Influence of Depth Camera Noise Models on Respiration Estimation
This work addresses the need for simulated data to train and test algorithms for vital sign monitoring using depth cameras, but it is incremental as it builds on existing approaches for controlled settings.
The paper tackled the problem of generating realistic depth-camera data for respiration estimation by developing a 3D-rendering simulation pipeline that incorporates different noise models, showing that noise model differences become significant at low image resolutions.
Depth cameras are an interesting modality for capturing vital signs such as respiratory rate. Plenty approaches exist to extract vital signs in a controlled setting, but in order to apply them more flexibly for example in multi-camera settings, a simulated environment is needed to generate enough data for training and testing of new algorithms. We show first results of a 3D-rendering simulation pipeline that focuses on different noise models in order to generate realistic, depth-camera based respiratory signals using both synthetic and real respiratory signals as a baseline. While most noise can be accurately modelled as Gaussian in this context, we can show that as soon as the available image resolution is too low, the differences between different noise models surface.