Timing estimation for gait in water from inertial sensor measurements: Analysis of the performance of 17 algorithms

2020 ◽  
Vol 197 ◽  
pp. 105703
Author(s):  
Giulia Pacini Panebianco ◽  
Maria Cristina Bisi ◽  
Rita Stagni ◽  
Silvia Fantozzi
Keyword(s):  
Inertial Sensor ◽  
Timing Estimation

A Novel Timing Estimation Method for Chirp-Based Systems

2011 ◽  
Vol E94-B (12) ◽  
pp. 3607-3609
Author(s):  
Sanghun YOON ◽  
Dae-Gun OH ◽  
Jong-Wha CHONG ◽  
Tae Moon ROH ◽  
Jong-Kee KWON ◽  
...  
Keyword(s):  
Estimation Method ◽  
Timing Estimation

Vision and Inertial Sensor Fusion for Terrain Relative Navigation

2021 ◽  
Author(s):  
Andrew Verras ◽  
Roshan Thomas Eapen ◽  
Andrew B. Simon ◽  
Manoranjan Majji ◽  
Ramchander Rao Bhaskara ◽  
...  
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
Relative Navigation

scholarly journals Displacement Estimation Based on Optical and Inertial Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1390
Author(s):  
Tomasz Ursel ◽  
Michał Olinski
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor ◽  
External Disturbance ◽  
Linear Displacement ◽  
Measuring System ◽  
Displacement Estimation ◽  
Mems Accelerometer ◽  
Pedestrian Navigation ◽  
Complementary Filter ◽  
Optical Flow Sensor

This article aims to develop a system capable of estimating the displacement of a moving object with the usage of a relatively cheap and easy to apply sensors. There is a growing need for such systems, not only for robots, but also, for instance, pedestrian navigation. In this paper, the theory for this idea, including data postprocessing algorithms for a MEMS accelerometer and an optical flow sensor (OFS), as well as the developed complementary filter applied for sensor fusion, are presented. In addition, a vital part of the accelerometer’s algorithm, the zero velocity states detection, is implemented. It is based on analysis of the acceleration’s signal and further application of acceleration symmetrization, greatly improving the obtained displacement. A test stand with a linear guide and motor enabling imposing a specified linear motion is built. The results of both sensors’ testing suggest that the displacement estimated by each of them is highly correct. Fusion of the sensors’ data gives even better outcomes, especially in cases with external disturbance of OFS. The comparative evaluation of estimated linear displacements, in each case related to encoder data, confirms the algorithms’ operation correctness and proves the chosen sensors’ usefulness in the development of a linear displacement measuring system.

scholarly journals Reference in-vitro dataset for inertial-sensor-to-bone alignment applied to the tibiofemoral joint

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ive Weygers ◽  
Manon Kok ◽  
Thomas Seel ◽  
Darshan Shah ◽  
Orçun Taylan ◽  
...  
Keyword(s):  
Inertial Sensors ◽  
Inertial Sensor ◽  
Ground Truth ◽  
Transformation Matrices ◽  
True Model ◽  
Advance Research ◽  
Manual Palpation ◽  
Visualization Tools ◽  
Inertial Measurements

AbstractSkin-attached inertial sensors are increasingly used for kinematic analysis. However, their ability to measure outside-lab can only be exploited after correctly aligning the sensor axes with the underlying anatomical axes. Emerging model-based inertial-sensor-to-bone alignment methods relate inertial measurements with a model of the joint to overcome calibration movements and sensor placement assumptions. It is unclear how good such alignment methods can identify the anatomical axes. Any misalignment results in kinematic cross-talk errors, which makes model validation and the interpretation of the resulting kinematics measurements challenging. This study provides an anatomically correct ground-truth reference dataset from dynamic motions on a cadaver. In contrast with existing references, this enables a true model evaluation that overcomes influences from soft-tissue artifacts, orientation and manual palpation errors. This dataset comprises extensive dynamic movements that are recorded with multimodal measurements including trajectories of optical and virtual (via computed tomography) anatomical markers, reference kinematics, inertial measurements, transformation matrices and visualization tools. The dataset can be used either as a ground-truth reference or to advance research in inertial-sensor-to-bone-alignment.

Unsupervised Balanced Covariance Learning for Visual-Inertial Sensor Fusion

2021 ◽  
Vol 6 (2) ◽  
pp. 819-826
Author(s):  
Youngji Kim ◽  
Sungho Yoon ◽  
Sujung Kim ◽  
Ayoung Kim
Keyword(s):  
Sensor Fusion ◽  
Inertial Sensor
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