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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

A Mobile Health Application Using Geolocation for Behavioral Activity Tracking.

Mohamed Emish¹, Zeyad Kelani¹, Maryam Hassani¹

¹Department of Informatics, University of California, Irvine, CA 92697-3100, USA.

Sensors (Basel, Switzerland)

|September 28, 2023

Summary

This study introduces an energy-efficient mobile health (mHealth) app using motion detection and GPS for physical activity and location tracking. The app accurately monitors user movement, ensuring data quality for improved health outcomes.

Keywords:

assisted global positioning system blockchain data integration geospatial data location-based health services mHealth mobility analysis software

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Last Updated: Jul 15, 2025

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Area of Science:

Digital Health
Mobile Health (mHealth)
Wearable Technology

Background:

mHealth apps offer opportunities for rich health data collection.
Analyzing physical activity patterns can improve health outcomes and reduce chronic disease risk.
Existing methods for tracking physical activity and location via smartphones can be energy-intensive.

Purpose of the Study:

To develop and evaluate an energy-efficient mobile health application for tracking physical activity and location.
To assess the accuracy, efficiency, and battery consumption of the developed mHealth app.
To explore the use of blockchain and intuitive interfaces for participant compensation and engagement in health research.

Main Methods:

Developed an energy-efficient tracking algorithm using smartphone motion sensors and GPS.
Implemented encryption algorithms and a serverless, scalable cloud storage design for data security and efficiency.
Utilized Google's Activity Recognition API (Android) and geofencing/motion sensors (iOS) for broad smartphone compatibility.
Designed a database schema using Mobile Advertising ID (MAID) for unique device identification and data quality.
Integrated blockchain and traditional payment systems for user compensation and an intuitive user interface.

Main Results:

The mobile tracking app demonstrated accurate location capture during movement.
The application promptly resumed tracking after periods of inactivity.
The app exhibited low battery consumption while running in the background during a 20-day test period.
The system ensured data security and efficient storage through encryption and cloud design.

Conclusions:

The developed mHealth application is effective for energy-efficient physical activity and location tracking.
The app's design supports accurate data collection, security, and user engagement.
This technology holds potential for improving health outcomes through detailed analysis of physical activity patterns.