Learning articulated motion structures with Bayesian Networks

This paper presents a general methodology for learning articulated motions that, despite having non-linear correlations, are cyclical and have a defined pattern of behavior Using conventional algorithms to extract features from images, a Bayesian classifier is applied to cluster and classify features of the moving object. Clusters are then associated in different frames and structure learning algorithms for Bayesian networks are used to recover the structure of the motion. This framework is applied to the human gait analysis and tracking but applications include any coordinated movement such as multi-robots behavior analysis.

The pedagogic prosthetic: Augmented learning as content-in-motion in hybrid educational spheres

This article draws on the design and implementation of three mobile learning projects introduced by Flanagan in 2011, 2012 and 2014 engaging a total of 206 participants. The latest of these projects is highlighted in this article. Two other projects provide additional examples of innovative strategies to engage mobile and cloud systems describing how electronic and mobile technology can help facilitate teaching and learning, assessment for learning and assessment as learning, and support communities of practice. The second section explains the theoretical premise supporting the implementation of technology and promulgates a hermeneutic phenomenological approach. The third section discusses mobility, both in terms of the exploration of wearable technology in the prototypes developed as a result of the projects, and the affordances of mobility within pedagogy. Finally the quantitative and qualitative methods in place to evaluate m-learning are explained.

A constraints-led perspective to understanding skill acquisition and game play: A basis for integration of motor learning theory and physical education praxis?

Background: In order to design appropriate environments for performance and learning of movement skills, physical educators need a sound theoretical model of the learner and of processes of learning. In physical education, this type of modelling informs the organization of learning environments and effective and efficient use of practice time. An emerging theoretical framework in motor learning, relevant to physical education, advocates a constraints-led perspective for acquisition of movement skills and game play knowledge. This framework shows how physical educators could use task, performer and environmental constraints to channel acquisition of movement skills and decision making behaviours in learners. From this viewpoint, learners generate specific movement solutions to satisfy the unique combination of constraints imposed on them, a process which can be harnessed during physical education lessons. Purpose: In this paper the aim is to provide an overview of the motor learning approach emanating from the constraints-led perspective, and examine how it can substantiate a platform for a new pedagogical framework in physical education: nonlinear pedagogy. We aim to demonstrate that it is only through theoretically valid and objective empirical work of an applied nature that a conceptually sound nonlinear pedagogy model can continue to evolve and support research in physical education. We present some important implications for designing practices in games lessons, showing how a constraints-led perspective on motor learning could assist physical educators in understanding how to structure learning experiences for learners at different stages, with specific focus on understanding the design of games teaching programmes in physical education, using exemplars from Rugby Union and Cricket. Findings: Research evidence from recent studies examining movement models demonstrates that physical education teachers need a strong understanding of sport performance so that task constraints can be manipulated so that information-movement couplings are maintained in a learning environment that is representative of real performance situations. Physical educators should also understand that movement variability may not necessarily be detrimental to learning and could be an important phenomenon prior to the acquisition of a stable and functional movement pattern. We highlight how the nonlinear pedagogical approach is student-centred and empowers individuals to become active learners via a more hands-off approach to learning. Summary: A constraints-based perspective has the potential to provide physical educators with a framework for understanding how performer, task and environmental constraints shape each individual‟s physical education. Understanding the underlying neurobiological processes present in a constraints-led perspective to skill acquisition and game play can raise awareness of physical educators that teaching is a dynamic 'art' interwoven with the 'science' of motor learning theories.

Learning object dynamics for smooth tracking of moving lip contours

A new technique is proposed for learning the dynamic characteristics of a deformable object, applied in particular to the problem of lip-tracking. Experimental results are given which demonstrate that the use of dynamic models allows the system to track more robustly under adverse conditions and to correct spurious, poorly tracked frames

Implementing the self-management threshold learning outcome for law : some intentional design strategies from the current curriculum toolbox

There is a growing awareness of the high levels of psychological distress being experienced by law students and the practising profession in Australia. In this context, a Threshold Learning Outcome (TLO) on self-management has been included in the six TLOs recently articulated as minimum learning outcomes for all Australian graduates of the Bachelor of Laws degree (LLB). The TLOs were developed during 2010 as part of the Australian Learning and Teaching Council’s (ALTC’s) project funded by the Australian Government to articulate ‘Learning and Teaching Academic Standards’. The TLOs are the result of a comprehensive national consultation process led by the ALTC’s Discipline Scholars: Law, Professors Sally Kift and Mark Israel.1 The TLOs have been endorsed by the Council of Australian Law Deans (CALD) and have received broad support from members of the judiciary and practising profession, representative bodies of the legal profession, law students and recent graduates, Legal Services Commissioners and the Law Admissions Consultative Committee. At the time of writing, TLOs for the Juris Doctor (JD) are also being developed, utilising the TLOs articulated for the LLB as their starting point but restating the JD requirements as the higher order outcomes expected of graduates of a ‘Masters Degree (Extended)’, this being the award level designation for the JD now set out in the new Australian Qualifications Framework.2 As Australian law schools begin embedding the learning, teaching and assessment of the TLOs in their curricula, and seek to assure graduates’ achievement of them, guidance on the implementation of the self-management TLO is salient and timely.

