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Author Spotlight: Investigating HR-Dependent Cardiac Function in Mouse Models Through a Novel Atrial-Pacing Approach

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Green Communication for Tracking Heart Rate with Smartbands.

Franks González-Landero¹, Iván García-Magariño^2,3, Raquel Lacuesta^4,5

¹Edison Desarrollos, 44002 Teruel, Spain. gonzalezfranks@edisondesarrollos.es.

Sensors (Basel, Switzerland)

|August 15, 2018

Summary

This study reduces smartband heart rate measurements to save energy, achieving an 83.57% reduction in communication energy use. The system identifies high-risk periods for frequent monitoring, enhancing wearable device efficiency.

Keywords:

Google fit body sensor networks eHealthcare heart rate smartband wearable sensors

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Area of Science:

Wearable technology
Biomedical engineering
Health informatics

Background:

Increasing use of wearable devices for health monitoring.
Need for energy efficiency in continuous data collection from wearables.
Integration of smartbands with smartphones for health applications.

Purpose of the Study:

To reduce the frequency of heart rate (HR) measurements by smartbands to conserve energy.
To maintain the utility of health monitoring applications despite reduced measurement frequency.
To develop an intelligent system for adaptive HR data acquisition in wearables.

Main Methods:

Utilized a Sony SB2 smartband for HR data collection.
Employed the Fit API for data storage and Android for data management.
Developed an algorithm to detect hourly ranges with elevated HR, indicating potential risk.

Main Results:

Achieved an 83.57% reduction in communication energy consumption through optimized measurement frequency.
Identified high-risk hourly ranges (e.g., >60 HR/hour) for frequent monitoring, reducing frequency to 6 HR/hour otherwise.
Daily prediction model achieved 63.04% accuracy using one day of training data from 20 users.

Conclusions:

The proposed approach significantly reduces energy consumption in wearable devices without compromising essential health monitoring.
Adaptive HR measurement frequency based on detected risk periods enhances wearable device efficiency.
The system provides timely alerts to users regarding potential health risks based on HR patterns.