Singular value decomposition on triaxial acceleration signal helps CNN based human fall recognition

What is it about?

We compared the effect of applying various Principal Component Analysis (PCA) based dimension reduction methods, namely, Singular Value Decomposition (SVD), Kernel PCA, and Sparse PCA, on triaxial accelerometer signals for Convolutional Neural Network (CNN) based human activity recognition (HAR) with a focus on fall detection. We found that SVD was the most effective dimension reduction method for improving CNN based fall recognition accuracy.

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Why is it important?

Human activity recognition including fall recognition requires constant prediction, thus making a small increase in recognition performance meaningful. Applying dimension reduction on triaxial accelerometer signals achieves this goal by realizing an increase in recognition performance by reducing noise. By concatenating raw triaxial acceleration signal with SVD signal, or signal magnitude vector with SVD signal, fall recognition performance can be possibly increased.

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