Motion states inference through 3D shoulder gait analysis and Hierarchical Hidden Markov Models

Automatically inferring human intention from walking movements is an important research concern in robotics and other fields of study. It is generally derived from temporal motion of limb position relative to the body. These changes can also be reected in the change of stance and gait. Conventional systems relying on gait are usually based on tracking the lower body motion (hip, foot) and are extracted from monocular camera data. However, such data can be inaccessible in crowded environments where occlusions of the lower body are prevalent. This paper proposes a novel approach to utilize upper body 3D-motion and Hierarchical Hidden Markov Models to estimate human ambulatory states, such as quietly standing, starting to walk (gait initiation), walking (gait cycle), or stopping (gait termination). Methods have been tested on real data acquired through a motion capture system where foot measurements (heels and toes) were used as ground truth data for labeling the states to train and test the models. Current results demonstrate the feasibility of using such a system to infer lower-body motion states and sub-states through observations of 3D shoulder motion online. Our results enable applications in situations where only upper body motion is readily observable

Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns

Wearable device-based gait recognition using angle embedded gait dynamic images and a convolutional neural network

The widespread installation of inertial sensors in smartphones and other wearable devices provides a valuable opportunity to identify people by analyzing their gait patterns, for either cooperative or non-cooperative circumstances. However, it is still a challenging task to reliably extract discriminative features for gait recognition with noisy and complex data sequences collected from casually worn wearable devices like smartphones. To cope with this problem, we propose a novel image-based gait recognition approach using the Convolutional Neural Network (CNN) without the need to manually extract discriminative features. The CNN’s input image, which is encoded straightforwardly from the inertial sensor data sequences, is called Angle Embedded Gait Dynamic Image (AE-GDI). AE-GDI is a new two-dimensional representation of gait dynamics, which is invariant to rotation and translation. The performance of the proposed approach in gait authentication and gait labeling is evaluated using two datasets: (1) the McGill University dataset, which is collected under realistic conditions; and (2) the Osaka University dataset with the largest number of subjects. Experimental results show that the proposed approach achieves competitive recognition accuracy over existing approaches and provides an effective parametric solution for identification among a large number of subjects by gait patterns

Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009

Dieser Tagungsband enthält die Beiträge des 19. Workshops „Computational Intelligence“ des Fachausschusses 5.14 der VDI/VDE-Gesellschaft für Mess- und Automatisierungstechnik (GMA) und der Fachgruppe „Fuzzy-Systeme und Soft-Computing“ der Gesellschaft für Informatik (GI), der vom 2.-4. Dezember 2009 im Haus Bommerholz bei Dortmund stattfindet

Using shape information from natural tree landmarks for improving SLAM performance

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references leaves 52-56.Localization and mapping are crucial components for robotic autonomy. However, such robots must often function in remote, outdoor areas with no a-priori knowledge of the environment. Consequently, it becomes necessary for field robots to be able to construct their own maps based on exteroceptive sensor readings. To this end, visual sensing and mapping through naturally occurring landmarks have distinct advantages. With the availability of high bandwidth data provided by visual sensors, meaningful and uniquely identifiable objects can be detected. This improves the construction of maps consisting of natural landmarks that are meaningful for human readers as well. In this thesis, we focus on the use of trees in an outdoor environment as a suitable set of landmarks for Simultaneous Localization and Mapping (SLAM). Trees have a relatively simple, near vertical structure which makes them easily and consistently detectable. Furthermore, the thickness of a tree can be accurately determined from different viewpoints. Our primary contribution is the usage of the width of a tree trunk as an additional sensory reading, allowing us to include the radius of tree trunks on the map. To this end, we introduce a new sensor model that relates the width of a tree landmark on the image plane to the radius of its trunk. We provide a mathematical formulation of this model, derive associated Jacobians and incorporate our sensor model into a working EKF SLAM implementation. Through simulations we show that the use of this new sensory reading improves the accuracy of both the map and the trajectory estimates without additional sensor hardware other than a monocular camera.Turan, BilalM.S

