Human activity recognition from mobile inertial sensors using recurrence plots

TL;DR

This paper introduces a novel method for human activity recognition using recurrence plots of inertial sensor data, transforming sensor signals into visual textures for improved classification accuracy.

Contribution

The study presents a new approach that leverages recurrence plots and visual descriptors to enhance activity recognition from mobile inertial sensors.

Findings

Recurrence plots outperform traditional time- and frequency-domain features.

RGB recurrence plots yield the highest classification accuracy.

The method effectively transforms sensor data into texture classification problem.

Abstract

Inertial sensors are present in most mobile devices nowadays and such devices are used by people during most of their daily activities. In this paper, we present an approach for human activity recognition based on inertial sensors by employing recurrence plots (RP) and visual descriptors. The pipeline of the proposed approach is the following: compute RPs from sensor data, compute visual features from RPs and use them in a machine learning protocol. As RPs generate texture visual patterns, we transform the problem of sensor data classification to a problem of texture classification. Experiments for classifying human activities based on accelerometer data showed that the proposed approach obtains the highest accuracies, outperforming time- and frequency-domain features directly extracted from sensor data. The best results are obtained when using RGB RPs, in which each RGB channel corresponds to the RP of an independent accelerometer axis.