Depth imaging has, through recent technological advances, become ubiquitous as products become smaller, more affordable, and more precise. Depth cameras have also emerged as a promising modality for activity recognition as they allow detection of users’ body joints and postures. Increased resolutions have now enabled a novel use of depth cameras that facilitate more fine-grained activity descriptors: The remote detection of a person’s breathing by picking up the small distance changes from the user’s chest over time. We propose in this work a novel method to model chest elevation to robustly monitor a user’s respiration, whenever users are sitting or standing, and facing the camera. The method is robust to users occasionally blocking their torso region and is able to provide meaningful breathing features to allow classification in activity recognition tasks. We illustrate that with this method, with specific activities such as paced-breathing meditating, performing breathing exercises, or post-exercise recovery, our model delivers a breathing accuracy that matches that of a commercial respiration chest monitor belt. Results show that the breathing rate can be detected with our method at an accuracy of 92 to 97% from a distance of two metres, outperforming state-of-the-art depth imagining methods especially for non-sedentary persons, and allowing separation of activities in respiration-derived features space.

中文翻译：

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在基于深度的活动识别中将呼吸作为一种感知方式。

随着产品变得更小，更便宜和更精确，深度成像已通过最近的技术进步而变得无处不在。深度相机也已经成为一种有前途的活动识别方式，因为它们可以检测用户的身体关节和姿势。现在，分辨率的提高使得深度相机得以新颖地使用，它可以促进更细粒度的活动描述符：通过随着时间的推移从用户的胸部获取较小的距离变化来远程检测人的呼吸。我们在这项工作中提出了一种新颖的方法，可以对胸部抬高进行建模，以在用户坐着或站着并面对相机时稳健地监视用户的呼吸。该方法对于偶尔会阻塞其躯干区域的用户具有鲁棒性，并且能够提供有意义的呼吸功能以允许在活动识别任务中进行分类。我们举例说明，通过这种方法，通过特定的活动（如打坐式呼吸冥想，进行呼吸运动或运动后恢复），我们的模型可提供与商业呼吸胸监测带相匹配的呼吸精度。结果表明，我们的方法可以在两米的距离内以92％到97％的精度检测呼吸速率，尤其是对于非沉迷者而言，其表现优于最新的深度成像方法，并且可以分离活动在呼吸衍生的特征空间中。执行呼吸运动或运动后恢复，我们的模型提供的呼吸精度与商业呼吸胸部监护带相匹配。结果表明，我们的方法可以在两米的距离内以92％到97％的精度检测呼吸速率，尤其是对于非沉迷者而言，其表现优于最新的深度成像方法，并且可以分离活动在呼吸衍生的特征空间中。执行呼吸运动或运动后恢复，我们的模型提供的呼吸精度与商业呼吸胸部监护带相匹配。结果表明，我们的方法可以在两米的距离内以92％到97％的精度检测呼吸速率，尤其是对于非沉迷者而言，其表现优于最新的深度成像方法，并且可以分离活动在呼吸衍生的特征空间中。