Next Article in Journal
Respiratory Activity during Exercise: A Feasibility Study on Transition Point Estimation Using Impedance Pneumography
Next Article in Special Issue
Architecture Design and VLSI Implementation of 3D Hand Gesture Recognition System
Previous Article in Journal
A Cost-Effective CNN-LSTM-Based Solution for Predicting Faulty Remote Water Meter Reading Devices in AMI Systems
Previous Article in Special Issue
Use of Multiple Low Cost Carbon Dioxide Sensors to Measure Exhaled Breath Distribution with Face Mask Type and Wearing Behaviour
Article

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

1
Centre for Research & Technology Hellas, Information Technologies Institute, 6th Km Charilaou—Thermi, 57001 Thessaloniki, Greece
2
School of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
Department of Neurology III, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
4
Department of Neurology I, Medical School, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Uichin Lee and Lorenzo Chiari
Sensors 2021, 21(18), 6230; https://doi.org/10.3390/s21186230
Received: 15 July 2021 / Revised: 7 September 2021 / Accepted: 14 September 2021 / Published: 17 September 2021
(This article belongs to the Special Issue Multi-Sensor for Human Activity Recognition)
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. View Full-Text
Keywords: wearables; sensors; ontologies; symbolic reasoning; knowledge graphs; ehealth; multiple sclerosis wearables; sensors; ontologies; symbolic reasoning; knowledge graphs; ehealth; multiple sclerosis
Show Figures

Figure 1

MDPI and ACS Style

Stavropoulos, T.G.; Meditskos, G.; Lazarou, I.; Mpaltadoros, L.; Papagiannopoulos, S.; Tsolaki, M.; Kompatsiaris, I. 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 2021, 21, 6230. https://doi.org/10.3390/s21186230

AMA Style

Stavropoulos TG, Meditskos G, Lazarou I, Mpaltadoros L, Papagiannopoulos S, Tsolaki M, Kompatsiaris I. 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. 2021; 21(18):6230. https://doi.org/10.3390/s21186230

Chicago/Turabian Style

Stavropoulos, Thanos G., Georgios Meditskos, Ioulietta Lazarou, Lampros Mpaltadoros, Sotirios Papagiannopoulos, Magda Tsolaki, and Ioannis Kompatsiaris. 2021. "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 21, no. 18: 6230. https://doi.org/10.3390/s21186230

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop