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A Deep Learning Approach for Human Action Recognition Using Skeletal Information.

Eirini Mathe^1,2, Apostolos Maniatis³, Evaggelos Spyrou^4,5

¹Institute of Informatics and Telecommunications, National Center for Scientific Research- "Demokritos", Athens, Greece.

Advances in Experimental Medicine and Biology

|May 30, 2020

Summary

This summary is machine-generated.

This study introduces a novel method for human action detection in daily activities using convolutional neural networks (CNNs). The approach converts human joint movements into images for effective action recognition.

Keywords:

Activities of daily living Convolutional neural networks Human action recognition

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Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Accurate human action detection is crucial for understanding activities of daily living (ADLs).
Existing methods often struggle with complex, multi-part human movements.
Processing raw sensor data for action recognition presents significant challenges.

Purpose of the Study:

To develop an effective approach for human action detection in ADLs.
To leverage convolutional neural networks (CNNs) for recognizing human actions.
To represent human actions as images for improved detection accuracy.

Main Methods:

Utilized 3D skeletal joint positions derived from RGB and depth sensor data.
Represented joint motion as 1D signals, concatenated into images.
Applied Discrete Fourier Transform (DFT) to these images for CNN training.

Main Results:

Demonstrated a viable method for human action detection using CNNs and DFT images.
Successfully evaluated the approach on a challenging, publicly available dataset.
The method showed promise for recognizing common activities of daily living.

Conclusions:

The proposed method effectively converts complex human motion into image representations for CNN analysis.
This approach offers a promising direction for robust human action detection in ADLs.
Further research can explore variations in signal processing and network architectures.