Application of Inertial Measurement Units for Advanced Safety Surveillance System Using Individualized Sensor Technology (ASSIST): A Data Fusion and Machine Learning Approach

2018 ◽  
Author(s):  
Amir Baghdadi
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Surveillance System ◽  
Sensor Technology ◽  
Learning Approach ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Safety Surveillance ◽  
Machine Learning Approach ◽  
Measurement Units

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

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.

scholarly journals Data Fusion Algorithms for Multiple Inertial Measurement Units

Sensors ◽  
2011 ◽  
Vol 11 (7) ◽  
pp. 6771-6798 ◽  
Author(s):  
Jared B. Bancroft ◽  
Gérard Lachapelle
Keyword(s):  
Data Fusion ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Measurement Units

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.

Application of Inertial Measurement Units and Machine Learning Classification in Cerebral Palsy (Preprint)

2021 ◽  
Author(s):  
Siavash Khaksar ◽  
Huizhu Pan ◽  
Iain Murray ◽  
Wanquan Liu ◽  
Himanshu Agrawal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cerebral Palsy ◽  
Joint Angle ◽  
Wrist Joint ◽  
Hand Movement ◽  
Machine Learning Algorithms ◽  
Inertial Measurement Units ◽  
Inertial Measurement ◽  
Machine Learning Classification ◽  
Measurement Units

BACKGROUND Cerebral palsy (CP) is a physical disability that affects movement and posture. About 17 million people worldwide and 34000 people in Australia are living with CP. In clinical and kinematic research, goniometers and inclinometers are the most commonly used clinical tools to measure joint angles and position in children with CP. OBJECTIVE This paper presents collaborative research between department of Electrical Engineering and Computing at Curtin University and the investigator team of a multi-centre randomised controlled trial involving children with CP. The main objective of this paper was to develop a digital solution for mass data collection and application of machine learning to classify the movement features associated with CP without the need to measure Euler, Quaternion, and joint measurement calculation and help determine the effectiveness of therapy. METHODS Custom, low-cost Inertial Measurement Units (IMUs) were developed to record the usual wrist movements of participants aged 5 to 15 years old with CP. The IMU data were used to calculate the joint angle of the wrist movement to determine the range of motion. Nine different machine learning algorithms were used to classify the movement features associated with CP. RESULTS Upon completion of the project, the wrist joint angle was successfully calculated, and CP movement was classified as a feature using machine learning on raw IMU data, with Random Forrest algorithm showing the highest accuracy at 85.75%. CONCLUSIONS Anecdotal feedback from MIT researchers were positive about the potential for IMUs to contribute accurate data about active ROM, especially in children for whom goniometric methods are challenging. There may also be potential to use IMUs for continued monitoring of hand movement throughout the day. CLINICALTRIAL The trial is registered with the ANZ Clinical Trials Registry (ACTRN12614001276640).

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