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

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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.