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

通过观察运动模式来跟踪人体运动是一个具有许多潜在应用的基础研究领域，如人体步态分析、独立生活设施中的老年人监测、人机交互和监视。迄今为止，摄像机网络或可穿戴传感器已被用于捕捉人体运动。然而，视觉传感一直在努力克服光照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，使用低成本的运动学深度传感器，这是一种新兴技术。提出的研究计划将针对通过分布式深度传感器网络对人进行鲁棒运动跟踪的方面。

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.