Activity-Aware Vital Sign Monitoring Based on a Multi-Agent Architecture

Abstract

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.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor

“…The parallel monitoring of bodily activity and vital signs, including temperature, pulse rate, and oxygen saturation, allows a comprehensive view of an individual's health status. This approach is particularly beneficial in tele-monitoring applications, where understanding the context and behavior is crucial for accurate feedback [23][24][25]. Correlating vital signs with specific activities aids in establishing individual baselines and identifying anomalies that may indicate health issues [26][27][28].…”

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor

“…The parallel monitoring of bodily activity and vital signs, including temperature, pulse rate, and oxygen saturation, allows a comprehensive view of an individual's health status. This approach is particularly beneficial in tele-monitoring applications, where understanding the context and behavior is crucial for accurate feedback [30][31][32]. Correlating vital signs with specific activities aids in establishing individual baselines and identifying anomalies that may indicate health issues [33][34][35].…”

Machine Learning for Healthcare Radars: Recent Progresses in Human Vital Sign Measurement and Activity Recognition

“…• Current research trend discussing these two topics together [12]- [18]. • Broad range of applications offered by healthcare radars, posing challenges in comprehensive coverage of several applications within a single paper.…”

“…Recently, several papers have begun to discuss vital sign measurement and activity recognition together [12], [13], [15], [18]. It provides a holistic approach for health monitoring by observing vital signs and activity recognition in living environment, and the medical experts can obtain a detailed overview of an individual's health.…”

“…As stated earlier, several research works are currently considering both the vital sign measurement and activity recognition in a holistic fashion [12]- [18]. Recently, concepts like activity-aware vital sign measurement [18] have been receiving increased attention.…”