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Indoor localization techniques proposed to date have assumed costly resources in terms of computation, power, or sensing modality for many wearable end-devices in the Internet of .ings (IoT). To make localization a universal feature for IoT devices, we propose EcoLoc, an indoor localization system using collaborative version of Conditional Random Fields (CCRF) integrated with our encounter model to...
We address the problem of feature space dimensionality reduction for the recognition of whole-body human action based on Hidden Markov Models. First, we describe how different features are derived from marker-based human motion capture and define a total number of 29 features with a total of 702 dimensions to describe human motion. We then propose a strategy for a systematic exploration of the space...
Shared control is a promising approach for designing an Advanced Driver Assistance System, since it unifies the advantages of both manual control and full automation. However, for a true cooperative shared control ADAS the automation has to understand the human and thus a suitable model which describes the driver in the control loop is essential. Our gray-box approach bases on the biological concept...
RNA-seq is exponentially becoming the de facto standard approach to compel considerable advantages over conventional technologies such as micro array by directly sequencing transcripts in gene expression profile. As the cost to sequencing is dropping rapidly, studies to dynamic change of gene expression in a given biological system over time have shown steady growth over the past few years as micro...
We study the use of different weighting mechanisms in robot learning to represent a movement as a combination of linear systems. Kinesthetic teaching is used to acquire a skill from demonstrations which is then reproduced by the robot. The behaviors of the systems are analyzed when the robot faces perturbation introduced by the user physically interacting with the robot to momentarily stop the task...
We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learning robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time-independent models to reproduce the dynamics of the demonstrated movements. The approach is systematically evaluated by using automatically generated...
Human activity analysis is an important problem in computer vision with applications in surveillance and summarization and indexing of consumer content. Complex human activities are characterized by non-linear dynamics that make learning, inference and recognition hard. In this paper, we consider the problem of modeling and recognizing complex activities which exhibit time-varying dynamics. To this...
The task of clustering multivariate trajectory data of varying length exists in various domains. Model-based methods are capable of handling varying length trajectories without changing the length or structure. Hidden Markov models (HMMs) are widely used for trajectory data modeling. However, HMMs are not suitable for trajectories of long duration. In this paper, we propose a similarity based representation...
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