Evaluation of Pose Tracking Accuracy in the First and Second Generations of Microsoft Kinect

Microsoft Kinect camera and its skeletal tracking capabilities have been embraced by many researchers and commercial developers in various applications of real-time human movement analysis. In this paper, we evaluate the accuracy of the human kinematic motion data in the first and second generation of the Kinect system, and compare the results with an optical motion capture system. We collected motion data in 12 exercises for 10 different subjects and from three different viewpoints. We report on the accuracy of the joint localization and bone length estimation of Kinect skeletons in comparison to the motion capture. We also analyze the distribution of the joint localization offsets by fitting a mixture of Gaussian and uniform distribution models to determine the outliers in the Kinect motion data. Our analysis shows that overall Kinect 2 has more robust and more accurate tracking of human pose as compared to Kinect 1.Comment: 10 pages, IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015

Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that ex-tract representative features from 3D depth data, a recon-structed 3D surface mesh or more commonly from the re-covered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface fea-tures. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynami-cal Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of hu-man activity recognition. Combined with simple classifica-tion frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets. 1

Reachable Workspace and Proximal Function Measures for Quantifying Upper Limb Motion.

There are a lack of quantitative measures for clinically assessing upper limb function. Conventional biomechanical performance measures are restricted to specialist labs due to hardware cost and complexity, while the resulting measurements require specialists for analysis. Depth cameras are low cost and portable systems that can track surrogate joint positions. However, these motions may not be biologically consistent, which can result in noisy, inaccurate movements. This paper introduces a rigid body modelling method to enforce biological feasibility of the recovered motions. This method is evaluated on an existing depth camera assessment: the reachable workspace (RW) measure for assessing gross shoulder function. As a rigid body model is used, position estimates of new proximal targets can be added, resulting in a proximal function (PF) measure for assessing a subject's ability to touch specific body landmarks. The accuracy, and repeatability of these measures is assessed on ten asymptomatic subjects, with and without rigid body constraints. This analysis is performed both on a low-cost depth camera system and a gold-standard active motion capture system. The addition of rigid body constraints was found to improve accuracy and concordance of the depth camera system, particularly in lateral reaching movements. Both RW and PF measures were found to be feasible candidates for clinical assessment, with future analysis needed to determine their ability to detect changes within specific patient populations

Wide-area external multicamera calibration using vision graphs and virtual calibration object

In this paper we address external calibration of distributed multi-camera system intended for tracking and observing. We present a robust and efficient method for wide area calibration using virtual calibration object created by two LED markers. Our algorithm does not require for all the cameras to share common volume; only pairwise overlap is required. We assume the cameras are internally calibrated prior to deployment. Calibration is performed by waiving the calibration bar over the camera coverage area. The initial pose of the cameras is calculated using essential matrix decompositions. Global calibration is solved by automatically constructing weighted vision graph and finding optimal transformation paths between the cameras. In the optimization process, we introduce novel parametrization for two-point calibration using direction normal. The results are increased accuracy and robustness of the method under the presence of noise. In the paper, we present experimental results on a synthetic and real camera setup. We have performed image noise analysis on a synthetic wide-area setup of 5 cameras. Finally, we present the results obtained on a real setup with 12 cameras. The results obtained on the real camera setup show that our approach compensates for error propagation when the path transformation includes two to three nodes. No significant difference in reprojection error was found between the cameras on non-direct and direct path of the vision graph. The mean reprojection error for the real cameras was below 0.4 pixels. Index Terms — External camera calibration, vision graph, multi-camera system, epipolar geometry 1

The Feasibility and Usability of RunningCoach: A Remote Coaching System for Long-Distance Runners

Studies have shown that about half of the injuries sustained during long-distance running involve the knee. Cadence (steps per minute) has been identified as a factor that is strongly associated with these running-related injuries, making it a worthwhile candidate for further study. As such, it is critical for long-distance runners to minimize their risk of injury by running at an appropriate running cadence. In this paper, we present the results of a study on the feasibility and usability of RunningCoach, a mobile health (mHealth) system that remotely monitors running cadence levels of runners in a continuous fashion, among other variables, and provides immediate feedback to runners in an effort to help them optimize their running cadence