3 research outputs found
Analyzing and Learning Movement Through Human-Computer Co-Creative Improvisation and Data Visualization
Recent years have seen an incredible rise in the availability of household motion and video capture technologies, ranging from the humble webcam to the relatively sophisticated Kinect sensor. Naturally, this precipitated a rise in both the quantity and quality of motion capture data available on the internet. The wealth of data on the internet has caused a new interest in the field of motion data classification, the specific task of having a model classify and sort different clips of human motion. However, there is comparatively little work in the field of motion data clustering, which is an unsupervised field that may be more useful in the future as it allows for agents to recognize âcategoriesâ of motions without the need for user input or classified data. Systems that can cluster motion data focus more on âwhat type of motion data is this, and what is it similar toâ rather than which motion is this. The LuminAI project, as described in this paper, is an example of a practical use for motion data clustering that allows the system to respond to user dance moves with a similar but different gesture. To analyze the efficacy and properties of this motion data clustering pipeline, we also propose a novel data visualization tool and the design considerations involved in its development.Undergraduat
Automated Testing of Motion-based Events in Mobile Applications
Automated test case generation is one of the main challenges in testing mobile applications. This challenge becomes more complicated when the application being tested supports motion-based events. In this paper, we propose a novel, hidden Markov model (HMM)-based approach to automatically generate movement-based gestures in mobile applications. A HMM classifier is used to generate movements, which mimic a userâs behaviour in interacting with the applicationâs User Interface (UI). We evaluate the proposed technique on three different case studies; the evaluation indicates that the technique not only generates realistic test cases, but also achieves better code coverage when compared to randomly generated test case
Riemannian Multi-Manifold Modeling
This paper advocates a novel framework for segmenting a dataset in a
Riemannian manifold into clusters lying around low-dimensional submanifolds
of . Important examples of , for which the proposed clustering algorithm
is computationally efficient, are the sphere, the set of positive definite
matrices, and the Grassmannian. The clustering problem with these examples of
is already useful for numerous application domains such as action
identification in video sequences, dynamic texture clustering, brain fiber
segmentation in medical imaging, and clustering of deformed images. The
proposed clustering algorithm constructs a data-affinity matrix by thoroughly
exploiting the intrinsic geometry and then applies spectral clustering. The
intrinsic local geometry is encoded by local sparse coding and more importantly
by directional information of local tangent spaces and geodesics. Theoretical
guarantees are established for a simplified variant of the algorithm even when
the clusters intersect. To avoid complication, these guarantees assume that the
underlying submanifolds are geodesic. Extensive validation on synthetic and
real data demonstrates the resiliency of the proposed method against deviations
from the theoretical model as well as its superior performance over
state-of-the-art techniques