On-line Time Warping of Human Motion Sequences

Some application areas require motions to be time warped on-line as a motion is captured, aligning a partially captured motion to a complete prerecorded motion. For example movement training applications for dance and medical procedures, require on-line time warping for analysing and visually feeding back the accuracy of human motions as they are being performed. Additionally, real-time production techniques such as virtual production, in camera visual effects and the use of avatars in live stage performances, require on-line time warping to align virtual character performances to a live performer. The work in this thesis first addresses a research gap in the measurement of the alignment of two motions, proposing approaches based on rank correlation and evaluating them against existing distance based approaches to measuring motion similarity. The thesis then goes onto propose and evaluate novel methods for on-line time warping, which plot alignments in a forward direction and utilise forecasting and local continuity constraint techniques. Current studies into measuring the similarity of motions focus on distance based metrics for measuring the similarity of the motions to support motion recognition applications, leaving a research gap regarding the effectiveness of similarity metrics bases on correlation and the optimal metrics for measuring the alignment of two motions. This thesis addresses this research gap by comparing the performance of variety of similarity metrics based on distance and correlation, including novel combinations of joint parameterisation and correlation methods. The ability of each metric to measure both the similarity and alignment of two motions is independently assessed. This work provides a detailed evaluation of a variety of different approaches to using correlation within a similarity metric, testing their performance to determine which approach is optimal and comparing their performance against established distance based metrics. The results show that a correlation based metric, in which joints are parameterised using displacement vectors and correlation is measured using Kendall Tau rank correlation, is the optimal approach for measuring the alignment between two motions. The study also showed that similarity metrics based on correlation are better at measuring the alignment of two motions, which is important in motion blending and style transfer applications as well as evaluating the performance of time warping algorithms. It also showed that metrics based on distance are better at measuring the similarity of two motions, which is more relevant to motion recognition and classification applications. A number of approaches to on-line time warping have been proposed within existing research, that are based on plotting an alignment path backwards from a selected end-point within the complete motion. While these approaches work for discrete applications, such as recognising a motion, their lack of monotonic constraint between alignment of each frame, means these approaches do not support applications that require an alignment to be maintained continuously over a number of frames. For example applications involving continuous real-time visualisation, feedback or interaction. To solve this problem, a number of novel on-line time warping algorithms, based on forward plotting, motion forecasting and local continuity constraints are proposed and evaluated by applying them to human motions. Two benchmarks standards for evaluating the performance of on-line time warping algorithms are established, based on UTW time warping and compering the resulting alignment path with that produced by DTW. This work also proposes a novel approach to adapting existing local continuity constraints to a forward plotting approach. The studies within this thesis demonstrates that these time warping approaches are able to produce alignments of sufficient quality to support applications that require an alignment to be maintained continuously. The on-line time warping algorithms proposed in this study can align a previously recorded motion to a user in real-time, as they are performing the same action or an opposing action recorded at the same time as the motion being align. This solution has a variety of potential application areas including: visualisation applications, such as aligning a motion to a live performer to facilitate in camera visual effects or a live stage performance with a virtual avatar; motion feedback applications such as dance training or medical rehabilitation; and interaction applications such as working with Cobots

A complex systems approach to education in Switzerland

The insights gained from the study of complex systems in biological, social, and engineered systems enables us not only to observe and understand, but also to actively design systems which will be capable of successfully coping with complex and dynamically changing situations. The methods and mindset required for this approach have been applied to educational systems with their diverse levels of scale and complexity. Based on the general case made by Yaneer Bar-Yam, this paper applies the complex systems approach to the educational system in Switzerland. It confirms that the complex systems approach is valid. Indeed, many recommendations made for the general case have already been implemented in the Swiss education system. To address existing problems and difficulties, further steps are recommended. This paper contributes to the further establishment complex systems approach by shedding light on an area which concerns us all, which is a frequent topic of discussion and dispute among politicians and the public, where billions of dollars have been spent without achieving the desired results, and where it is difficult to directly derive consequences from actions taken. The analysis of the education system's different levels, their complexity and scale will clarify how such a dynamic system should be approached, and how it can be guided towards the desired performance