Natural Feature Tracking Augmented Reality for On-Site Assembly Assistance Systems

We introduce a natural feature tracking approach that facilitates the tracking of rigid objects for an on-site assembly assistance system. The tracking system must track multiple circuit boards without added fiducial markers, and they are manipulated by the user. We use a common SIFT feature matching detector enhanced with a probability search. This search estimates how likely a set of query descriptors belongs to a particular object. The method was realized and tested. The results show that the probability search enhanced the identification of different circuit boards

A global research priority agenda to advance public health responses to fatty liver disease

Background & aims An estimated 38% of adults worldwide have non-alcoholic fatty liver disease (NAFLD). From individual impacts to widespread public health and economic consequences, the implications of this disease are profound. This study aimed to develop an aligned, prioritised fatty liver disease research agenda for the global health community. Methods Nine co-chairs drafted initial research priorities, subsequently reviewed by 40 core authors and debated during a three-day in-person meeting. Following a Delphi methodology, over two rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the priorities, via Qualtrics XM, indicating agreement using a four-point Likert-scale and providing written feedback. The core group revised the draft priorities between rounds. In R2, panellists also ranked the priorities within six domains: epidemiology, models of care, treatment and care, education and awareness, patient and community perspectives, and leadership and public health policy. Results The consensus-built fatty liver disease research agenda encompasses 28 priorities. The mean percentage of ‘agree’ responses increased from 78.3 in R1 to 81.1 in R2. Five priorities received unanimous combined agreement (‘agree’ + ‘somewhat agree’); the remaining 23 priorities had >90% combined agreement. While all but one of the priorities exhibited at least a super-majority of agreement (>66.7% ‘agree’), 13 priorities had 90% combined agreement. Conclusions Adopting this multidisciplinary consensus-built research priorities agenda can deliver a step-change in addressing fatty liver disease, mitigating against its individual and societal harms and proactively altering its natural history through prevention, identification, treatment, and care. This agenda should catalyse the global health community’s efforts to advance and accelerate responses to this widespread and fast-growing public health threat. Impact and implications An estimated 38% of adults and 13% of children and adolescents worldwide have fatty liver disease, making it the most prevalent liver disease in history. Despite substantial scientific progress in the past three decades, the burden continues to grow, with an urgent need to advance understanding of how to prevent, manage, and treat the disease. Through a global consensus process, a multidisciplinary group agreed on 28 research priorities covering a broad range of themes, from disease burden, treatment, and health system responses to awareness and policy. The findings have relevance for clinical and non-clinical researchers as well as funders working on fatty liver disease and non-communicable diseases more broadly, setting out a prioritised, ranked research agenda for turning the tide on this fast-growing public health threat

Learning Kinematics from Direct Self-Observation using Nearest-Neighbor Methods

Abstract Commonly, the inverse kinematic function of robotic manipulators is derived analytically from the robot model. However, there are cases in which a model is not a priori available. In this paper, we propose an approach that enables an autonomous robot to estimate the inverse kinematic function on-the-fly directly from self-observation and without a given kinematic model. The robot executes randomly sampled joint configurations and observes the resulting world positions. To approximate the inverse kinematic function, we propose to use instance-based learning techniques such as Nearest Neighbor and Linear Weighted Regression. After learning, the robot can take advantage of the learned model to build roadmaps for motion planning. A further advantage of our approach is that the environment can implicitly be represented by the sample configurations. We analyze properties of this approach and present results obtained from experiments on a real 6-DOF robot and from simulation. We show that our approach allows us to accurately control robots with unknown kinematic models of various complexity and joint types.

Teaching undergraduate marketing students using 'hot seating through puppetry': An exploratory study

Changes in preferred methods of learning among many students in recent years have challenged educators to introduce more interactive and experiential teaching methods. 'Hot seating' - where a person, such as an invited subject expert is interviewed by an audience - is a well-established interactive method of learning, but is often limited by availability of willing and suitable interviewees. In this exploratory study, university business undergraduates were required to interact with a lecturer-operated puppet representing a corporate client interviewee in a simulated sales presentation. Reflective diaries were used to gain insights into students' perceptions of this teaching technique. Results suggest that students: (i) gained practical business skills; (ii) were exposed to commercial responsibilities and (iii) assimilated relevant academic theory. Benefits and limitations of 'hot seating through puppetry' and its possible contribution to teaching and learning in a variety of contexts are discussed, together with suggestions for further research