Fast human body 3D data acquisition based on multi body sensor

2016 ◽  
Author(s):  
Renqi Yuan ◽  
Zengbo Xu
Keyword(s):  
Data Acquisition ◽  
Human Body ◽  
3D Data ◽  
Multi Body

3D City Modeling and Visualization for Smart Phone Applications

2012 ◽  
pp. 254-296
Author(s):  
Juha Hyyppä ◽  
Lingli Zhu ◽  
Zhengjun Liu ◽  
Harri Kaartinen ◽  
Anttoni Jaakkola
Keyword(s):  
Data Acquisition ◽  
Laser Scanning ◽  
3D Models ◽  
Image Capture ◽  
3D City Models ◽  
Central Computer ◽  
3D Data ◽  
Open Operation ◽  
City Modeling ◽  
City Models

Smartphones with larger screens, powerful processors, abundant memory, and an open operation system provide many possibilities for 3D city or photorealistic model applications. 3D city or photorealistic models can be used by the users to locate themselves in the 3D world, or they can be used as methods for visualizing the surrounding environment once a smartphone has already located the phone by other means, e.g. by using GNSS, and then to provide an interface in the form of a 3D model for the location-based services. In principle, 3D models can be also used for positioning purposes. For example, matching of images exported from the smartphone and then registering them in the existing 3D photorealistic world provides the position of the image capture. In that process, the central computer can do a similar image matching task when the users locate themselves interactively into the 3D world. As the benefits of 3D city models are obvious, this chapter demonstrates the technology used to provide photorealistic 3D city models and focus on 3D data acquisition and the methods available in 3D city modeling, and the development of 3D display technology for smartphone applications. Currently, global geoinformatic data providers, such as Google, Nokia (NAVTEQ), and TomTom (Tele Atlas), are expanding their products from 2D to 3D. This chapter is a presentation of a case study of 3D data acquisition, modeling and mapping, and visualization for a smartphone, including an example based on data collected by mobile laser scanning data from the Tapiola (Espoo, Finland) test field.

Immersive Image Mining in Cardiology

2011 ◽  
pp. 586-592
Author(s):  
Xiaoqiang Liu ◽  
Henk Koppelaar ◽  
Ronald Hamers ◽  
Nico Bruining
Keyword(s):  
Heart Failure ◽  
Virtual Reality ◽  
Data Acquisition ◽  
Human Body ◽  
Human Heart ◽  
Image Mining ◽  
Computational Sciences ◽  
Direct Inspection

Buried within the human body, the heart prohibits direct inspection, so most knowledge about heart failure is obtained by autopsy (in hindsight). Live immersive inspection within the human heart requires advanced data acquisition, image mining and virtual reality techniques. Computational sciences are being exploited as means to investigate biomedical processes in cardiology.

Evaluation of Human Body Dynamical Behaviour During Handling Maneuvers and Crash Test Simulations Using Multi-Body Codes

2006 ◽  
Author(s):  
Francesco Braghin ◽  
Paolo Pennacchi ◽  
Edoardo Sabbioni
Keyword(s):  
Rigid Body ◽  
Human Body ◽  
Dynamical Behaviour ◽  
Crash Test ◽  
Frontal Crash ◽  
Race Car ◽  
Body Dynamics ◽  
Multi Body ◽  
Vehicle Chassis ◽  
Crash Tests

The dynamic behavior of the human body during race car maneuvers and frontal crash tests is analyzed in this paper. Both the vehicle and the human body have been modeled using the multi-body approach. Two commercial codes, BRG LifeMOD Biomechanics Modeler®, for the simulation of the human body dynamics, and MSC ADAMS/Car® for the modeling of the vehicle behavior, have been used for the purpose. Due to the impossibility of co-simulating, at first the accelerations on the driver’s chassis are determined using the vehicle’s multibody code and approximating the driver as a rigid body. Then, the calculated accelerations are applied to the vehicle chassis in the biomechanics code to assess the accelerations in various significant points on the driver.

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