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IoT-based information system for healthcare application: Design methodology approach
Politechnika Gdanska, POL.
BetterSolutions S.A., POL.
Blekinge Institute of Technology, Faculty of Engineering, Department of Applied Signal Processing.ORCID iD: 0000-0003-3262-3221
2017 (English)In: Applied Sciences, E-ISSN 2076-3417, Vol. - 7, no - 6, article id 596Article in journal (Refereed) Published
Abstract [en]

- Over the last few decades, life expectancy has increased significantly. However, elderly people who live on their own often need assistance due to mobility difficulties, symptoms of dementia or other health problems. In such cases, an autonomous supporting system may be helpful. This paper proposes the Internet of Things (IoT)-based information system for indoor and outdoor use. Since the conducted survey of related works indicated a lack of methodological approaches to the design process, therefore a Design Methodology (DM), which approaches the design target from the perspective of the stakeholders, contracting authorities and potential users, is introduced. The implemented solution applies the three-axial accelerometer and magnetometer, Pedestrian Dead Reckoning (PDR), thresholding and the decision trees algorithm. Such an architecture enables the localization of a monitored person within four room-zones with accuracy; furthermore, it identifies falls and the activities of lying, standing, sitting and walking. Based on the identified activities, the system classifies current activities as normal, suspicious or dangerous, which is used to notify the healthcare staff about possible problems. The real-life scenarios validated the high robustness of the proposed solution. Moreover, the test results satisfied both stakeholders and future users and ensured further cooperation with the project. © 2017 by the authors.

Place, publisher, year, edition, pages
MDPI AG , 2017. Vol. - 7, no - 6, article id 596
National Category
Signal Processing Other Health Sciences
Identifiers
URN: urn:nbn:se:bth-14672DOI: 10.3390/app7060596ISI: 000404449800072OAI: oai:DiVA.org:bth-14672DiVA, id: diva2:1113900
Note

open access

Available from: 2017-06-22 Created: 2017-06-22 Last updated: 2024-10-21Bibliographically 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

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Kulesza, Wlodek

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