scholarly journals Pedestrian navigation: how can inertial measurment units assist smartphones?

2021 ◽  
Vol 9 (2) ◽  
pp. 22-34
Author(s):  
I. A. Chistyakov ◽  
I. V. Grishov ◽  
A. A. Nikulin ◽  
M. V. Pikhletsky ◽  
I. B. Gartseev
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Time Synchronization ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Pedestrian Navigation ◽  
Navigation Algorithms ◽  
Modified Kalman Filter ◽  
Measurement Units ◽  
Combined Measurements

This paper is devoted to construction of reference walking trajectories for developing pedestrian navigation algorithms for smartphones. Such trajectories can be used both for verification of classical algorithms of navigation or for application of machine learning technics. Reconstruction of closed trajectories based on data from foot-mounted inertial measurement units (IMU) is investigated. The advantages of the approach are the use of inexpensive sensors and the simplicity of the presented method. We propose algorithms for reconstruction of smooth 2D pedestrian trajectories based on measurements from a single IMU as well as on combined measurements from two IMU’s. Introduced algorithms are based on application of modified Kalman filter with an assumption of IMU having zero velocity when foot contacts the ground. In case of two measurement units, it is additionally assumed that the positions of the sensors cannot differ significantly from each other. The algorithms were tested on trajectories lasting from 1 to 10 minutes, passing indoors on horizontal surfaces. Obtained results were compared with high precision trajectories acquired with GNSS RTK receivers. Additionally, the process of inter-device time synchronization is investigated and detailed description of the experiments and used equipment is given. The dataset used for verification of proposed algorithms is freely available at: http://gartseev.ru/projects/rtj2021.

scholarly journals Analysis of ergonomic and unergonomic human lifting behaviors by using Inertial Measurement Units

2017 ◽  
Vol 3 (1) ◽  
pp. 7-10 ◽  
Author(s):  
Jan Kuschan ◽  
Henning Schmidt ◽  
Jörg Krüger
Keyword(s):  
Quality Of Life ◽  
Machine Learning ◽  
Angular Velocity ◽  
Musculoskeletal System ◽  
Occupational Diseases ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Main Components

Abstract:This paper presents an analysis of two distinct human lifting movements regarding acceleration and angular velocity. For the first movement, the ergonomic one, the test persons produced the lifting power by squatting down, bending at the hips and knees only. Whereas performing the unergonomic one they bent forward lifting the box mainly with their backs. The measurements were taken by using a vest equipped with five Inertial Measurement Units (IMU) with 9 Dimensions of Freedom (DOF) each. In the following the IMU data captured for these two movements will be evaluated using statistics and visualized. It will also be discussed with respect to their suitability as features for further machine learning classifications. The reason for observing these movements is that occupational diseases of the musculoskeletal system lead to a reduction of the workers’ quality of life and extra costs for companies. Therefore, a vest, called CareJack, was designed to give the worker a real-time feedback about his ergonomic state while working. The CareJack is an approach to reduce the risk of spinal and back diseases. This paper will also present the idea behind it as well as its main components.

A Simplified Extended Kalman Filter Used for Magnetic and Inertial Measurement Units

2020 ◽  
pp. 1-1
Author(s):  
Hailong Rong ◽  
Yanping Zhu ◽  
Jidong Lv ◽  
Cuiyun Peng ◽  
Ling Zou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

Performance Comparison of Inertial Measurement Units Fused With Odometry in Extended Kalman Filter for Dead-Reckoning Navigation

2019 ◽  
Author(s):  
Kevin Carey ◽  
Benjamin Abruzzo ◽  
David P. Harvie ◽  
Christopher Korpela
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Autonomous Vehicle ◽  
Dead Reckoning ◽  
Ground Truth ◽  
Performance Comparison ◽  
Inertial Measurement Units ◽  
Ground Vehicle ◽  
Inertial Measurement ◽  
Measurement Units

Abstract This paper aims to aid robot and autonomous vehicle designers by providing a comparison between four different inertial measurement units (IMUs) which could be used to aid in vehicle navigation in a GPS-denied or inertial-only scenario. A differential-drive ground vehicle was designed to carry the multiple different IMUs, mounted coaxially, to enable direct comparison of performance in a planar environment. The experiments focused on the growth of pose error of the ground vehicle originating from the odometry senors and the IMUs. An extended Kalman Filter was developed to fuse the odometry and inertial measurements for this comparison. The four specific IMUs evaluated were: CNS 5000, Xsens 300, Microstrain GX5-35, and Phidgets 1044 and the ground truth for experiments was provided by an Optitrack motion capture system (MCS). Finally, metrics for choosing IMUs, merging cost and performance considerations, are proposed and discussed. While the CNS 5000 has the best objective error specifications, based on these metrics the Xsens 300 exhibits the best absolute performance while the Phidgets 1044 provides the best performance-per-dollar.

A machine learning approach to estimate Minimum Toe Clearance using Inertial Measurement Units

2015 ◽  
Vol 48 (16) ◽  
pp. 4309-4316 ◽  
Author(s):  
Braveena K. Santhiranayagam ◽  
Daniel T.H. Lai ◽  
W.A. Sparrow ◽  
Rezaul K. Begg
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Approach ◽  
Measurement Units

Quaternion Kalman filter for inertial measurement units

2017 ◽  
Author(s):  
Jossue Carino Escobar ◽  
Aurelien Cabarbaye ◽  
Moises Bonilla Estrada ◽  
Rogelio Lozano
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

Kalman filter for angle estimation using dual inertial measurement units on unicycle robot

2016 ◽  
Author(s):  
S. Ekti Radin Charel ◽  
Eko Henfri Binugroho ◽  
M. Anfa'ur Rosyidi ◽  
R. Sanggar Dewanto ◽  
Dadet Pramadihanto
Keyword(s):  
Kalman Filter ◽  
Inertial Measurement Units ◽  
Angle Estimation ◽  
Inertial Measurement ◽  
Measurement Units
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