Computers in the Human Interaction Loop

Waibel, Alexander; Stiefelhagen, Rainer

doi:10.1007/978-1-84882-054-8_1

Cited by 48 publications

(49 citation statements)

References 81 publications

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“…Again, most of the research in this area is aimed at providing easy access to computerized services for individuals or groups to efficiently accomplish their tasks, and support for organizational and social aspects of co-located meetings has received relatively little attention. For example, in the CHIL project [34], most of the services provided were aimed at offering better ways of connecting people (the Connector service) and supporting human memory (the Memory Jog). Recently, there has been some interest in the automatic analysis of group interaction, but this has been focused mostly on technological challenges, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…In the framework of the CHIL project (http://chil.server.de [34]), we designed a service that generates unobtrusive feedback to participants in a meeting about the social dynamics, presented in real time on the basis of captured audio-visual cues. Our goal is to make the members aware of their behaviour and in this way, influence the group's social dynamics.…”

Section: Listener Eye Gazementioning

confidence: 99%

“…Less invasive technology that is suitable for real meetings is under development, for example, speaker localization and diarization on the basis of input from microphone arrays and camera-based head pose estimation (see among others the CHIL project [34]). …”

Section: Technologymentioning

confidence: 99%

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Multimodal support for social dynamics in co-located meetings

Terken

Sturm

2010

Pers Ubiquit Comput

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In this paper, we present a system that employs perceptual technologies (i.e. technologies that perceive the context through sensors such as cameras and microphone) to provide feedback about people's behaviour in small group meetings. The system measures aspects of behaviour that are relevant to the social dynamics of the meeting, speaking time and gaze behaviour, and provides visual feedback about these aspects to the meeting participants through a peripheral display. We describe the system properties and the perceptual components. Also, we present a study aimed at evaluating the effect of such a system on meeting behaviour. Groups of participants, amounting to 82 participants in all, discussed topics of general interest. Analysis of the data of 58 participants showed that feedback influenced the behaviour of the participants in such a way that it made over-participators speak less and underparticipators speak more. Analysis of the micro-patterns of six participants indicated that feedback on gaze behaviour had little effect on the interaction dynamics. We conclude that perceptual technologies can be used to build services that may help people to improve their meeting skills and we consider some ways in which such systems may be deployed in meetings.

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Section: Related Workmentioning

confidence: 99%

Section: Listener Eye Gazementioning

confidence: 99%

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Multimodal support for social dynamics in co-located meetings

Terken

Sturm

2010

Pers Ubiquit Comput

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“…There has been much progress lately in the visual perception of humans and their interaction with other people as well as with machines [1]. Most studies in this area however, focus on sub-problems like tracking or gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

Towards High-Level Human Activity Recognition through Computer Vision and Temporal Logic

IJsselmuiden

Stiefelhagen

2010

KI 2010: Advances in Artificial Intelligence

Self Cite

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Abstract. Most approaches to the visual perception of humans do not include high-level activity recognitition. This paper presents a system that fuses and interprets the outputs of several computer vision components as well as speech recognition to obtain a high-level understanding of the perceived scene. Our laboratory for investigating new ways of human-machine interaction and teamwork support, is equipped with an assemblage of cameras, some close-talking microphones, and a videowall as main interaction device. Here, we develop state of the art real-time computer vision systems to track and identify users, and estimate their visual focus of attention and gesture activity. We also monitor the users' speech activity in real time. This paper explains our approach to highlevel activity recognition based on these perceptual components and a temporal logic engine.

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“…One important aspect of user context is environment. A cellphone that is aware of its user's environment, like the Connector system under development in the CHIL project [1], would be able to switch between notification modes automatically; for example, switching to silent mode if the user is in a restaurant or theater, or halting notification entirely if the user is attending a meeting or lecture.…”

Section: Introductionmentioning

confidence: 99%

Classifying User Environment for Mobile Applications using Linear Autoencoding of Ambient Audio

Malkin

Waibel

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

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Many mobile devices and applications can act in contextsensitive ways, but rely on explicit human action for context awareness. It would be preferable if our devices were able to attain context awareness without human intervention. One important aspect of user context is environment. We present a novel method for classifying environment types based on acoustic signals. This method makes use of linear autoencoding neural networks, and is motivated by the observation that biological coding systems seem to be heavily influenced by the statistics of their environments. We show that the autoencoder method achieved a lower error rate than a standard gaussian mixture model on a representative sample task, and that a linear combination of autoencoders and GMMs yielded better performance than either alone.

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Computers in the Human Interaction Loop

Cited by 48 publications

References 81 publications

Multimodal support for social dynamics in co-located meetings

Multimodal support for social dynamics in co-located meetings

Towards High-Level Human Activity Recognition through Computer Vision and Temporal Logic

Classifying User Environment for Mobile Applications using Linear Autoencoding of Ambient Audio

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