scholarly journals Sports Dance Action Recognition System Oriented to Human Motion Monitoring and Sensing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shasha Ni ◽  
Dawei Yao
Keyword(s):  
Action Recognition ◽  
Human Movement ◽  
Research Direction ◽  
Recognition System ◽  
Human Motion ◽  
Dance Movement ◽  
Movement Recognition ◽  
Dance Movements ◽  
Human Motion Monitoring ◽  
Movement Monitoring

Because of its high research value, action recognition has become a very popular research direction in recent years. However, the research on the combination of motion recognition technology and dance movements is still in its infancy. At the same time, due to the high complexity of dance movements and the problems of human body self-occlusion when performing dances, research on dance video action recognition has been caused. Progress is relatively slow. This article mainly introduces the research of sports dance action recognition system oriented to human motion monitoring and sensing, fully considers the abovementioned problems, and makes in-depth research and analysis on the current excellent action recognition research content in this field. This paper proposes a research method of sports dance movement recognition for human movement monitoring and sensing, including sports dance movement classification algorithm and sports dance movement preprocessing algorithm, which is used to conduct research experiments on sports dance movement recognition for human movement monitoring and sensing. The experimental results of this article show that the average recognition accuracy of the sports dance action recognition system for human motion monitoring and sensing is 92%, which can be used in daily sports dance training and competition.

The Research of 3D Human Motion Simulation and Video Analysis System Implemented in Sports Training

2014 ◽  
Vol 926-930 ◽  
pp. 2743-2746 ◽  
Author(s):  
Rui Min Hu ◽  
Zhen Dong He ◽  
Feng Bai
Keyword(s):  
Computer Vision ◽  
Video Analysis ◽  
Rapid Development ◽  
Human Movement ◽  
Research Direction ◽  
Human Motion ◽  
Motion Simulation ◽  
Sports Training ◽  
Camera Tracking ◽  
Human Motion Simulation

With the rapid development of computer technology, human motion tracking based on video is a kind of using ordinary camera tracking unmarked human movement technology. It has important application value in automatic monitoring, human-computer interaction, sports analysis and many other fields. This research is a hot research direction in the field of computer vision in recent years. Because of the complexity of the problem and the lack of understanding of the nature of the human visual tracking based on video is always a difficult problem in computer vision. The research content of this article is set in sports training, for motion analysis of non-contact, no interfere with measurement and simulation requirements, the use of computer graphics and computer vision technology, discussing 3D human motion simulation technology based on video analysis.

Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Chunyan Xu ◽  
Rong Liu ◽  
Tong Zhang ◽  
Zhen Cui ◽  
Jian Yang ◽  
...  
Keyword(s):  
Action Recognition ◽  
Temporal Dynamics ◽  
Recognition Task ◽  
Human Movement ◽  
Body Part ◽  
Human Action ◽  
Human Motion ◽  
Body Parts ◽  
Dynamic Interactions ◽  
Spatio Temporal

In this work, we propose a dual-stream structured graph convolution network ( DS-SGCN ) to solve the skeleton-based action recognition problem. The spatio-temporal coordinates and appearance contexts of the skeletal joints are jointly integrated into the graph convolution learning process on both the video and skeleton modalities. To effectively represent the skeletal graph of discrete joints, we create a structured graph convolution module specifically designed to encode partitioned body parts along with their dynamic interactions in the spatio-temporal sequence. In more detail, we build a set of structured intra-part graphs, each of which can be adopted to represent a distinctive body part (e.g., left arm, right leg, head). The inter-part graph is then constructed to model the dynamic interactions across different body parts; here each node corresponds to an intra-part graph built above, while an edge between two nodes is used to express these internal relationships of human movement. We implement the graph convolution learning on both intra- and inter-part graphs in order to obtain the inherent characteristics and dynamic interactions, respectively, of human action. After integrating the intra- and inter-levels of spatial context/coordinate cues, a convolution filtering process is conducted on time slices to capture these temporal dynamics of human motion. Finally, we fuse two streams of graph convolution responses in order to predict the category information of human action in an end-to-end fashion. Comprehensive experiments on five single/multi-modal benchmark datasets (including NTU RGB+D 60, NTU RGB+D 120, MSR-Daily 3D, N-UCLA, and HDM05) demonstrate that the proposed DS-SGCN framework achieves encouraging performance on the skeleton-based action recognition task.

