Using Inertial Sensors in Driver Posture Tracking Systems

2018 ◽  
pp. 17-24 ◽  
Author(s):  
Silviu Butnariu ◽  
Gheorghe Mogan ◽  
Csaba Antonya
Keyword(s):  
Inertial Sensors ◽  
Tracking Systems ◽  
Posture Tracking

Design and Implementation of an Improved Real-Time Tracking System for Navigation Surgery by Fusion of Optical and Inertial Tracking Methods

2012 ◽  
Vol 186 ◽  
pp. 273-279 ◽  
Author(s):  
Ali Soroush ◽  
Farzam Farahmand ◽  
Hassan Salarieh
Keyword(s):  
Inertial Sensors ◽  
Tracking System ◽  
Time Integration ◽  
Optical Tracking ◽  
Tracking Systems ◽  
Fusion Algorithm ◽  
Navigation Surgery ◽  
Orientation Data ◽  
Individual System ◽  
Real Time Tracking

The fusion of the optical and inertial tracking systems seems an attractive solution to solve the shadowing problem of the optical tracking systems, and remove the time integration troubles of the inertial sensors. We developed a fusion algorithm for this purpose, based on the Kalman filter, and examined its efficacy to improve the position and orientation data, obtained by each individual system. Experimental results indicated that the proposed fusion algorithm could effectively estimate the 2 seconds missing data of the optical tracker.

scholarly journals Modern Facilities for Experimental Measurement of Dynamic Loads Induced by Humans: A Literature Review

2013 ◽  
Vol 20 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Vitomir Racic ◽  
Aleksandar Pavic ◽  
James Mark William Brownjohn
Keyword(s):  
Motion Tracking ◽  
Inertial Sensors ◽  
Force Plate ◽  
Contact Forces ◽  
Tracking Systems ◽  
Force Measurements ◽  
Foot Pressure ◽  
Engineering Structures ◽  
Optical Motion ◽  
Optical Motion Tracking

This paper provides a critical overview of available technology and facilities for determining human-induced dynamic forces of civil engineering structures, such as due to walking, running, jumping and bouncing. In addition to traditional equipment for direct force measurements comprising force plate(s), foot pressure insoles and instrumented treadmills, the review also investigates possibility of using optical motion tracking systems (marker-based and marker-free optoelectronic technology) and non-optical motion tracking systems (inertial sensors) to reproduce contact forces between humans and structures based on body kinematics data and known body mass distribution. Although significant technological advancements have been made in the last decade, the literature survey showed that the state-of-the-art force measurements are often limited to individuals in artificial laboratory environments. Experimental identification of seriously needed group- and crowd-induced force data recorded on as-built structures, such as footbridges, grandstands and floors, still remains a challenge due to the complexity of human actions and the lack of adequate equipment.

scholarly journals Inertial Tracking Systems for Virtual Reality Devices

2019 ◽  
Vol 9 (1) ◽  
pp. 3988-3994
Keyword(s):  
Virtual Reality ◽  
Motion Capture ◽  
Noise Suppression ◽  
Inertial Sensors ◽  
Head Tracking ◽  
Tracking Systems ◽  
Coordinate Rotation ◽  
3D Positioning

Tracking systems are the most important components in virtual reality devices and appliances. In the simplest case, in order to represent current picture for a user of virtual reality glasses, it is required to provide head tracking. In complex devices of virtual reality, it is also required to track position of device itself and dynamics of its motions. This work is aimed at development of 3D positioning algorithms for virtual reality devices and appliances. In this work positioning is based on operation of inertial sensors. The issues of motion capture and calibration of inertial devices are considered as well as application of Madgwick filter for noise suppression and quaternions for description of coordinate rotation. This work discusses the application of the developed algorithms for various virtual reality devices and appliances.

GPS and GSM Based Tracking System for Objects Moving over Wide Geographical Areas

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Fatima Ameen ◽  
Ziad Mohammed ◽  
Abdulrahman Siddiq
Keyword(s):  
Global Positioning System ◽  
Mobile Communications ◽  
Moving Objects ◽  
Tracking System ◽  
Geographical Area ◽  
Tracking Systems ◽  
Acceptable Result ◽  
Internet Service ◽  
Global Positioning ◽  
Tracking Objects

Tracking systems of moving objects provide a useful means to better control, manage and secure them. Tracking systems are used in different scales of applications such as indoors, outdoors and even used to track vehicles, ships and air planes moving over the globe. This paper presents the design and implementation of a system for tracking objects moving over a wide geographical area. The system depends on the Global Positioning System (GPS) and Global System for Mobile Communications (GSM) technologies without requiring the Internet service. The implemented system uses the freely available GPS service to determine the position of the moving objects. The tests of the implemented system in different regions and conditions show that the maximum uncertainty in the obtained positions is a circle with radius of about 16 m, which is an acceptable result for tracking the movement of objects in wide and open environments.

Deadlift Recognition and Application based on Multiple Modalities using Recurrent Neural Network

2020 ◽  
Vol 2020 (17) ◽  
pp. 2-1-2-6
Author(s):  
Shih-Wei Sun ◽  
Ting-Chen Mou ◽  
Pao-Chi Chang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Network Structure ◽  
Inertial Sensors ◽  
Body Movement ◽  
The Body ◽  
Body Parts ◽  
Multimodal Sensing ◽  
Multiple Devices ◽  
Neural Network Structure

To improve the workout efficiency and to provide the body movement suggestions to users in a “smart gym” environment, we propose to use a depth camera for capturing a user’s body parts and mount multiple inertial sensors on the body parts of a user to generate deadlift behavior models generated by a recurrent neural network structure. The contribution of this paper is trifold: 1) The multimodal sensing signals obtained from multiple devices are fused for generating the deadlift behavior classifiers, 2) the recurrent neural network structure can analyze the information from the synchronized skeletal and inertial sensing data, and 3) a Vaplab dataset is generated for evaluating the deadlift behaviors recognizing capability in the proposed method.

Decoupling of high gain multivariable tracking systems

1983 ◽  
Author(s):  
D. RIDGELY ◽  
S. BANDA ◽  
J. DAZZO
Keyword(s):  
High Gain ◽  
Tracking Systems

Multi-Hypothesis Test Detection for Star Tracking Systems

2021 ◽  
Author(s):  
Jordan T. Kirk ◽  
Stephen c. Cain
Keyword(s):  
Hypothesis Test ◽  
Tracking Systems ◽  
Star Tracking
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