Self-supervised Human Activity Recognition by Learning to Predict Cross-Dimensional Motion

Understand

We propose the use of self-supervised learning for human activity recognition with smartphone accelerometer data.

• Our proposed solution consists of two steps.
• First, the representations of unlabeled input signals are learned by training a deep convolutional neural network to predict a segment of accelerometer values.
• Our model exploits a novel scheme to leverage past and present motion in x and y dimensions, as well as past values of the z axis to predict values in the z dimension.