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Classifier-Based Data Transmission Reduction in Wearable Sensor Network for Human Activity Monitoring.

Marcin Lewandowski¹, Bartłomiej Płaczek¹, Marcin Bernas²

¹Institute of Computer Science, University of Silesia, Będzińska 39, 41-200 Sosnowiec, Poland.

Sensors (Basel, Switzerland)

|December 30, 2020

Summary

This study introduces a novel method using embedded classifiers to reduce data transmissions in wireless wearable sensor networks, significantly extending battery life for activity monitoring. The approach maintains high accuracy in human activity recognition while conserving energy.

Keywords:

activity recognition embedded machine learning energy consumption lifetime transmission suppression wearable sensors wireless sensor network

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Area of Science:

Wireless Sensor Networks
Biomedical Engineering
Human Activity Recognition

Background:

Wireless wearable sensor networks (WWSNs) are crucial for healthcare, activity monitoring, and human-machine interfacing.
The operational lifetime of battery-powered sensor nodes is a critical limitation for WWSNs.

Purpose of the Study:

To develop and evaluate a new method for extending the network lifetime of WWSNs by minimizing unnecessary data transmissions.
To assess the impact of data selection on recognition system accuracy within WWSNs.

Main Methods:

Implementation of embedded classifiers within sensor nodes to intelligently decide on data transmission.
Development of a classifier training procedure considering data selection's effect on recognition accuracy.
Prototyping a WWSN for human activity monitoring to test the proposed method.

Main Results:

The proposed method significantly prolongs WWSN network lifetime.
High accuracy of human activity recognition is preserved with the new method.
Demonstrated advantages over existing algorithms for reducing data transmission.

Conclusions:

The embedded classifier approach effectively extends WWSN operational duration.
This method offers a viable solution for energy conservation in wearable sensor networks without compromising performance.
The approach shows promise for enhancing the practicality and longevity of WWSN applications.