Clustering-based Human Locomotion Parameters for Motion Type Classification
نویسندگان
چکیده
منابع مشابه
Force-Based Motion Editing for Locomotion Tasks
This paper describes a fast technique for modifying motion sequences for complex articulated mechanisms in a way that preserves physical properties of the motion. This technique is relevant to the problem of teaching motion tasks by demonstration, because it allows a single example to be adapted to a range of situations. Motion may be obtained from any source; for example, it may be captured fr...
متن کاملOn Model-Based Clustering, Classification, and Discriminant Analysis
The use of mixture models for clustering and classification has burgeoned into an important subfield of multivariate analysis. These approaches have been around for a half-century or so, with significant activity in the area over the past decade. The primary focus of this paper is to review work in model-based clustering, classification, and discriminant analysis, with particular attenti...
متن کاملGraph-Based Action Models for Human Motion Classification
Recognizing human actions is an important ability for service and domestic robots. This paper presents a novel approach for learning and recognizing motion models from human motion capturing data. The key idea is to represent observed motion trajectories as a graph, where the nodes correspond to poses and the edges indicate pose similarities. We optimize this graph using least squares minimizat...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملWavelet Statistics for Human Motion Classification
Human motion is as much characterized by its low frequency shape as by its high frequency temporal discontinuities – such as when a joint reaches its physical limit or when a foot touches the floor. Wavelets are particularly efficient at capturing both high and low frequency information. We introduce a method of classifying human motion using wavelet coefficients to build a representation of hu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Studies in Informatics and Control
سال: 2016
ISSN: 1220-1766,1841-429X
DOI: 10.24846/v25i3y201609