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A Machine Learning Approach for Human Activity Recognition.

Angelos Papoutsis¹, Giannis Botilias¹, Petros Karvelis¹

¹Department 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 presents an Internet of Things (IoT) and machine learning system for Human Activity Recognition (HAR), achieving 99% accuracy. The system effectively monitors daily life activities using wearable sensors, outperforming existing datasets.

Keywords:

Activity recognition health machine learning predictive methods sensors

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Area of Science:

Computer Science
Mobile Health
Internet of Things

Background:

Human Activity Recognition (HAR) is crucial for mobile health applications.
Developing accurate systems for individual activity monitoring is essential.
Existing methods require validation and comparison with real-world data.

Purpose of the Study:

To design and develop a system for monitoring and assisting individuals using IoT and machine learning.
To validate the system's performance by comparing collected data with a public dataset.
To investigate the applicability of wearable devices for HAR.

Main Methods:

Utilized Internet of Things (IoT) and machine learning technologies.
Collected data via a mobile application and a wearable device with accelerometer and gyroscope sensors.
Compared collected data against a publicly available dataset for validation.

Main Results:

Achieved a 99% classification accuracy for various activities using the developed system and dataset.
The system's performance surpassed the results obtained from the publicly available dataset (97%).
Validated the functionality and applicability of the chosen wearable device for HAR.

Conclusions:

The developed IoT and machine learning system demonstrates high effectiveness for Human Activity Recognition.
Wearable sensor data, when processed by advanced algorithms, offers superior performance in HAR.
This approach holds significant potential for enhancing mobile health monitoring and assistance.