Activity-Aware Vital SignMonitoring Based on a Multi-Agent Architecture
| Autor: | Viorel Negru, Todor Ivașcu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
human activity recognition
Activities of daily living Computer science Real-time computing Vital signs Wearable computer 02 engineering and technology TP1-1185 Ontology (information science) Accelerometer Biochemistry Article Analytical Chemistry vital signs Knowledge-based systems health status monitoring Activities of Daily Living 0202 electrical engineering electronic engineering information engineering Humans knowledge-based system Electrical and Electronic Engineering Agent architecture Exercise Instrumentation Chemical technology wearable sensors 020206 networking & telecommunications Awareness Wrist multi-agent architecture Atomic and Molecular Physics and Optics 020201 artificial intelligence & image processing Algorithms Sign (mathematics) |
| Zdroj: | Sensors Volume 21 Issue 12 Sensors, Vol 21, Iss 4181, p 4181 (2021) Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s21124181 |
| Popis: | Vital sign monitoring outside the clinical environment based on wearable sensors ensures better support in assessing a patient’s health condition, and in case of health deterioration, automatic alerts can be sent to the care providers. In everyday life, the users can perform different physical activities, and considering that vital sign measurements depend on the intensity of the activity, we proposed an architecture based on the multi-agent paradigm to handle this issue dynamically. Different types of agents were proposed that processed different sensor signals and recognized simple activities of daily living. The system was validated using a real-life dataset where subjects wore accelerometer sensors on the chest, wrist, and ankle. The system relied on ontology-based models to address the data heterogeneity and combined different wearable sensor sources in order to achieve better performance. The results showed an accuracy of 95.25% on intersubject activity classification. Moreover, the proposed method, which automatically extracted vital sign threshold ranges for each physical activity recognized by the system, showed promising results for remote health status evaluation. |
| Databáze: | OpenAIRE |
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