Human Body Motion Capture System Based on Inertia Sensing Technology

2013 ◽  
Vol 722 ◽  
pp. 454-458
Author(s):  
Shu Ai Li ◽  
Yong Sheng Wang ◽  
Rui Pai Xiang
Keyword(s):  
Sensor Network ◽  
Motion Capture ◽  
Human Body ◽  
The Body ◽  
Body Motion ◽  
Network Nodes ◽  
Motion Data ◽  
Time Motion ◽  
Sensing Technology ◽  
Reconstruction Software

To solve the bottleneck problem of defining motion trajectory of virtual role in animation creation process, this paper presents a solution of mechanical human body motion capture technology, mainly involving inertia sensing technology, Bluetooth, the design of sensor network nodes and the development of reconstruction software of human body motion model. The system uses sensor network to collect motion data of the body key joints, and the data are delivered to workstation through Bluetooth, the software on workstation uses analytical inverse kinematics algorithm to analyze the motion data. So the system has advantages of lower cost and high precision. Meanwhile, the paper also provides a solid foundation for the research of multiplayer real-time motion capture technology.

Real-Time Motion Capture for a Human Body using Accelerometers

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 601-610 ◽  
Author(s):  
Jihong Lee ◽  
Insoo Ha
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
Human Body ◽  
Degrees Of Freedom ◽  
Low Cost ◽  
Human Motion ◽  
The Body ◽  
Sensor Data ◽  
Time Motion ◽  
Gravity Acceleration

In this paper we propose a set of techniques for a real-time motion capture of a human body. The proposed motion capture system is based on low cost accelerometers, and is capable of identifying the body configuration by extracting gravity-related terms from the sensor data. One sensor unit is composed of 3 accelerometers arranged orthogonally to each other, and is capable of identifying 2 rotating angles of joints with 2 degrees of freedom. A geometric fusion technique is applied to cope with the uncertainty of sensor data. A practical calibration technique is also proposed to handle errors in aligning the sensing axis to the coordination axis. In the case where motion acceleration is not negligible compared with gravity acceleration, a compensation technique to extract gravity acceleration from the sensor data is proposed. Experimental results not only for individual techniques but also for human motion capturing with graphics are included.

scholarly journals Subsecond total-body imaging using ultrasensitive positron emission tomography

2020 ◽  
Vol 117 (5) ◽  
pp. 2265-2267 ◽  
Author(s):  
Xuezhu Zhang ◽  
Simon R. Cherry ◽  
Zhaoheng Xie ◽  
Hongcheng Shi ◽  
Ramsey D. Badawi ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Motion Tracking ◽  
The Body ◽  
Body Motion ◽  
Emission Tomography ◽  
Ct Scanner ◽  
External Monitoring ◽  
Time Motion ◽  
Positron Emission ◽  
Total Body

A 194-cm-long total-body positron emission tomography/computed tomography (PET/CT) scanner (uEXPLORER), has been constructed to offer a transformative platform for human radiotracer imaging in clinical research and healthcare. Its total-body coverage and exceptional sensitivity provide opportunities for innovative studies of physiology, biochemistry, and pharmacology. The objective of this study is to develop a method to perform ultrahigh (100 ms) temporal resolution dynamic PET imaging by combining advanced dynamic image reconstruction paradigms with the uEXPLORER scanner. We aim to capture the fast dynamics of initial radiotracer distribution, as well as cardiac motion, in the human body. The results show that we can visualize radiotracer transport in the body on timescales of 100 ms and obtain motion-frozen images with superior image quality compared to conventional methods. The proposed method has applications in studying fast tracer dynamics, such as blood flow and the dynamic response to neural modulation, as well as performing real-time motion tracking (e.g., cardiac and respiratory motion, and gross body motion) without any external monitoring device (e.g., electrocardiogram, breathing belt, or optical trackers).

Research on the Application of Motion Capture of Kinect Technology in the Sport Training

2014 ◽  
Vol 926-930 ◽  
pp. 2714-2717
Author(s):  
Quan Wei Shi
Keyword(s):  
Real Time ◽  
Motion Capture ◽  
The Body ◽  
Sports Training ◽  
The Real ◽  
Sport Training ◽  
Time Motion ◽  
Development Costs ◽  
Graphics Rendering ◽  
Rendering Engine

For the real-time motion capture in the sport training to analysis and study, this paper adopts Kinect technology and the development of sports training combined with. Kinect somatosensory the camera as the system core, the body movements, facial expressions capture system in development costs, operating results and the development efficiency has the optimal balance point. The purpose of this research is based on the OGRE graphics rendering engine, using 3DSMAX and open source code, the design and implementation of Kinect somatosensory camera and 3DSMAX, OGRE combination of game action, motion capture system based on. This system provides an important help for realizing the real-time motion capture in the sports training, can be used in the field of sports training.

scholarly journals Remo Dance Motion Estimation with Markerless Motion Capture Using The Optical Flow Method

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Neny Kurniati ◽  
Achmad Basuki ◽  
Dadet Pramadihanto
Keyword(s):  
Motion Estimation ◽  
Optical Flow ◽  
Motion Capture ◽  
Motion Detection ◽  
The Body ◽  
Flow Method ◽  
Optical Flow Method ◽  
Time Motion ◽  
Markerless Motion Capture ◽  
Bounding Box

