Semantic Segmentation of Motion Capture Using Laban Movement Analysis

Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. This paper presents an automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA). LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features which, often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features

Game Development for Computer Science Education (Extended Abstract)

Educators have long used digital games as platforms for teaching. Games tend to have several qualities that aren’t typically found in homework: they situate problems within a compelling alternate reality that unfolds through intriguing narrative, they draw more upon a player’s intrinsic motivations than extrinsic ones, they facilitate deliberate low intensity practice, and they emphasize a spirit of play instead of work. At ITiCSE 2016, this working group convened to survey the landscape of existing digital games that have been used to teach and learn computer science concepts. Our group discovered that these games lacked explicitly defined learning goals and even less evaluation of whether or not the games achieved these goals. As part of this process, we identified and played over 120 games that have been released or described in literature as means for learning computer science concepts. In our report, we classified how these games support the learning objectives outlined in the ACM/IEEE Computer Science Curricula 2013. While we found more games than we expected, few games explicitly stated their learning goals and even fewer were evaluated for their capacity to meet these goals. Most of the games we surveyed fell into two categories: short-lived proof-of-concept projects built by academics or closed-source games built by professional developers. Gathering adequate learning data is challenging in either situation. Our original intent for the second year of our working group was to prepare a comprehensive framework for collecting and analyzing learning data from computer science learning games. Upon further discussion, however, we decided that a better next step is to validate the design and development guidelines that we put forth in our final report for ITiCSE 2016. We extend this working group to a second year—with a mission to collaboratively develop a game with clearly defined learning objectives and define a methodology for evaluating its capacity to meet its goals

Automated motion capture segmentation using Laban Movement Analysis

Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. An automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA) is presented. LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features, which often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features. Segmentations produced using LMA features are more similar to manual segmentations, both at the frame and segment level, than are several other automatic segmentation methods. The LMA based segmentation method has the added benefit of improving the performance classifiers for which it segments the input. This has implications in many areas that utilize motion capture analysis including human-computer interaction and computer animation

Automated motion capture segmentation using Laban Movement Analysis

Many applications that utilize motion capture data require small, discrete, semantic segments of data, but most motion capture collection processes produce long sequences of data. The smaller segments are often created from the longer sequences manually. This segmentation process is very laborious and time consuming. An automatic motion capture segmentation method based on movement qualities derived from Laban Movement Analysis (LMA) is presented. LMA provides a good compromise between high-level semantic features, which are difficult to extract for general motions, and low-level kinematic features, which often yield unsophisticated segmentations. The LMA features are computed using a collection of neural networks trained with temporal variance in order to create a classifier that is more robust with regard to input boundaries. The actual segmentation points are derived through simple time series analysis of the LMA features. Segmentations produced using LMA features are more similar to manual segmentations, both at the frame and segment level, than are several other automatic segmentation methods. The LMA based segmentation method has the added benefit of improving the performance classifiers for which it segments the input. This has implications in many areas that utilize motion capture analysis including human-computer interaction and computer animation

If Memory Serves: Towards Designing and Evaluating a Game for Teaching Pointers to Undergraduate Students

Games can serve as a valuable tool for enriching computer science education, since they can facilitate a number of conditions that can promote learning and instigate affective change. As part of the 22nd ACM Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE 2017), the Working Group on Game Development for Computer Science Education convened to extend their prior work, a review of the literature and a review of over 120 educational games that support computing instruction. The Working Group builds off this earlier work to design and develop a prototype of a game grounded in specific learning objectives. They provide the source code for the game to the computing education community for further review, adaptation, and exploration. To aid this endeavor, the Working Group also chose to explore the research methods needed to establish validity, highlighting a need for more rigorous approaches to evaluate the effectiveness of the use of games in computer science education. This report provides two distinct contributions to the body of knowledge in games for computer science education. We present an experience report in the form of a case study describing the design and development of If Memory Serves, a game to support teaching pointers to undergraduate students. We then propose guidelines to validate its effectiveness rooted in theoretical approaches for evaluating learning in games and media. We include an invitation to the computer science education community to explore the game's potential in classrooms and report on its ability to achieve the stated learning outcomes