Modelling and Synthesizing Motion Artifacts in Unobtrusive Multimodal Sensing using Copulas.

The use of non-contact sensing modalities to estimate apatient's vital signs is a promising approach to improve remote monitoring. One of the main challenges in non-contact sensing are motion artifacts, which can cause severe problems and must not be disregarded when designing non-contact systems. Combining multiple sensors and using intelligent sensor-fusion algorithms can reduce the influence of motion artifacts and improve the robustness of the vital sign estimation. Training and validating algorithms are important parts of the development process, but acquiring real data is usually a time-consuming task. Therefore a method to generate a large number of multi-sensor motion artifacts is needed. In this paper we investigate motion artifacts and their inter-dependence in a multi-sensor system. From these analyses, a multivariate mathematical artifact model is derived. Further-more, we propose a general synthesizing algorithm for artificial motion artifacts that allows creating an arbitrary number of multi-sensor motion artifacts. Finally, we compare the artificially created artifacts with real artifacts and evaluate our algorithm. Both qualitative indicators, e.g. signal morphology, and quantitative analyses, e.g. statistical distance measures, show a good accuracy of our model.