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.

翻译：深度相机是一种用于捕捉呼吸频率等生命体征的有趣模态。在受控环境下已有多种提取生命体征的方法，但为了更灵活地应用（例如在多相机场景中），需要模拟环境来生成足够数据以训练和测试新算法。我们展示了一个专注于不同噪声模型的3D渲染模拟流程的初步结果，该流程使用合成与真实呼吸信号作为基线，生成基于深度相机的逼真呼吸信号。虽然在此背景下大多数噪声可精确建模为高斯分布，但我们证明，当可用图像分辨率过低时，不同噪声模型间的差异便会显现。