Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis
In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that ad...
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doaj-f6d339992afc476cb1b1cef77a260bdc2021-09-26T01:23:44ZengMDPI AGSensors1424-82202021-09-01216230623010.3390/s21186230Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple SclerosisThanos G. Stavropoulos0Georgios Meditskos1Ioulietta Lazarou2Lampros Mpaltadoros3Sotirios Papagiannopoulos4Magda Tsolaki5Ioannis Kompatsiaris6Centre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, GreeceCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, GreeceCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, GreeceCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, GreeceDepartment of Neurology III, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceDepartment of Neurology I, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, GreeceCentre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, GreeceIn this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework.https://www.mdpi.com/1424-8220/21/18/6230wearablessensorsontologiessymbolic reasoningknowledge graphsehealth |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Thanos G. Stavropoulos Georgios Meditskos Ioulietta Lazarou Lampros Mpaltadoros Sotirios Papagiannopoulos Magda Tsolaki Ioannis Kompatsiaris |
spellingShingle |
Thanos G. Stavropoulos Georgios Meditskos Ioulietta Lazarou Lampros Mpaltadoros Sotirios Papagiannopoulos Magda Tsolaki Ioannis Kompatsiaris Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis Sensors wearables sensors ontologies symbolic reasoning knowledge graphs ehealth |
author_facet |
Thanos G. Stavropoulos Georgios Meditskos Ioulietta Lazarou Lampros Mpaltadoros Sotirios Papagiannopoulos Magda Tsolaki Ioannis Kompatsiaris |
author_sort |
Thanos G. Stavropoulos |
title |
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis |
title_short |
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis |
title_full |
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis |
title_fullStr |
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis |
title_full_unstemmed |
Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis |
title_sort |
detection of health-related events and behaviours from wearable sensor lifestyle data using symbolic intelligence: a proof-of-concept application in the care of multiple sclerosis |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2021-09-01 |
description |
In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework. |
topic |
wearables sensors ontologies symbolic reasoning knowledge graphs ehealth |
url |
https://www.mdpi.com/1424-8220/21/18/6230 |
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