scholarly journals Real-Time Human Motion Capture with Multiple Depth Cameras

2016 ◽  
Author(s):  
Alireza Shafaei ◽  
James J. Little
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Human Motion ◽  
Depth Cameras ◽  
Human Motion Capture

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.

Design of Multi-User Aerobics Wireless Human Motion Capture System Based on MEMS

2013 ◽  
Vol 650 ◽  
pp. 518-522
Author(s):  
Juan Xiao
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Large Scale ◽  
Three Dimensional ◽  
Human Motion ◽  
Motion Capture System ◽  
Human Motion Capture ◽  
Large Scale Data ◽  
Core Technology ◽  
Real Time Transmission

Main characteristics of recent human motion capture systems are analyzed in the paper firstly. Based on that, a new multi-user aerobics wireless human motion capture system based on MEMS is proposed. Design of its framework and core technology solutions including large-scale data obtain, multi-hop wireless sensor and high-frequency real-time transmission are put forward. Finally, three-dimensional real-time reconstructions of the multi-user aerobics wireless motion capture system are showed in the paper.

scholarly journals Real-Time Human Motion Capture Driven by a Wireless Sensor Network

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Peng-zhan Chen ◽  
Jie Li ◽  
Man Luo ◽  
Nian-hua Zhu
Keyword(s):  
Real Time ◽  
Sensor Network ◽  
Motion Capture ◽  
Human Motion ◽  
Human Model ◽  
Human Skeleton ◽  
Inertial Sensing ◽  
Skeleton Model ◽  
Human Motion Capture ◽  
Motion Capture Systems

The motion of a real object model is reconstructed through measurements of the position, direction, and angle of moving objects in 3D space in a process called “motion capture.” With the development of inertial sensing technology, motion capture systems that are based on inertial sensing have become a research hot spot. However, the solution of motion attitude remains a challenge that restricts the rapid development of motion capture systems. In this study, a human motion capture system based on inertial sensors is developed, and the real-time movement of a human model controlled by real people’s movement is achieved. According to the features of the system of human motion capture and reappearance, a hierarchical modeling approach based on a 3D human body model is proposed. The method collects articular movement data on the basis of rigid body dynamics through a miniature sensor network, controls the human skeleton model, and reproduces human posture according to the features of human articular movement. Finally, the feasibility of the system is validated by testing of system properties via capture of continuous dynamic movement. Experiment results show that the scheme utilizes a real-time sensor network-driven human skeleton model to achieve the accurate reproduction of human motion state. The system also has good application value.

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