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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

Track My Health: An IoT Approach for Data Acquisition and Activity Recognition.

Giannis Botilias¹, Angelos Papoutsis¹, Petros Karvelis¹

¹Laboratory of Knowledge and Intelligent Computing, Department of Informatics and Telecommunications, University of Ioannina, Kostakioi, 47150 Arta, Greece.

Studies in Health Technology and Informatics

|October 22, 2020

Summary

This summary is machine-generated.

This study introduces an Internet of Things system for Human Activity Recognition (HAR) using wearable sensors. The system collects and processes vital signals to identify human health status via machine learning.

Keywords:

Human activity recognition IoT data acquisition m-health sensors

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Area of Science:

Biomedical Engineering
Computer Science
Wearable Technology

Background:

Human Activity Recognition (HAR) is crucial for self-care and preventative health.
Advances in wearable technology and machine learning offer new avenues for HAR research.
Wearable devices with sensors like accelerometers and gyroscopes are key to collecting activity data.

Purpose of the Study:

To present an Internet of Things (IoT) based system for monitoring vital signals.
To develop a system for data acquisition, processing, and storage for HAR.
To prepare data for a machine learning module aimed at identifying human health status.

Main Methods:

Utilized wearable devices with accelerometer and gyroscope sensors.
Developed a mobile application for data collection and storage in a database.
Implemented an Internet of Things (IoT) architecture for signal monitoring.

Main Results:

Successfully designed and developed a system for collecting and storing sensor data from wearable devices.
Established a data acquisition and processing module ready to feed a machine learning model.
Laid the groundwork for machine learning-based human health status identification.

Conclusions:

The developed IoT system effectively acquires, processes, and stores vital signals from wearable sensors.
This system provides a foundation for machine learning models to accurately recognize human activities and health conditions.
The integration of IoT and wearable technology advances the field of Human Activity Recognition for health applications.