Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Otim, Timothy; Díez, Luis Enrique; Bahillo, Alfonso; López-Iturri, Peio; Falcone, Francisco

doi:10.3390/electronics8111351

Cited by 25 publications

(25 citation statements)

References 46 publications

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“…To determine the additional influence of a body in a more realistic situation, another measurement was performed in an indoor environment at the IIoT lab in Ghent. There has already been research towards the human body impact on UWB radiation [25,26,[41][42][43] and for the best body placement of a tag [18,44].…”

Section: Body and Materials Influence On The Position Accuracymentioning

confidence: 99%

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

Vleugels

Herbruggen

Fontaine

et al. 2021

Sensors

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Currently, gathering statistics and information for ice hockey training purposes mostly happens by hand, whereas the automated systems that do exist are expensive and difficult to set up. To remedy this, in this paper, we propose and analyse a wearable system that combines player localisation and activity classification to automatically gather information. A stick-worn inertial measurement unit was used to capture acceleration and rotation data from six ice hockey activities. A convolutional neural network was able to distinguish the six activities from an unseen player with a 76% accuracy at a sample frequency of 100 Hz. Using unseen data from players used to train the model, a 99% accuracy was reached. With a peak detection algorithm, activities could be automatically detected and extracted from a complete measurement for classification. Additionally, the feasibility of a time difference of arrival based ultra-wideband system operating at a 25 Hz update rate was determined. We concluded that the system, when the data were filtered and smoothed, provided acceptable accuracy for use in ice hockey. Combining both, it was possible to gather useful information about a wide range of interesting performance measures. This shows that our proposed system is a suitable solution for the analysis of ice hockey.

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Section: Body and Materials Influence On The Position Accuracymentioning

confidence: 99%

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

Vleugels

Herbruggen

Fontaine

et al. 2021

Sensors

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show abstract

“…Recent studies have been concentrated on visual and non-visual perception systems to recognize human actions [9]. One method amongst non-visual approaches consists of using wearable devices [10][11][12][13][14][15]. Nevertheless, applying this technology as a possible solution for an industrial situation seems at present neither feasible nor comfortable in industrial environments because of restrictions that it will impose on the operator's movements.…”

Section: Human Action Recognition (Har)mentioning

confidence: 99%

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

Amin

Rezayati

Venn

et al. 2020

Sensors

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Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human–robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

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“…We visualized the CRB given by (5), which sets a lower bound for the MSE of our estimation problem proposed in Section 2. We selected two representative positions as test points according to the square root of the trace of CRB matrix.…”

Section: Visualization Of Cramér-rao Bound and Selection Of Testing Pmentioning

confidence: 99%

“…During the deployment of powerful location-based applications such as enhanced 911 services, asset management, and workflow automation, wireless location is a classical problem and has attracted intensive research recently [1][2][3][4][5][6]. Technically, wireless location can be based on a variety of different kinds of measurements, such as range [7], angle [8], energy [9], visible light [10,11], or fingerprinting [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A Semidefinite Relaxation Method for Elliptical Location

2020

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Wireless location is a supporting technology in many application scenarios of wireless communication systems. Recently, an increasing number of studies have been conducted on range-based elliptical location in a variety of backgrounds. Specifically, the design and implementation of position estimators are of great significance. The difficulties arising from implementing a maximum likelihood estimator for elliptical location come from the nonconvexity of the negative log-likelihood functions. The need for computational efficiency further enhances the difficulties. Traditional algorithms suffer from the problems of high computational cost and low initialization justifiability. On the other hand, existing closed-form solutions are sensitive to the measurement noise levels. We recognize that the root of these drawbacks lies in an oversimplified linear approximation of the nonconvex model, and accordingly design a maximum likelihood estimator through semidefinite relaxation for elliptical location. We relax the elliptical location problems to semidefinite programs, which can be solved efficiently with interior-point methods. Additionally, we theoretically analyze the complexity of the proposed algorithm. Finally, we design and carry out a series of simulation experiments, showing that the proposed algorithm outperforms several widely used closed-form solutions at a wide range of noise levels. Extensive results under extreme noise conditions verify the deployability of the algorithm.

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Effects of the Body Wearable Sensor Position on the UWB Localization Accuracy

Cited by 25 publications

References 46 publications

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

Ultra-Wideband Indoor Positioning and IMU-Based Activity Recognition for Ice Hockey Analytics

A Mixed-Perception Approach for Safe Human–Robot Collaboration in Industrial Automation

A Semidefinite Relaxation Method for Elliptical Location

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