Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Wireless monitoring system for fireman's competence objective assessment
Politechnika Gdanska, POL.
BetterSolutions S.A., POL.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.
Show others and affiliations
2017 (English)In: Elektronika ir Elektrotechnika, ISSN 1392-1215, Vol. 23, no 4, p. 56-62Article in journal (Refereed) Published
Abstract [en]

Developing technologies associated with tracking human movement and behaviour enable new applications for competence assessments from training results of professionals, such as medical staff, sportsmen or emergency servicemen. This article considers a methodological approach to design a system for firefighter's skills and competence assessment. Assessed training features such as in-building behaviour and tasks execution are analysed based on data gathered with wireless Ultra-Wideband Real-Time Location System, UWB RTLS, and Inertial Measurement Unit, IMU. The assessment is based on the predefined required training tasks, the expert's expertise and results of the trainee's test. The Unity game engine is used for data processing and visualization. As the comprehensive final map of the trainee's skills, the spider diagram is applied and the single score method provides the conclusive statement. The proposed solution was verified experimentally in real environment.

Place, publisher, year, edition, pages
Kauno Technologijos Universitetas , 2017. Vol. 23, no 4, p. 56-62
Keywords [en]
Inertial measurement unit, Real-time location system, Spider diagram, Tracking, Training quality assessment, Wireless multi-sensor system
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-15069DOI: 10.5755/j01.eie.23.4.18723ISI: 000407181800010Scopus ID: 2-s2.0-85027121091OAI: oai:DiVA.org:bth-15069DiVA, id: diva2:1136081
Available from: 2017-08-25 Created: 2017-08-25 Last updated: 2020-10-20Bibliographically approved
In thesis
1. Detection and Classification Multi-sensor Systems: Implementation of IoT and Systematic Design Approaches
Open this publication in new window or tab >>Detection and Classification Multi-sensor Systems: Implementation of IoT and Systematic Design Approaches
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection and classification of features or properties, which characterize people, things or even events can be done in reliable way due to the development of new technologies such as Internet of Things (IoT), and also due to advances in Artificial Intelligence (AI) and machine learning algorithms. Interconnection of users with sensors and actuators have become everyday reality and IoT, an advanced notation of a Multi-sensor System, has become an integral part of systems for assessment of people’s habits and skills as well as the evaluation of quality of things or events' performances. The assessment approach presented in this thesis could be understood as an evaluation of multidimensional fuzzy quantities, which lack standards or references.

The main objective of this thesis is systematical design of multi-sensor systems for industrial and behavioral applications. The systematization is based on User Oriented Design (UOD), the methodology where stakeholders and future users are actively involved in all steps of the development process. An impact of the application environment on design principles is quantitatively and qualitatively analyzed. It shows different design approaches, which can be used for developing systems monitoring human activities or industrial processes.

The features identification approach applied in this thesis involves the extraction of the necessary data, which could be used for behavior classification or skills assessment. The data used for these purposes are vision or radio-based localization and orientation combined with measurement data of speed, acceleration, execution time or the remaining energy level.

Background removal, colour segmentation, Canny filtering and Hough Transform are the algorithms used in vision applications presented in the thesis. In cases of radio-based solutions the methods of angle of arrival, time difference of arrival and pedestrian dead reckoning were utilized. The applied classification and assessment methods were based on AI with algorithms such as decision trees, support vector machines and k-nearest neighborhood.

The thesis proposes a graphical methodology for visualization and assessment of multidimensional fuzzy quantities, which facilitate assessor's conceptualization of strengths and weaknesses in a person's skills or abilities. Moreover, the proposed method can be concluded as a single number or score useful for the evaluation of skills improvement during of training.

The thesis is divided into two parts. The first part, Prolegomena, shows the technical background, an overview of applied theories along with research and design methods related to systems for identification and classification of people’s habits and skills as well as assessing the quality of things or performances. Moreover, this part shows relationships among the papers constituting the second part titled Papers, which includes six reformatted papers published in peer reviewed journals. All the papers concern the design of IoT systems for industrial and behavioral applications.

 

Place, publisher, year, edition, pages
Karlskrona: Blekinge Tekniska Högskola, 2020. p. 236
Series
Blekinge Institute of Technology Doctoral Dissertation Series, ISSN 1653-2090 ; 2020:10
Keywords
Assessment, Behavior Recognition, Classification, Design Methodology, Detection, Indoor Localization, Internet of Things, Multi-Sensor System, Skills Assessment, Outdoor Localization, Wireless Sensor Network
National Category
Signal Processing
Research subject
Applied Signal Processing
Identifiers
urn:nbn:se:bth-20566 (URN)978-91-7295-410-6 (ISBN)
Public defence
2020-12-04, J1630, Campus Gräsvik, Karlskrona, 13:00 (English)
Opponent
Supervisors
Available from: 2020-10-21 Created: 2020-10-20 Last updated: 2020-12-14Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Kulesza, Wlodek

Search in DiVA

By author/editor
Bork-Ceszlak, KrzysztofZydanowicz, TadeuszKulesza, Wlodek
By organisation
Department of Applied Signal Processing
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 203 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf