Key result
A Stochastic Petri Net model of a multi-sensor wearable health monitoring system and a Java-based simulation framework were developed to address synchronization and temporal dependencies.
The proposed SPN model and Java simulation framework provide a method to handle synchronization and temporal dependencies in multi-sensor wearable health monitoring systems.
Answers are grounded in this paper and cite their sources.
May aid wearable monitor design; leaves open clinical validation before practice use.
The deployment of Wearable Health Monitoring Systems (WHMS) can potentially enable ubiquitous and continuous monitoring of a patient's physiological parameters. Moreover by incorporating multiple biosensors in such a system a comprehensive estimation of the user's health condition can possibly be derived. In this paper we present a Stochastic Petri Net (SPN) model of a multi-sensor WHMS along with a corresponding simulation framework implemented in Java. The proposed model is built on top of a previously published multisensor data fusion strategy, which has been expanded in this work to take into account synchronization issues and temporal dependencies between the measured bio-signals.
No takes yet. Share an insight, caveat, or question.
Pantelopoulos et al. (2009) studied this question. Stochastic Petri Net (SPN) model of a multi-sensor WHMS was evaluated. A Stochastic Petri Net model of a multi-sensor wearable health monitoring system and a Java-based simulation framework were developed to address synchronization and temporal dependencies.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: