Proceedings ArticleDOI
On single sensor-based inertial navigation
Nicolo Strozzi,Federico Parisi,Gianluigi Ferrari +2 more
- pp 300-305
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TLDR
The main goal of this research is to investigate the peculiarities of different inertial navigation algorithms, in order to highlight the impact of the sensor's placement, together with inertial sensor issues.Citations
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Proceedings ArticleDOI
Improved formulation of the IMU and MARG orientation gradient descent algorithm for motion tracking in human-machine interfaces
TL;DR: The improved efficiency and accuracy show significant potential for increasing the scope of inertial measurement in applications where low power or greater precision is necessary such as very small wearable or implantable systems.
Journal ArticleDOI
Impact of on-body IMU placement on inertial navigation
TL;DR: A comparison between different inertial systems is presented, investigating the impacts of on-body placement of Inertial Measurement Units and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy.
Proceedings ArticleDOI
A multifloor hybrid inertial/barometric navigation system
TL;DR: The inertial (MARG) sub-system, by properly processing the signals collected by the MARG sensors placed on the test subject's feet, reconstructs the two-dimensional navigation pattern by applying a Zero velocity UPdaTe (ZUPT) technique.
Journal ArticleDOI
A Hybrid Framework for Mitigating Heading Drift for a Wearable Pedestrian Navigation System through Adaptive Fusion of Inertial and Magnetic Measurements
Liqiang Zhang,Yu Liu,Jinglin Sun +2 more
TL;DR: In this article, a hybrid framework that integrates a foot-state classifier that triggers the zero-velocity update (ZUPT) algorithm, zero-angular-rate update algorithm, and a state lock, a magnetic disturbance detector, a human-motion-classifier-aided adaptive fusion module (AFM), and an error-state Kalman filter (ESKF) that estimates the optimal systematic error.
Proceedings ArticleDOI
A Novel Step Detection and Step Length Estimation Algorithm for Hand-held Smartphones
TL;DR: An innovative step detection algorithm which is independent of the holding mode, the only assumption being that the device is hand-held and its movement is related to the upper body displacement during walking is proposed.
References
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Proceedings ArticleDOI
Estimation of IMU and MARG orientation using a gradient descent algorithm
TL;DR: This paper presents a novel orientation algorithm designed to support a computationally efficient, wearable inertial human motion tracking system for rehabilitation applications, applicable to inertial measurement units (IMUs) consisting of tri-axis gyroscopes and accelerometers, and magnetic angular rate and gravity sensor arrays that also include tri- axis magnetometers.
Journal ArticleDOI
Assessment of spatio-temporal gait parameters from trunk accelerations during human walking
Wiebren Zijlstra,At L. Hof +1 more
TL;DR: The duration of subsequent stride cycles and left/right steps, and estimations of step length and walking speed can be obtained from lower trunk accelerations, which can be the basis for an analysis of other signals within the stride cycle.
Journal ArticleDOI
WiFi-based indoor positioning
Chouchang Yang,Huai-Rong Shao +1 more
TL;DR: Simulation results show that the WiFi-based positioning approach can achieve 1 m accuracy without any hardware change in commercial WiFi products, which is much better than the conventional solutions from both academia and industry concerning the trade-off of cost and system complexity.
Proceedings ArticleDOI
A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU
TL;DR: This paper uses low-performance Micro-Electro-Mechanical inertial sensors attached to the foot of a person, and describes, implements and compares several of the most relevant algorithms for step detection, stride length, heading and position estimation.
Journal ArticleDOI
Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs
TL;DR: A novel complementary filter for MAVs that fuses together gyroscope data with accelerometer and magnetic field readings and outperforms other common methods, using publicly available datasets with ground-truth data recorded during a real flight experiment of a micro quadrotor helicopter.