A Stretchable Strain Sensor Based on CNTs/GR for Human Motion Monitoring

NANO ◽  
2022 ◽  
Author(s):  
Delin Chen ◽  
Hongmei Zhao ◽  
Weidong Yang ◽  
Dawei Wang ◽  
Xiaowei Huang ◽  
...  
Keyword(s):  
Strain Sensor ◽  
Human Movement ◽  
Human Motion ◽  
Strain Sensors ◽  
Quick Response ◽  
Practical Applications ◽  
Sensing Range ◽  
Human Monitoring ◽  
Human Motion Monitoring ◽  
Smart Wearable

Flexible/stretchable strain sensors have attracted much attention due to their advantages for human-computer interaction, smart wearable and human monitoring. However, there are still great challenges on gaining super durability, quick response, and wide sensing range. This paper provides a simple process to obtain a sensor which is based on graphene (GR)/carbon nanotubes (CNTs) and Ecoflex hybrid, which demonstrates superb endurance (over 1000 cycles at 100% strain), remarkable sensitivity (strain over 125% sensitivity up to 20) and wide sensing range (175%). All results indicate that it is capable for human movement monitoring, such as finger and knee bending and pulse beat. Most importantly, it can be used as a warning function for the night cyclist’s ride. This research provides the feasibility of using this sensor for practical applications.

INTERACTIVE HUMAN POSE AND ACTION RECOGNITION USING DYNAMICAL MOTION PRIMITIVES

2007 ◽  
Vol 04 (02) ◽  
pp. 365-385 ◽  
Author(s):  
ODEST CHADWICKE JENKINS ◽  
GERMÁN GONZÁLEZ SERRANO ◽  
MATTHEW M. LOPER
Keyword(s):  
Decision Making ◽  
Action Recognition ◽  
Human Performance ◽  
Robot Vision ◽  
Human Movement ◽  
Human Motion ◽  
Movement Speed ◽  
Movement Dynamics ◽  
Human Pose ◽  
Socially Interactive Robots

There is currently a division between real-world human performance and the decision making of socially interactive robots. This circumstance is partially due to the difficulty in estimating human cues, such as pose and gesture, from robot sensing. Towards bridging this division, we present a method for kinematic pose estimation and action recognition from monocular robot vision through the use of dynamical human motion vocabularies. Our notion of a motion vocabulary is comprised of movement primitives that structure a human's action space for decision making and predict human movement dynamics. Through prediction, such primitives can be used to both generate motor commands for specific actions and perceive humans performing those actions. In this paper, we focus specifically on the perception of human pose and performed actions using a known vocabulary of primitives. Given image observations over time, each primitive infers pose independently using its expected dynamics in the context of a particle filter. Pose estimates from a set of primitives inferencing in parallel are arbitrated to estimate the action being performed. The efficacy of our approach is demonstrated through interactive-time pose and action recognition over extended motion trials. Results evidence our approach requires small numbers of particles for tracking, is robust to unsegmented multi-action movement, movement speed, camera viewpoint and is able to recover from occlusions.

Stretchable, Sensitive, And Environment-Tolerant Ionic Conductive Organohydrogel Reinforced With Cellulose Nanofibers For Human Motion Monitoring

2021 ◽  
Author(s):  
Miao li ◽  
Yutong Yang ◽  
Chengbin Yue ◽  
Yongming Song ◽  
Maurizio Manzo ◽  
...  
Keyword(s):  
Ionic Conductivity ◽  
Cellulose Nanofibers ◽  
Human Movement ◽  
Human Motion ◽  
Gauge Factor ◽  
Joint Movement ◽  
Strain Sensing ◽  
One Pot ◽  
Large Joint ◽  
Human Motion Monitoring

Abstract Conductive hydrogel (CH) strain sensors have made significant progress in wearable electronic products in recent years. However, the use of aqueous solvents as the dispersion medium in CHs largely limits the scope of applications of CHs and impedes the combination of the mechanical properties and ionic conductivity, which is urgently desired to be addressed. Herein, a simple one-pot preparation of anti-freezing, anti-drying ionic CHs with high stretchability (up to 869%), toughness (6.60 MJ/m3), and Young's modulus (0.56 MPa) was proposed. These CHs consist of polyvinyl alcohol, tannic acid, and sodium chloride dispersed in a solvent consisting of glycerol and cellulose nanofiber suspension. The thus-synthesized CHs exhibit good ionic conductivity (~ 0.86 S/m) and strain sensitivity (gauge factor of 8.54). The organohydrogel possesses a sensitive strain sensing capability and a wide-working temperature range (-50°C to 60°C), and good stability (30 d in room-temperature) to detect human movement, such as large (joint movement) and subtle movements (voice in the throat). These advantages allow organohydrogel sensors to show great potential for electronic skin, personal healthcare, and flexible wearable devices.

