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Related Experiment Video

Updated: May 14, 2026

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

An Application for Pairing with Wearable Devices to Monitor Personal Health Status

Published on: February 3, 2022

Location and activity tracking with the cloud.

Taj Morton¹, Alex Weeks, Samuel House

¹School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97330, USA. mortont@onid.orst.edu

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

|February 1, 2013

Summary

This study introduces a wearable sensor prototype for real-time monitoring of elderly individuals at home. This technology supports independent living and enables early disease detection, reducing healthcare costs.

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Area of Science:

Gerontology
Biomedical Engineering
Health Informatics

Background:

Aging population necessitates innovative solutions for independent living.
Continuous monitoring of elderly individuals is crucial for timely healthcare interventions.
Activity, gait velocity, movement, and location are key biomarkers for healthy aging.

Purpose of the Study:

To develop and present a prototype for real-time monitoring of elderly adults in their homes.
To establish a foundation for future products and services aiding independent living and early disease diagnosis.
To explore the potential of integrated wearable sensors and cloud-based data processing for elder care.

Main Methods:

Integration of a wearable location-tracking sensor with cloud-based data processing.
Development of a vertically-integrated prototype for simultaneous real-time tracking and analysis.
Utilizing critical biomarkers such as activity, gait velocity, movement, and location for monitoring.

Main Results:

A functional prototype enabling real-time tracking and analysis of multiple individuals.
Demonstration of a basic infrastructure for advanced elder care solutions.
Potential for simultaneous monitoring and analysis of a large cohort.

Conclusions:

The developed prototype offers a viable infrastructure for remote elderly monitoring.
Real-time data analysis can facilitate early disease diagnosis and intervention.
This technology supports the goal of enabling elderly individuals to live independently for longer.