Study of Kinematic Tracking and Monitoring of Human Movements in a Collaborative Network of Depth Sensors

深度传感器协作网络中人体运动的运动跟踪和监测研究

• 批准号: RGPIN-2014-04160
• 负责人: Payandeh, Shahram
• 金额: $ 1.97万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=638482
• 关键词: Study; Kinematic; Tracking; Monitoring; Human

Tracking human body movement by observing motion patterns is a fundamental research area with many potential applications, such as human gait analysis, monitoring seniors in independent living facilities, human-robot interaction, and surveillance. To date, networks of cameras or wearable sensors have been utilized to capture human body movements. However, visual sensing has struggled to overcome variations in illumination and surface texture properties, and wearable sensors have faced poor acceptance. A more feasible and economical approach is currently being developed using low cost kinematic depth sensors, which are an emerging technology. The proposed research program will target the aspects of robust motion tracking of people through a network of distributed depth sensors.Research results will contribute to the field of human biomechanics and robotics and benefit Canadians in a number of ways. The research will provide a practical tool that can be used in patient rehabilitation by observing their movements in their natural living habitat without the inconvenience of wearing sensors; it will also offer a unique, non-intrusive approach for their deployment in monitoring activities of our aging population for their safety in private or public caregiving facilities. This investigation will undertake development of novel calibration methods for various networks of depth sensing technologies, modeling and understanding the nature of noise and sensitivity of measurements related to the location of bodies and movements of limbs. Two kinematic tracking models are proposed based on the depth measurements: a coarse tracking model; and a fine tracking model. In the coarse model, we define the overall surrounding shape of persons based on points at extremities and the novel method based on shapes of cross-sectional cuts. We propose to extend the notion of dividing the physical monitoring area into coarser volumes (e.g., cubes) and associate the distributed depth measurements to corresponding 3D volumes. For each volume, we will explore various approaches for finding a suitable representation of the surface prescribed by measured depth information. The reconstructed coarse shape model is then used as a basis for tracking selected limbs of the person, e.g. arms, feet, and head. First, information about the location of extremities is used to define a local distance function along the mesh model between them. For each limb occupying a set of cubes, tracking variables will be defined to represent the underlying skeleton and local simple geometrical shape of the limb. Due to natural uncertainties associated with body and limb movements, a tracking method for each limb is proposed based on a novel intelligent filter framework (intelligent particle filter). I plan to develop, study, and experiment with various motion models of the tracking variables and prior motion probability distributions that can represent the knowledge of expected tracking variables at each time step. Then a set of predicted tracking variables will be defined that can be compared and weighted with the actual measured depth sensor information. The expected novel contributions are associated with development of a robust model-based switching method for tracking as a function of global motion intentions of the person and the local motion patterns of the selected limbs. The overall objective is to start by tracking one person and their associated limbs and extend the results to multiple people moving in the monitoring area. At each stage of the development, incremental results will be validated against an existing marker-based system and compared with other known motion prediction methods.

通过观察运动模式跟踪人体运动是一个基本的研究领域，具有许多潜在的应用，例如人体步态分析，监测独立生活设施中的老年人，人类机器人的互动和监视。迄今为止，已经利用了相机或可穿戴传感器的网络来捕获人体运动。但是，视觉传感一直在努力克服照明和表面纹理特性的变化，可穿戴传感器的接受程度不佳。目前使用低成本运动深度传感器（这是一种新兴技术）开发了一种更可行，更经济的方法。拟议的研究计划将通过分布式深度传感器网络来针对人们对人的强大运动跟踪的各个方面。研究结果将在许多方面为人类生物力学和机器人技术和机器人技术和受益加拿大人带来好处。这项研究将提供一种实用的工具，可以通过观察自然生活栖息地的运动而无需佩戴传感器的不便，可用于患者康复。它还将为他们在私人或公共照料设施中的安全监控人口的监视活动中的部署提供一种独特的，非侵入性的方法。这项研究将开发针对各种深度传感技术网络的新型校准方法，建模和理解与身体位置和四肢运动相关的测量的噪声和灵敏度的性质。根据深度测量值提出了两个运动学跟踪模型：粗糙的跟踪模型；和一个很好的跟踪模型。在粗糙的模型中，我们根据四肢的点和基于横截面切割形状的新方法来定义人的整体形状。我们建议将物理监测区域分为更粗的体积（例如，立方体）的概念，并将分布式深度测量值与相应的3D体积相关联。对于每一卷，我们将探索各种方法，以查找通过测量深度信息规定的表面表面的适当表示。然后，重建的粗大模型被用作跟踪人的选定肢体的基础，例如手臂，脚和头。首先，有关四肢位置的信息用于定义沿它们之间的网格模型的局部距离函数。对于占据一组立方体的每个肢体，将定义跟踪变量以表示肢体的基本骨架和局部简单的几何形状。由于与身体和肢体运动相关的自然不确定性，根据新型的智能滤波器框架（智能粒子滤波器）提出了针对每个肢体的跟踪方法。我计划开发，研究和实验跟踪变量的各种运动模型以及可以代表每个时间步骤中预期跟踪变量知识的先前运动概率分布。然后将定义一组预测的跟踪变量，可以将其与实际测量的深度传感器信息进行比较并加权。预期的新颖贡献与开发基于强大的模型的开关方法有关，该方法与人的全球运动意图以及所选肢体的局部运动模式有关。总体目标是从跟踪一个人及其相关的肢体开始，并将结果扩展到在监视区域中移动的多人。在开发的每个阶段，将根据现有的基于标记的系统验证增量结果，并将其与其他已知运动预测方法进行比较。