The Threshold Learning Outcome on self-management for the Bachelor of Laws degree : a proposed focus for teaching strategies in the first year law curriculum

The Australian Learning and Teaching Council (ALTC) Discipline Scholars for Law, Professors Sally Kift and Mark Israel, articulated six Threshold Learning Outcomes (TLOs) for the Bachelor of Laws degree as part of the ALTC’s 2010 project on Learning and Teaching Academic Standards. One of these TLOs promotes the learning, teaching and assessment of self-management skills in Australian law schools. This paper explores the concept of self-management and how it can be relevantly applied in the first year of legal education. Recent literature from the United States (US) and Australia provides insights into the types of issues facing law students, as well as potential antidotes to these problems. Based on these findings, I argue that designing a pedagogical framework for the first year law curriculum that promotes students’ connection with their intrinsic interests, values, motivations and purposes will facilitate student success in terms of their personal well-being, ethical dispositions and academic engagement.

Assessing the structure of learning outcomes from counselling using the SOLO taxonomy: an exploratory study

Thirty-five clients who had received counselling completed a letter to a friend describing in as much detail as possible what they had learned from counselling. The participants' written responses were analysed and classified using the Structure of Learning Outcomes (SOLO) taxonomy. The results suggested that an expanded SOLO offers a promising and exciting way to view the outcomes of counselling within a learning framework. If the SOLO taxonomy is found to be stable in subsequent research, and clients are easily able to be classified using the taxonomy, then this approach may have implications for the process of counselling. To maximise the learning outcomes, counsellors could use strategies and techniques to enhance their clients' learning.

A robust structure and motion replacement for bundle adjustment

This paper demonstrates the application of a robust form of pose estimation and scene reconstruction using data from camera images. We demonstrate results that suggest the ability of the algorithm to rival methods of RANSAC based pose estimation polished by bundle adjustment in terms of solution robustness, speed and accuracy, even when given poor initialisations. Our simulated results show the behaviour of the algorithm in a number of novel simulated scenarios reflective of real world cases that show the ability of the algorithm to handle large observation noise and difficult reconstruction scenes. These results have a number of implications for the vision and robotics community, and show that the application of visual motion estimation on robotic platforms in an online fashion is approaching real-world feasibility.

Developing the Pattern and Structure Assessment (PASA) interview to inform early mathematics learning

The Pattern and Structure Mathematical Awareness Program(PASMAP) stems from a 2-year longitudinal study on students’ early mathematical development. The paper outlines the interview assessment the Pattern and Structure Assessment(PASA) designed to describe students’ awareness of mathematical pattern and structure across a range of concepts. An overview of students’ performance across items and descriptions of their structural development are described.

Graph rigidity for near-coplanar structure from motion

Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.

Learning hierarchical prototypes of motion time series for interactive systems

Abstract. For interactive systems, recognition, reproduction, and generalization of observed motion data are crucial for successful interaction. In this paper, we present a novel method for analysis of motion data that we refer to as K-OMM-trees. K-OMM-trees combine Ordered Means Models (OMMs) a model-based machine learning approach for time series with an hierarchical analysis technique for very large data sets, the K-tree algorithm. The proposed K-OMM-trees enable unsupervised prototype extraction of motion time series data with hierarchical data representation. After introducing the algorithmic details, we apply the proposed method to a gesture data set that includes substantial inter-class variations. Results from our studies show that K-OMM-trees are able to substantially increase the recognition performance and to learn an inherent data hierarchy with meaningful gesture abstractions.

STALKERBOT : learning to navigate dynamic human environments by following people

Service robots that operate in human environments will accomplish tasks most efficiently and least disruptively if they have the capability to mimic and understand the motion patterns of the people in their workspace. This work demonstrates how a robot can create a humancentric navigational map online, and that this map re ects changes in the environment that trigger altered motion patterns of people. An RGBD sensor mounted on the robot is used to detect and track people moving through the environment. The trajectories are clustered online and organised into a tree-like probabilistic data structure which can be used to detect anomalous trajectories. A costmap is reverse engineered from the clustered trajectories that can then inform the robot's onboard planning process. Results show that the resultant paths taken by the robot mimic expected human behaviour and can allow the robot to respond to altered human motion behaviours in the environment.

Reconceptualizing Early Mathematics Learning : The Fundamental Role of Pattern and Structure

The Pattern and Structure Mathematics Awareness Program (PASMAP) was developed concurrently with the studies of AMPS and the development of the Pattern and Structure Assessment (PASA) interview. We summarize some early classroom-based teaching studies and describe the PASMAP that resulted. A large-scale two-year longitudinal study, Reconceptualizing Early Mathematics Learning (REML) resulted. We provide an overview of the REML study and discuss the consequences for our view of early mathematics learning. A purposive sample of four large primary schools, two in Sydney and two in Brisbane, representing 316 students from diverse socio-economic and cultural contexts, participated in an evaluation of the PASMAP intervention throughout the 2009 school year and a follow-up assessment in 2010. Two different mathematics programs were implemented: in each school, two Kindergarten teachers implemented the PASMAP and another two implemented their regular program. The study shows that both groups of students made substantial gains on the ‘I Can Do Maths’ standardized assessment and the PASA interview, but highly significant differences were found on the latter with PASMAP students outperforming the regular group on PASA scores. Qualitative analysis of students’ responses for structural development showed increased levels for the PASMAP students. Implications for pedagogy and curriculum are discussed.