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Dana Kulic

Bio: Dana Kulic is an academic researcher from Monash University. The author has contributed to research in topics: Robot & Motion capture. The author has an hindex of 33, co-authored 228 publications receiving 5789 citations. Previous affiliations of Dana Kulic include Northwestern University & University of Tokyo.


Papers
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Journal ArticleDOI
TL;DR: A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety, distilled into five consistent questionnaires using semantic differential scales.
Abstract: This study emphasizes the need for standardized measurement tools for human robot interaction (HRI). If we are to make progress in this field then we must be able to compare the results from different studies. A literature review has been performed on the measurements of five key concepts in HRI: anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety. The results have been distilled into five consistent questionnaires using semantic differential scales. We report reliability and validity indicators based on several empirical studies that used these questionnaires. It is our hope that these questionnaires can be used by robot developers to monitor their progress. Psychologists are invited to further develop the questionnaires by adding new concepts, and to conduct further validations where it appears necessary.

1,889 citations

Proceedings ArticleDOI
03 Nov 2017
TL;DR: The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability.
Abstract: While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson’s Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54% to 86.88%.

348 citations

Journal ArticleDOI
TL;DR: An approach for on-line, incremental learning of full body motion primitives from observation of human motion using hidden Markov models, so that the same model can be used for both motion recognition and motion generation.
Abstract: In this paper we describe an approach for on-line, incremental learning of full body motion primitives from observation of human motion. The continuous observation sequence is first partitioned into motion segments, using stochastic segmentation. Next, motion segments are incrementally clustered and organized into a hierarchical tree structure representing the known motion primitives. Motion primitives are encoded using hidden Markov models, so that the same model can be used for both motion recognition and motion generation. At the same time, the temporal relationship between motion primitives is learned via the construction of a motion primitive graph. The motion primitive graph can then be used to construct motions, consisting of sequences of motion primitives. The approach is implemented and tested during on-line observation and on the IRT humanoid robot.

229 citations

Journal ArticleDOI
TL;DR: A novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion, abstracted into a dynamic stochastic model, analogous to the mirror neuron hypothesis in primates is described.
Abstract: This paper describes a novel approach for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a dynamic stochastic model, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. The model size is adaptable based on the discrimination requirements in the associated region of the current knowledge base. A new algorithm for sequentially training the Markov chains is developed, to reduce the computation cost during model adaptation. As new motion patterns are observed, they are incrementally grouped together using hierarchical agglomerative clustering based on their relative distance in the model space. The clustering algorithm forms a tree structure, with specialized motions at the tree leaves, and generalized motions closer to the root. The generated tree structure will depend on the type of training data provided, so that the most specialized motions will be those for which the most training has been received. Tests with motion capture data for a variety of motion primitives demonstrate the efficacy of the algorithm.

226 citations

Journal ArticleDOI
TL;DR: The implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus, and the results of the HMM affective estimation are compared to a previously implemented fuzzy inference engine.
Abstract: In order for humans and robots to interact in an effective and intuitive manner, robots must obtain information about the human affective state in response to the robot's actions. This secondary mode of interactive communication is hypothesized to permit a more natural collaboration, similar to the "body language" interaction between two cooperating humans. This paper describes the implementation and validation of a hidden Markov model (HMM) for estimating human affective state in real time, using robot motions as the stimulus. Inputs to the system are physiological signals such as heart rate, perspiration rate, and facial muscle contraction. Affective state was estimated using a two- dimensional valence-arousal representation. A robot manipulator was used to generate motions expected during human-robot interaction, and human subjects were asked to report their response to these motions. The human physiological response was also measured. Robot motions were generated using both a nominal potential field planner and a recently reported safe motion planner that minimizes the potential collision forces along the path. The robot motions were tested with 36 subjects. This data was used to train and validate the HMM model. The results of the HMM affective estimation are also compared to a previously implemented fuzzy inference engine.

216 citations


Cited by
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Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 1990
TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

2,933 citations

Journal ArticleDOI
TL;DR: In some religious traditions, the myth of the ‘Fall from the Garden of Eden’ symbolizes the loss of the primordial state through the veiling of higher consciousness.
Abstract: Human beings are described by many spiritual traditions as ‘blind’ or ‘asleep’ or ‘in a dream.’ These terms refers to the limited attenuated state of consciousness of most human beings caught up in patterns of conditioned thought, feeling and perception, which prevent the development of our latent, higher spiritual possibilities. In the words of Idries Shah: “Man, like a sleepwalker who suddenly ‘comes to’ on some lonely road has in general no correct idea as to his origins or his destiny.” In some religious traditions, such as Christianity and Islam, the myth of the ‘Fall from the Garden of Eden’ symbolizes the loss of the primordial state through the veiling of higher consciousness. Other traditions use similar metaphors to describe the spiritual condition of humanity:

2,223 citations

01 Jan 2014
TL;DR: Using Language部分的�’学模式既不落俗套,又能真正体现新课程标准所倡导的�'学理念,正是年努力探索的问题.
Abstract: 人教版高中英语新课程教材中,语言运用(Using Language)是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

2,071 citations