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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587640
• 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.

通过观察运动模式来跟踪人体运动是一个基础性的研究领域，具有许多潜在的应用前景，如人体步态分析、独立生活设施中的老年人监控、人机交互和监视等。到目前为止，摄像头或可穿戴传感器网络已经被用来捕捉人体运动。然而，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度也很低。目前正在开发一种更可行、更经济的方法，使用低成本的动态深度传感器，这是一项新兴技术。拟议的研究计划将通过分布式深度传感器网络来针对人的健壮运动跟踪方面。 研究成果将为人类生物力学和机器人领域做出贡献，并在多个方面造福加拿大人。这项研究将提供一种实用的工具，可用于患者康复，通过观察他们在自然生活环境中的活动，而不需要佩戴传感器的不便；它还将提供一种独特的、非侵入性的方法，用于监测我们的老龄化人口的活动，以确保他们在私人或公共护理设施中的安全。 这项调查将为各种深度传感技术网络开发新的校准方法，对噪声的性质以及与身体和肢体运动有关的测量的敏感性进行建模和了解。提出了两种基于深度测量的运动跟踪模型：粗略跟踪模型和精细跟踪模型。在粗略模型中，我们根据四肢上的点来定义人的整体周围形状，并提出了一种基于横截面形状的新方法。我们建议扩展将物理监测区域划分为更粗的体积(例如，立方体)的概念，并将分布的深度测量与相应的3D体积相关联。对于每个体积，我们将探索各种方法来寻找由测量的深度信息指定的表面的合适表示。然后，重建的粗形状模型被用作跟踪人的选定肢体(例如手臂、脚和头部)的基础。首先，使用有关四肢位置的信息来定义它们之间沿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，将定义跟踪变量来表示肢体的底层骨架和局部简单几何形状。 针对人体和肢体运动的自然不确定性，提出了一种基于一种新型智能滤波框架(智能粒子滤波)的肢体跟踪方法。我计划开发、研究和实验跟踪变量的各种运动模型和先前的运动概率分布，这些模型可以表示每个时间步的预期跟踪变量的知识。然后，将定义一组预测跟踪变量，这些变量可以与实际测量的深度传感器信息进行比较和加权。预期的新贡献与基于模型的稳健切换方法的开发有关，该切换方法用于跟踪作为人的全局运动意图和所选肢体的局部运动模式的函数。总体目标是从跟踪一个人及其相关肢体开始，并将结果扩展到在监测区域内活动的多个人。在开发的每个阶段，增量结果将针对现有的基于标记的系统进行验证，并与其他已知的运动预测方法进行比较。