Song ◽  
Aibin Zhu ◽  
Yao Tu ◽  
Hu Huang ◽  
Muhammad Affan Arif ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Human Motion ◽  
Control Mode ◽  
Multilayer Perceptrons ◽  
Model Parameters ◽  
Motion Pattern ◽  
Accuracy Rate ◽  
Feature Parameters

In response to the need for an exoskeleton to quickly identify the wearer’s movement mode in the mixed control mode, this paper studies the impact of different feature parameters of the surface electromyography (sEMG) signal on the accuracy of human motion pattern recognition using multilayer perceptrons and long short-term memory (LSTM) neural networks. The sEMG signals are extracted from the seven common human motion patterns in daily life, and the time domain and frequency domain features are extracted to build a feature parameter dataset for training the classifier. Recognition of human lower extremity movement patterns based on multilayer perceptrons and the LSTM neural network were carried out, and the final recognition accuracy rates of different feature parameters and different classifier model parameters were compared in the process of establishing the dataset. The experimental results show that the best accuracy rate of human motion pattern recognition using multilayer perceptrons is 95.53%, and the best accuracy rate of human motion pattern recognition using the LSTM neural network is 96.57%.

Natural human–computer interaction control of multi operators in collaborative virtual maintenance based on optical human motion capture system

2016 ◽  
Vol 07 (02) ◽  
pp. 1650002
Author(s):  
Xiangyang Li ◽  
Zhili Zhang ◽  
Feng Liang ◽  
Qinhe Gao ◽  
Lilong Tan
Keyword(s):  
Human Computer Interaction ◽  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Motion Capture System ◽  
Human Motion Capture ◽  
Interaction Control ◽  
Time Motion ◽  
Virtual Maintenance ◽  
Computer Interaction

Aiming at the human–computer interaction control (HCIC) requirements of multi operators in collaborative virtual maintenance (CVM), real-time motion capture and simulation drive of multi operators with optical human motion capture system (HMCS) is proposed. The detailed realization process of real-time motion capture and data drive for virtual operators in CVM environment is presented to actualize the natural and online interactive operations. In order to ensure the cooperative and orderly interactions of virtual operators with the input operations of actual operators, collaborative HCIC model is established according to specific planning, allocating and decision-making of different maintenance tasks as well as the human–computer interaction features and collaborative maintenance operation features among multi maintenance trainees in CVM process. Finally, results of the experimental implementation validate the effectiveness and practicability of proposed methods, models, strategies and mechanisms.

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