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In this paper, we address natural human-robot interaction (HRI) in a smart assisted living (SAIL) system for the elderly and the disabled. Two common HRI problems are studied: hand gesture recognition and daily activity recognition. For hand gesture recognition, we implemented a neural network for gesture spotting and a hierarchical hidden Markov model for context-based recognition. For daily activity...
In this work, we address the problem of learning arm gestures from imitation by humanoid robots when the training set contains missing data. We assume that multiple gesture demonstrations are available. The problem is challenging because of the fact that there is no temporal alignment between the demonstrations. In this work, we propose two approaches to handle the missing data problem. One approach...
This paper presents a platform to implement and evaluate a learning by imitation framework which enables humanoid robots to learn hand gestures from human beings. A marker based system is used to capture human motion data. From this data we extract the shoulder and elbow joint angles, which uniquely characterize a particular hand gesture. The proposed imitation learning framework aims to generalize...
In this paper, we propose an online hand gesture recognition algorithm for a robot assisted living system. A neural network-based gesture spotting method is combined with the hierarchical hidden Markov model (HHMM) to recognize hand gestures. In the segmentation module, the neural network is used to determine whether the HHMM-based recognition module should be applied. In the recognition module, Bayesian...
In this paper, we propose a smart assisted living (SAIL) System and design a hierarchical hidden Markov model (HHMM) based algorithm for human intention recognition. We focus on the problem of classifying hand gestures by using a single inertial sensor worn on a finger of the subject. The variation of context information, which is modeled by an HMM is used to improve the accuracy of hand gesture recognition...
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