Orientation estimation in mobile virtual environments with inertial sensors

Lee, Bhoram; Bang, Won-Chul; Kim, James D. K.; Kim, Chang Yeong

doi:10.1109/tce.2011.5955225

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Hence, suitable lightweight sensors are needed to instrument the body to provide user data pertaining to user activities in daily life environments. Nowadays, there is a range of sensor based human posture and activity detection research, such as [ 13 , 14 , 22 , 33 , 37 – 40 ]. In this research, data from normal behaviour can be gathered from wearable sensors.…”

Section: Related Workmentioning

confidence: 99%

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

Zhang

Poslad

2013

Sensors

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Wearable and accompanied sensors and devices are increasingly being used for user activity recognition. However, typical GPS-based and accelerometer-based (ACC) methods face three main challenges: a low recognition accuracy; a coarse recognition capability, i.e., they cannot recognise both human posture (during travelling) and transportation mode simultaneously, and a relatively high computational complexity. Here, a new GPS and Foot-Force (GPS + FF) sensor method is proposed to overcome these challenges that leverages a set of wearable FF sensors in combination with GPS, e.g., in a mobile phone. User mobility activities that can be recognised include both daily user postures and common transportation modes: sitting, standing, walking, cycling, bus passenger, car passenger (including private cars and taxis) and car driver. The novelty of this work is that our approach provides a more comprehensive recognition capability in terms of reliably recognising both human posture and transportation mode simultaneously during travel. In addition, by comparing the new GPS + FF method with both an ACC method (62% accuracy) and a GPS + ACC based method (70% accuracy) as baseline methods, it obtains a higher accuracy (95%) with less computational complexity, when tested on a dataset obtained from ten individuals.

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Section: Related Workmentioning

confidence: 99%

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

Zhang

Poslad

2013

Sensors

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“…The attitude determination results are often later processed by complementary and Kalman filters [15]- [17]f o r more smooth estimates. This requires a later loosely coupled fusion with gyroscope [18]. Generally speaking, to achieve the goal of extremely low power consumption, one attitude estimation algorithm should have at least 1) Energy-saving sensors; 2) Time-costly measurement equations; 3) Simple and explicit solutions.…”

Section: Introductionmentioning

confidence: 99%

A Super Fast Attitude Determination Algorithm for Consumer-Level Accelerometer and Magnetometer

Zhou

Fourati

et al. 2018

IEEE Trans. Consumer Electron.

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A super fast attitude solution is obtained for consumer electronics accelerometer-magnetometer combination. The quaternion parameterizing the orientation is analytically derived from a least-square optimization that maintains very simple form. Like previously developed approaches, this algorithm does not require predetermined magnetometer reference vector. It has been proven in the paper that the proposed algorithm is equivalent to two recent representative methods. Computational complexity analysis shows that the proposed algorithm has the least floating-number operations. Comparisons with recent and classical methods indicate the definite superiority of the proposed algorithm on execution time in real embedded applications.

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“…This method is nearly twice as accurate as the methods based on low-pass filtering of accelerometer signals. In [21], a three-axis accelerometer and a dual-axis angular rate sensor are utilized for orientation sensing in mobile virtual environments. It presents a technical and theoretical description for implementing an orientation-aware device, which is used for navigating large images spread out on a virtual hemispheric space in front of the user through a mobile display.…”

Section: Introductionmentioning

confidence: 99%

Hand Motion-Based Remote Control Interface with Vibrotactile Feedback for Home Robots

Qiao

Zhang

et al. 2013

International Journal of Advanced Robotic Systems

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This paper presents the design and implementation of a hand-held interface system for the locomotion control of home robots. A handheld controller is proposed to implement hand motion recognition and hand motion-based robot control. The handheld controller can provide a 'connect-and-play' service for the users to control the home robot with visual and vibrotactile feedback. Six natural hand gestures are defined for navigating the home robots. A three-axis accelerometer is used to detect the hand motions of the user. The recorded acceleration data are analysed and classified to corresponding control commands according to their characteristic curves. A vibration motor is used to provide vibrotactile feedback to the user when an improper operation is performed. The performances of the proposed hand motion-based interface and the traditional keyboard and mouse interface have been compared in robot navigation experiments. The experimental results of home robot navigation show that the success rate of the handheld controller is 13.33% higher than the PC based controller. The precision of the handheld controller is 15.4% more than that of the PC and the execution time is 24.7% less than the PC based controller. This means that the proposed hand motion-based interface is more efficient and flexible.

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Orientation estimation in mobile virtual environments with inertial sensors

Cited by 7 publications

References 16 publications

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

Design and Test of a Hybrid Foot Force Sensing and GPS System for Richer User Mobility Activity Recognition

A Super Fast Attitude Determination Algorithm for Consumer-Level Accelerometer and Magnetometer

Hand Motion-Based Remote Control Interface with Vibrotactile Feedback for Home Robots

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