Motion capture has been developed and applied in various fields, one of them is dancing. Remo dance is a dance from East Java that tells the struggle of a prince who fought on the battlefield. Remo dancer does not use body-tight costume. He wears a few costume pieces and accessories, so required a motion detection method that can detect limb motion which does not damage the beauty of the costumes and does not interfere motion of the dancer. The method is Markerless Motion Capture. Limbs motions are partial behavior. This means that all limbs do not move simultaneously, but alternately. It required motion tracking to detect parts of the body moving and where the direction of motion. Optical flow is a method that is suitable for the above conditions. Moving body parts will be detected by the bounding box. A bounding box differential value between frames can determine the direction of the motion and how far the object is moving. The optical flow method is simple and does not require a monochrome background. This method does not use complex feature extraction process so it can be applied to real-time motion capture. Performance of motion detection with optical flow method is determined by the value of the ratio between the area of the blob and the area of the bounding box. Estimate coordinates are not necessarily like original coordinates, but if the chart of estimate motion similar to the chart of the original motion, it means motion estimation it can be said to have the same motion with the original.Keywords: Motion Capture, Markerless, Remo Dance, Optical Flow

scholarly journals Kinematic dataset of actors expressing emotions

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Mingming Zhang ◽  
Lu Yu ◽  
Keye Zhang ◽  
Bixuan Du ◽  
Bin Zhan ◽  
...  
Keyword(s):  
Motion Capture ◽  
Human Body ◽  
The Body ◽  
Body Movements ◽  
Basic Emotions ◽  
Research And Practice ◽  
Information Forensics ◽  
Daily Events ◽  
Body Kinematic ◽  
Neutral Expression

Abstract Human body movements can convey a variety of emotions and even create advantages in some special life situations. However, how emotion is encoded in body movements has remained unclear. One reason is that there is a lack of public human body kinematic dataset regarding the expressing of various emotions. Therefore, we aimed to produce a comprehensive dataset to assist in recognizing cues from all parts of the body that indicate six basic emotions (happiness, sadness, anger, fear, disgust, surprise) and neutral expression. The present dataset was created using a portable wireless motion capture system. Twenty-two semi-professional actors (half male) completed performances according to the standardized guidance and preferred daily events. A total of 1402 recordings at 125 Hz were collected, consisting of the position and rotation data of 72 anatomical nodes. To our knowledge, this is now the largest emotional kinematic dataset of the human body. We hope this dataset will contribute to multiple fields of research and practice, including social neuroscience, psychiatry, computer vision, and biometric and information forensics.

Powering Healthcare IoT Sensors-Based Triboelectric Nanogenerator

2020 ◽  
pp. 29-51
Author(s):  
Saeed Ahmed Khan ◽  
Shamsuddin Lakho ◽  
Ahmed Ali ◽  
Abdul Qadir Rahimoon ◽  
Izhar Hussain Memon ◽  
...  
Keyword(s):  
Human Body ◽  
Pressure Sensors ◽  
Wearable Devices ◽  
Temperature Sensors ◽  
The Body ◽  
Body Motion ◽  
Triboelectric Nanogenerator ◽  
Self Powered ◽  
Fitness Tracking ◽  
Triboelectric Nanogenerators

Most of the emerging electronic devices are wearable in nature. However, the frequent changing or charging the battery of all wearable devices is the big challenge. Interestingly, with those wearable devices that are directly associated with the human body, the body can be used in transferring or generating energy in a number of techniques. One technique is triboelectric nanogenerators (TENG). This chapter covers different applications where the human body is used as a triboelectric layer and as a sensor. Wearable TENG has been discussed in detail based on four basic modes that could be used to monitor the human health. In all the discussions, the main focus is to power the wearable healthcare internet of things (IoT) sensor through human body motion based on self-powered TENG. The IoT sensors-based wearable devices related to human body can be used to develop smart body temperature sensors, pressure sensors, smart textiles, and fitness tracking sensors.

Application of Deep Learning to Develop a Safety Confirmation System for the Elderly in a Nursing Home

2017 ◽  
Vol 29 (2) ◽  
pp. 338-345 ◽  
Author(s):  
Masaru Kawakami ◽  
◽  
Shogo Toba ◽  
Kohei Fukuda ◽  
Shinya Hori ◽  
...  
Keyword(s):  
Nursing Home ◽  
Detection System ◽  
Elderly Care ◽  
Elderly Person ◽  
Bed Rest ◽  
The Elderly ◽  
The Body ◽  
Body Motion ◽  
Motion Data ◽  
System A

[abstFig src='/00290002/07.jpg' width='210' text='The motion detection system' ] Fall accident prevention is one of the most important issues in elderly care settings. To prevent an accident, it is necessary to notify caregivers if the elderly person is getting out of bed. We have previously developed a posture discrimination system based on body motions. Herein, we propose a discrimination method by using machine learning to improve the performance of the system. A purpose of this study is to evaluate the proposed method. Elderly people in a nursing home were chosen as subjects in this study. We analyzed the body motion data during bed rest and bed exit of the subjects using the proposed method. These results suggest that it is effective.

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