Position estimation with a low-cost inertial measurement unit

Llorach, Gerard; Evans, Alun; Agenjo, Javi; Blat, Josep

doi:10.1109/cisti.2014.6877049

Cited by 9 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, positioning in indoor environment is even more demanding on accuracy and GNSS receivers are commonly not able to estimate position in these environments. Currently there has been a lot of different systems proposed to solve indoor positioning problem, based on radio networks [3][4][5][6], inertial measurements [7,8], image processing [9], or magnetic measurements [10].…”

Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

View full text Add to dashboard Cite

Recently positioning services are getting more attention not only within research community but also from service providers. From the service providers point of view positioning service that will be able to work seamlessly in all environments, for example, indoor, dense urban, and rural, has a huge potential to open new markets. However, such system does not only need to provide accurate position estimates but have to be scalable and resistant to fake positioning requests. In the previous works we have proposed a modular system, which is able to provide seamless positioning in various environments. The system automatically selects optimal positioning module based on available radio signals. The system currently consists of three positioning modules—GPS, GSM based positioning, and Wi-Fi based positioning. In this paper we will propose algorithm which will reduce time needed for position estimation and thus allow higher scalability of the modular system and thus allow providing positioning services to higher amount of users. Such improvement is extremely important, for real world application where large number of users will require position estimates, since positioning error is affected by response time of the positioning server.

show abstract

Section: Introductionmentioning

confidence: 99%

Scalability Optimization of Seamless Positioning Service

Machaj¹,

Brída²,

Benikovsky³

2016

Mobile Information Systems

View full text Add to dashboard Cite

show abstract

“…However, the optical approach is very expensive and has time and space limitations in the test. Other previous studies have applied the wearable sensors based on microelectromechanical systems (MEMS) for motion detection [ 9 , 16 , 17 , 18 , 19 ]. They used the sensor signals to measure the temporal parameters, explored various abnormalities prior to formal assessment, or investigated physical activity levels in children and youth with ID [ 24 ].…”

Section: Related Workmentioning

confidence: 99%

“…Detailed performance parameters or locomotor patterns cannot be objectively measured using manual scoring and the reports are not objective and automatic. Therefore, wearable devices have been adopted [ 9 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Most wearable devices contain a triaxial accelerometer and a gyroscope. The accelerometer measures the acceleration of an object along its movement trajectory, and the gyroscope measures an object’s rotational angular velocity around the roll, pitch, and yaw axes [ 18 ]. These sensors can be mounted on different parts of the body to measure the movement of the limbs or the trunk without excessive restrictions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Development of a Smart Ball to Evaluate Locomotor Performance: Application in Adolescents with Intellectual Disabilities

Pandelaki

et al. 2020

Sensors

View full text Add to dashboard Cite

Adolescents with intellectual disabilities display maladaptive behaviors in activities of daily living because of physical abnormalities or neurological disorders. These adolescents typically exhibit poor locomotor performance and low cognitive abilities in moving the body to perform tasks (e.g., throwing an object or catching an object) smoothly, quickly, and gracefully when compared with typically developing adolescents. Measuring movement time and distance alone does not provide a complete picture of the atypical performance. In this study, a smart ball with an inertial sensor embedded inside was proposed to measure the locomotor performance of adolescents with intellectual disabilities. Four ball games were designed for use with this smart ball: two lower limb games (dribbling along a straight line and a zigzag line) and two upper limb games (picking up a ball and throwing-and-catching). The results of 25 adolescents with intellectual disabilities (aged 18.36 ± 2.46 years) were compared with the results of 25 typically developing adolescents (aged 18.36 ± 0.49 years) in the four tests. Adolescents with intellectual disabilities exhibited considerable motor-performance differences from typically developing adolescents in terms of moving speed, hand–eye coordination, and object control in all tests.

show abstract

“…The visual results will feel like as if it was in 3D and the objects in the 2D image will seem interactive. In the mainstream VR systems such as HTC Vive and Oculus Rift external sensors are used for navigation and virtual movements [24]. Multiple long range IR sensors, showed in Figure 1, are used track the movements of the wearer of the VR headset.…”

Section: Literature Reviewmentioning

confidence: 99%

3D visualization of 2D/360° image and navigation in virtual reality through motion processing via smartphone sensors

Alam¹,

Aurini²,

Islam³

2019

View full text Add to dashboard Cite

The 360° images or regular 2D images look appealing in Virtual Reality yet they fail to represent depth and how the depth can be used to give an experience to the user from two dimensional images. We proposed an approach for creating stereogram from computer generated depth map using approximation algorithm and later use these stereo pairs for giving a complete experience on VR along with forward and backward navigation using mobile sensors. Firstly the image is being segmented into two images from which we generated our disparity map and afterwards generate the depth image from it. After the creation of the depth image, stereo pair which is the left and right image for the eyes were created. Acquired image from the previous process then handled by Cardboard SDK for VR support used in the Android devices using Google Cardboard headset. With the VR image in the stereoscopic device, we use the accelerometer sensor of the device to determine the movement of the device while head mounted. Unlike the other VR navigation systems offered (HTC Vibe, Oculus) using external sensors, our approach is to use the built-in sensors for motion processing. Using the accelerometer reading from the movement, the user will be able to move around virtually in the constructed image. The results of this experiment are the visual changes of the image displayed in VR according to the viewer's physical movement.

show abstract

Position estimation with a low-cost inertial measurement unit

Cited by 9 publications

References 6 publications

Scalability Optimization of Seamless Positioning Service

Scalability Optimization of Seamless Positioning Service

Development of a Smart Ball to Evaluate Locomotor Performance: Application in Adolescents with Intellectual Disabilities

3D visualization of 2D/360° image and navigation in virtual reality through motion processing via smartphone sensors

Contact Info

Product

Resources

About

Position estimation with a low-cost inertial measurement unit

Cited by 9 publications

References 6 publications

Scalability Optimization of Seamless Positioning Service

Scalability Optimization of Seamless Positioning Service

Development of a Smart Ball to Evaluate Locomotor Performance: Application in Adolescents with Intellectual Disabilities

3D visualization of 2D/360&#xB0; image and navigation in virtual reality through motion processing via smartphone sensors

Contact Info

Product

Resources

About

3D visualization of 2D/360° image and navigation in virtual reality through motion processing via smartphone sensors