scholarly journals A REVIEW ON MACHINE LEARNING ALGORITHMS ON HUMAN ACTION RECOGNITION

2017 ◽  
Vol 10 (13) ◽  
pp. 406
Author(s):  
Ankush Rai ◽  
Jagadeesh Kannan R
Keyword(s):  
Action Recognition ◽  
Human Action Recognition ◽  
Human Movement ◽  
Human Action ◽  
Machine Learning Algorithms ◽  
Automated Recognition ◽  
Computer Interfaces ◽  
Human Computer Interfaces ◽  
Movement Recognition ◽  
Open Datasets

Human action recognition is a vital field of computer vision research. Its applications incorporate observation frameworks, patient monitoring frameworks, and an assortment of frameworks that include interactions between persons and electronic gadgets, for example, human-computer interfaces. The vast majority of these applications require an automated recognition of abnormal or anomalistic action states, made out of various straightforward (or nuclear) actions of persons. This study gives an overview of different best in class research papers on human movement recognition. Open datasets intended for the assessment of the recognition procedures are also discussed in this paper too, for comparing results of several methodologies on this datasets. We examine both the approaches produced for basic human actions and those for abnormal action states. These methodologies are taxonomically classified based on looking at the points of interest and constraints of every methodology. Space-time volume approaches and sequential methodologies that represent actions and perceive such action sets straightforwardly from images are discussed. Next, hierarchical recognition approaches for abnormal action states are introduced and looked at. Statistics based methodologies, syntactic methodologies, and description based methodologies for hierarchical recognition is examined in the paper.

Movement Analysis of Philippine Folk Dance Itik-itik

2019 ◽  
Vol 2 (1) ◽  
pp. 56-63
Author(s):  
Pangilinan Math C ◽  
Fontanilla Lyndo V ◽  
Pineda Israel C ◽  
Rocelle E Agtang ◽  
Soriano Ria M ◽  
...  
Keyword(s):  
Movement Analysis ◽  
Adductor Muscle ◽  
Mechanical Analysis ◽  
Muscle Group ◽  
Upper Body ◽  
Folk Dance ◽  
Dance Movement ◽  
Dance Teacher ◽  
Hip Muscle ◽  
Dance Movements

The purpose of the study was to describe and analyze the dance movements of the Philippine folk dance Itik-itik. The researchers adopted the movement analysis method similar to that of Mackenzie that involves the (1) description of the actual movements which occur at the joints involved; (2) the plane in which the movement occurs; and (3) the muscles producing the movement (agonist & antagonist). In addition, similar to the study of Martin and Miller, the researchers also had done a mechanical analysis on the lever type involved in the execution of the dance movement in terms of force, axis, and resistance. Results revealed that the prominent dance steps in the Philippine local dance Itik-itik are the (1) running, (2) cross step, slide close, slide close step, (3) heel, close-ball, close arm, (4) step, slide-close, slide, (5) arms extension/flexion, and (6) flapping of the arms. The joints involved are the shoulder and hip muscle which are ball and socket type of joints; and elbow, knee and ankle which are hinge joints. The major muscles involved in the dance for the lower body include the quadriceps, hamstring muscle group, adductor muscle group, calves and gluts. While for the upper body muscles involved are the pectoralis major, latissimus dorsi, deltoid, trapezius, biceps, and triceps muscles. The type of lever used in performing the dance comprise majority of 1st class and 3rd class levers. By knowing the muscles involved in the dance the dance teacher may be able to devise activities to gradually prepare the prime mover muscles before the actual execution for injury prevention. Thus, the movements in the dance may improve the health and skill related fitness of the performers.

CNT-Br/PEDOT:PSS/PAAS three-network composite conductive hydrogel for human motion monitoring

2021 ◽  
Vol 45 (1) ◽  
pp. 208-216
Author(s):  
Zhonghua Zhao ◽  
Xiang Yuan ◽  
Yicheng Huang ◽  
Jikui Wang
Keyword(s):  
Human Motion ◽  
Conductive Hydrogel ◽  
Conductive Hydrogels ◽  
Human Motion Monitoring

Conductive hydrogels are promising flexible conductors for human motion monitoring.

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