Human activity recognition from mobile inertial sensors using recurrence plots
This work addresses activity recognition for mobile device users, presenting an incremental improvement by applying recurrence plots and visual descriptors to sensor data.
The paper tackles human activity recognition from mobile inertial sensors by converting sensor data into recurrence plots and treating them as texture images for classification, achieving the highest accuracies compared to traditional time- and frequency-domain features, with RGB recurrence plots yielding the best results.
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.