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. 2017 Mar 17;17(3):609.
doi: 10.3390/s17030609.

A Context-Aware S-Health Service System for Drivers

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Free PMC article

A Context-Aware S-Health Service System for Drivers

Jingkun Chang et al. Sensors (Basel). .
Free PMC article

Abstract

As a stressful and sensitive task, driving can be disturbed by various factors from the health condition of the driver to the environmental variables of the vehicle. Continuous monitoring of driving hazards and providing the most appropriate business services to meet actual needs can guarantee safe driving and make great use of the existing information resources and business services. However, there is no in-depth research on the perception of a driver's health status or the provision of customized business services in case of various hazardous situations. In order to constantly monitor the health status of the drivers and react to abnormal situations, this paper proposes a context-aware service system providing a configurable architecture for the design and implementation of the smart health service system for safe driving, which can perceive a driver's health status and provide helpful services to the driver. With the context-aware technology to construct a smart health services system for safe driving, this is the first time that such a service system has been implemented in practice. Additionally, an assessment model is proposed to mitigate the impact of the acceptable abnormal status and, thus, reduce the unnecessary invocation of the services. With regard to different assessed situations, the business services can be invoked for the driver to adapt to hazardous situations according to the services configuration model, which can take full advantage of the existing information resources and business services. The evaluation results indicate that the alteration of the observed status in a valid time range T can be tolerated and the frequency of the service invocation can be reduced.

Keywords: context awareness; customized business services; driver health; semantic sensor network (SSN); situation assessment; smart health.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
System architecture consists of four main parts Context Information Provider (CIP), Commutation, Context-Aware Service Provider (CASP) and Business Service Provider (BSP).
Figure 2
Figure 2
Inheritance relationship among Vehicle-Driver Semantic Sensor Network (VDSSN), SSN and DOLCE Ultra Lite (DUL) ontologies.
Figure 3
Figure 3
Demonstration of the relationship between instances.
Figure 4
Figure 4
Demonstration of the reasoning processes.
Figure 5
Figure 5
Cases of situation assessment process based on historical status.
Figure 6
Figure 6
Adjustment of the assessment status.
Figure 7
Figure 7
The invocation of selected services.
Figure 8
Figure 8
Time consumption from the sensory data to assessment status.
Figure 9
Figure 9
Assessment demonstration during the observation of body temperature.
Figure 10
Figure 10
Assessment result as the valid quantity increases from 1 to 5 during the CO observation: (a) Assessment status; and (b) Times of services invocation.

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