179,082 research outputs found
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
Sensing and mapping for interactive performance
This paper describes a trans-domain mapping (TDM) framework for translating meaningful activities from one creative domain onto another. The multi-disciplinary framework is designed to facilitate an intuitive and non-intrusive interactive multimedia performance interface that offers the users or performers real-time control of multimedia events using their physical movements. It is intended to be a highly dynamic real-time performance tool, sensing and tracking activities and changes, in order to provide interactive multimedia performances.
From a straightforward definition of the TDM framework, this paper reports several implementations and multi-disciplinary collaborative projects using the proposed framework, including a motion and colour-sensitive system, a sensor-based system for triggering musical events, and a distributed multimedia server for audio mapping of a real-time face tracker, and discusses different aspects of mapping strategies in their context.
Plausible future directions, developments and exploration with the proposed framework, including stage augmenta tion, virtual and augmented reality, which involve sensing and mapping of physical and non-physical changes onto multimedia control events, are discussed
Distributed Learning System Design: A New Approach and an Agenda for Future Research
This article presents a theoretical framework designed to guide distributed learning design, with the goal of enhancing the effectiveness of distributed learning systems. The authors begin with a review of the extant research on distributed learning design, and themes embedded in this literature are extracted and discussed to identify critical gaps that should be addressed by future work in this area. A conceptual framework that integrates instructional objectives, targeted competencies, instructional design considerations, and technological features is then developed to address the most pressing gaps in current research and practice. The rationale and logic underlying this framework is explicated. The framework is designed to help guide trainers and instructional designers through critical stages of the distributed learning system design process. In addition, it is intended to help researchers identify critical issues that should serve as the focus of future research efforts. Recommendations and future research directions are presented and discussed
RGB-D-based Action Recognition Datasets: A Survey
Human action recognition from RGB-D (Red, Green, Blue and Depth) data has
attracted increasing attention since the first work reported in 2010. Over this
period, many benchmark datasets have been created to facilitate the development
and evaluation of new algorithms. This raises the question of which dataset to
select and how to use it in providing a fair and objective comparative
evaluation against state-of-the-art methods. To address this issue, this paper
provides a comprehensive review of the most commonly used action recognition
related RGB-D video datasets, including 27 single-view datasets, 10 multi-view
datasets, and 7 multi-person datasets. The detailed information and analysis of
these datasets is a useful resource in guiding insightful selection of datasets
for future research. In addition, the issues with current algorithm evaluation
vis-\'{a}-vis limitations of the available datasets and evaluation protocols
are also highlighted; resulting in a number of recommendations for collection
of new datasets and use of evaluation protocols
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