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New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.

Tsige Tadesse Alemayoh¹, Jae Hoon Lee¹, Shingo Okamoto¹

¹Department of Mechanical Engineering, Graduate School of Science and Engineering, Ehime University, Matsuyama 790-8577, Japan.

Sensors (Basel, Switzerland)

|April 30, 2021

Summary

This study introduces a novel method for structuring time series data to improve human activity recognition using smartphones. A deep learning approach achieved high accuracy, demonstrating practical real-time applications.

Keywords:

convolutional neural network deep learning human activity recognition inertial measurement unit sensors input adaptation

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Area of Science:

Computer Science
Artificial Intelligence
Human-Computer Interaction

Background:

Effective human-assistive technologies require computing devices to accurately recognize human movements in real-time.
Ubiquitous sensors and intelligent methods are crucial for developing reliable activity recognition systems.

Purpose of the Study:

To develop a novel data structuring method for deeper feature extraction in human activity recognition.
To enhance the performance of real-time activity recognition for smart devices and human-robot collaboration.

Main Methods:

Collected activity data from eight activities using a smartphone and a custom iOS application.
Structured data into single and double channels for deep temporal and spatial feature extraction.
Utilized Fourier and wavelet domains alongside the time domain for data representation.
Employed various neural network models, focusing on a convolutional neural network with double-channeled time-domain input.

Main Results:

The convolutional neural network with double-channeled time-domain input demonstrated superior performance in activity classification.
The proposed method achieved better performance when evaluated on public datasets.
Real-time testing on computers and smartphones showed promising results for the trained model's practicability.

Conclusions:

The developed data structuring and deep learning method significantly improves human activity recognition accuracy.
This approach offers a practical and efficient solution for real-time human movement awareness in smart devices and assistive technologies.