Generating Multivariate Synthetic Time Series Data for Absent Sensors from Correlated Sources

Missing sensor data in human activity recognition is an active field of research that is being targeted with generative models for synthetic data generation. In contrast to most previous approaches, we aim to generate data of a sensor exclusively from data available at sensors in different body locations. Particularly, we evaluate existing approaches proposed in the literature for their suitability in this scenario. In this paper, we focus on the prediction of acceleration data and generate machine learning models based on generative adversarial networks and trained using correlated data from sensors in different body positions to generate synthetic sensor data that can replace the missing data from a sensor in a specific body position. The accuracy of the generated synthetic data is evaluated using a classification model based on a convolutional neural network for human activity recognition.