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Ben Kröse

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Publication statistics

Pub. period:2008-2011
Pub. count:4
Number of co-authors:4

Co-authors

Number of publications with 3 favourite co-authors:

Vanessa Evers:3
Bob Wielinga:2
Marcel Heerink:2

Productive colleagues

Ben Kröse's 3 most productive colleagues in number of publications:

Vanessa Evers:33
Hayley Hung:6
Bob Wielinga:5

Calendar

May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

May 29

Intl. Workshop on Design and Spontaneity in Computer-Supported Collaborative Learning (DS-CSCL 2013)

May 22

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Ben Kröse

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Publications by Ben Kröse (bibliography)

2011

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Hung, Hayley and Kröse, Ben (2011): Detecting F-formations as dominant sets. In: Proceedings of the 2011 International Conference on Multimodal Interfaces 2011. pp. 231-238.

“The first step towards analysing social interactive behaviour in crowded environments is to identify who is interacting with whom. This paper presents a new method for detecting focused encounters or F-formations in a crowded, real-life social environment. An F-formation is a specific instance of a group of people who are congregated together with the intent of conversing and exchanging information with each other. We propose a new method of estimating F-formations using a graph clustering algorithm by formulating the problem in terms of identifying dominant sets. A dominant set is a form of maximal clique which occurs in edge weighted graphs. As well as using the proximity between people, body orientation information is used; we propose a socially motivated estimate of focus orientation (SMEFO), which is calculated with location information only. Our experiments show significant improvements in performance over the existing modularity cut algorithm and indicates the effectiveness of using a local social context for detecting F-formations.”

2010

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Evers, Vanessa and Kröse, Ben (2010): Toward an ambient empathic health companion for self care in the intelligent home. In: Proceedings of the 2010 Annual European Conference on Cognitive Ergonomics 2010. pp. 365-366.

“Motivation -- This paper describes our work in progress to develop a personal monitoring system that can monitor the physical and emotional condition of a patient by using contextual information from a sensor network, provide the patient with feedback concerning their health status and motivate the patient to adopt behavior with a positive health impact (such as exercising or taking medication at the right moment). Research approach -- We will extend the capabilities of an existing robotic health buddy with a (DBN based) sensor network. Then we will carry out a series of controlled, long-term field experiments where we identify and evaluate the effects of various agent social communicative behaviours on the user's adoption of health improving lifestyle patterns. Findings/Design -- The findings of the experiments will inform the final design of the health buddy and it's behaviours. We will also realise system adaptivity of the data processing and data fusion methods as well as the health buddy adaptivity to the user's emotional state. Research limitations/Implications -- The project will limit itself to monitoring and motivating people who suffer from cardiovascular chronic conditions and to the home environment. Originality/Value -- The research makes a contribution to the needs of health monitoring for a specific user group. The health buddy will use social behaviours to motivate users over a long-term time period. Take away message -- Home health monitoring and self care can be more enjoyable and easier through motivating smart health buddies.”

2009

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Heerink, Marcel, Kröse, Ben, Wielinga, Bob and Evers, Vanessa (2009): Measuring the influence of social abilities on acceptance of an interface robot and a screen agent by elderly users. In: Proceedings of the HCI09 Conference on People and Computers XXIII 2009. pp. 430-439.

“Personal robots and screen agents can be equipped with social abilities to facilitate interaction. This paper describes our research on the influence of these abilities on elderly user's acceptance of such a system. Experiments were set up in eldercare institutions where a robotic and screen agent with simulated conversational capabilities were used in a Wizard of Oz experiment. Both agents were used with two conditions: a more socially communicative (the agent made use of a larger set of social abilities in interaction) and a less socially communicative interface. Results show that participants who were confronted with the more socially communicative version of the robotic agent felt more comfortable and were more expressive in communicating with it. This suggests that the more socially communicative condition would be more likely to be accepted as a conversational partner. This effect was less strong however, with the screen agent, suggesting that embodiment plays a role in this. Furthermore, results did show a correlation between social abilities as perceived by participants and some aspects of technology acceptance for both systems, but this did not relate to the more and less socially communicative conditions. Evaluating the experiments and specifically the use of our acceptance model we suggest that this particular context of robotic and screen agents for elderly users requires the development of a more appropriate acceptance model which not only features technology acceptance, but also conversational acceptance.”

2008

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Heerink, Marcel, Kröse, Ben, Wielinga, Bob and Evers, Vanessa (2008): Enjoyment intention to use and actual use of a conversational robot by elderly people. In: Proceedings of the 3rd ACM/IEEE International Conference on Human Robot Interaction 2008. pp. 113-120.

“In this paper we explore the concept of enjoyment as a possible factor influencing acceptance of robotic technology by elderly people. We describe an experiment with a conversational robot and elderly users (n=30) that incorporates both a test session and a long term user observation. The experiment did confirm the hypothesis that perceived enjoyment has an effect on the intention to use a robotic system. Furthermore, findings show that the general assumption in technology acceptance models that intention to use predicts actual use is also applicable to this specific technology used by elderly people.”

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