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Application-oriented programming model for sensor networks embedded in the human body.

Talles M G de A Barbosa¹, Iwens G Sene, Adson F da Rocha

¹Dept. of Comput. Sci., Catholic Univ. of Goiás, Goiânia, Brazil. talles@ucg.br

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This summary is machine-generated.

This study introduces a novel programming model for body-worn sensor networks, enabling healthcare professionals to easily program and reconfigure devices for applications like electrocardiogram monitoring.

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Area of Science:

Biomedical Engineering
Computer Science
Wireless Sensor Networks

Background:

Current programming models for body sensor networks are often complex and inflexible.
Healthcare professionals require intuitive methods to manage and adapt embedded sensor systems.
The need for efficient reconfiguration of multi-functional sensor nodes is critical for real-time health monitoring.

Purpose of the Study:

To present a new, layered, application-oriented programming model for human body sensor networks.
To enable healthcare professionals to program and reconfigure these networks locally or via the internet.
To evaluate the efficiency of this model through benchmarking.

Main Methods:

Development of a four-layer, top-down programming model.
Implementation of multi-programming for application-oriented software.
Benchmarking of a multi-functional sensor node for electrocardiogram (ECG) measurement and transmission.

Main Results:

The proposed model facilitates intuitive programming and reconfiguration of body sensor networks.
Benchmarking assessed the mean programming time for a multi-functional sensor node.
The model demonstrated potential for efficient electrocardiogram data acquisition and transmission.

Conclusions:

The new programming model simplifies the management of human body sensor networks.
It empowers healthcare professionals with remote and local network control capabilities.
This approach is promising for enhancing the usability and adaptability of wearable health monitoring systems.