@inproceedings{bulling11_ubicomp,
title = {Recognition of Visual Memory Recall Processes Using Eye Movement Analysis},
author = {Andreas Bulling and Daniel Roggen},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/bulling11_ubicomp.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 13th International Conference on Ubiquitous Computing (UbiComp 2011)},
pages = {455-464},
abstract = {Physical activity, location, as well as a person's psychophysiological and affective state are common dimensions for developing context-aware systems in ubiquitous computing. An important yet missing contextual dimension is the cognitive context that comprises all aspects related to mental information processing, such as perception, memory, knowledge, or learning. In this work we investigate the feasibility of recognising visual memory recall. We use a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the methodology in a dual user study with a total of fourteen participants looking at familiar and unfamiliar pictures from four picture categories: abstract, landscapes, faces, and buildings. Using person-independent training, we are able to discriminate between familiar and unfamiliar abstract pictures with a top recognition rate of 84.3% (89.3% recall, 21.0% false positive rate) over all participants. We show that eye movement analysis is a promising approach to infer the cognitive context of a person and discuss the key challenges for the real-world implementation of eye-based cognition-aware systems.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{tessendorf11_daga,
title = {Kontexterkennung für Hörgeräte mittels zusätzlicher Sensormodalitäten},
author = {Bernd Tessendorf and Andreas Bulling and Daniel Roggen and Thomas Stiefmeier and Manuela Feilner and Peter Derleth and Gerhard Tröster},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 37th Annual Convention for Acoustics (DAGA 2011)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{tessendorf11_pervasive,
title = {Recognition of Hearing Needs From Body and Eye Movements to Improve Hearing Instruments},
author = {Bernd Tessendorf and Andreas Bulling and Daniel Roggen and Thomas Stiefmeier and Manuela Feilner and Peter Derleth and Gerhard Tröster},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/tessendorf11_pervasive.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 9th International Conference on Pervasive Computing},
pages = {314-331},
publisher = {Springer},
abstract = {Hearing instruments (HIs) have emerged as true pervasive computers as they continuously adapt the hearing program to the user’s context. However, current HIs are not able to distinguish different hearing needs in the same acoustic environment. In this work, we explore how information derived from body and eye movements can be used to improve the recognition of such hearing needs. We conduct an experiment to provoke an acoustic environment in which different hearing needs arise: active conversation and working while colleagues are having a conversation in a noisy office environment. We record body movements on nine body locations, eye movements using electrooculography (EOG), and sound using commercial HIs for eleven participants. Using a support vector machine (SVM) classifier and person-independent training we improve the accuracy of 77% based on sound to an accuracy of 92% using body movements. With a view to a future implementation into a HI we then perform a detailed analysis of the sensors attached to the head. We achieve the best accuracy of 86% using eye movements compared to 84% for head movements. Our work demonstrates the potential of additional sensor modalities for future HIs and motivates to investigate the wider applicability of this approach on further hearing situations and needs.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{turner11_petmei,
title = {Combining Gaze with Manual Interaction to Extend Physical Reach},
author = {Jayson Turner and Andreas Bulling and Hans Gellersen},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/turner11_petmei.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 1st International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2011)},
pages = {33-36},
publisher = {ACM Press},
abstract = {Situated public displays and interactive surfaces are becoming ubiquitous in our daily lives. Issues arise with these devices when attempting to interact over a distance or with content that is physically out of reach. In this paper we outline three techniques that combine gaze with manual hand-controlled input to move objects. We demonstrate and discuss how these techniques could be applied to two scenarios involving, (1) a multi-touch surface and (2) a public display and a mobile device.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{velloso11_ubicomp,
title = {Towards Qualitative Assessment of Weight Lifting Exercises Using Body-Worn Sensors},
author = {Eduardo Velloso and Andreas Bulling and Hans Gellersen},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/velloso11_ubicomp.pdf},
isbn = {978-1-4503-0630-0},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 13th International Conference on Ubiquitous Computing (UbiComp 2011)},
pages = {587-588},
publisher = {ACM Press},
abstract = {Sports exercises are beneficial for general health and fitness. Some exercises such as weight lifting are particularly error-prone and using incorrect techniques can result in serious injuries. The current work aims to develop a weight lifting assistant that relies on motion sensors mounted on the body and integrated into gym equipment that provides qualitative feedback on the user\'s performance. We believe that by comparing motion data recorded from different parts of the body with a mathematical model of the correct technique, we will be able to qualitatively assess the user\'s performance, and provide a score and suggestions for improvement.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{velloso11_wsr,
title = {The Web of Things as an Infrastructure for Improving Users\' Health and Wellbeing},
author = {Eduardo Velloso and Débora Cardador and Katia Vega and Wallace Ugulino and Andreas Bulling and Hans Gellersen and Hugo Fuks},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/velloso11_wsr.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 2nd Workshop of the Brazilian Institute for Web Science Research},
abstract = {This position paper outlines the authors’ vision on how the Web of Things, using interconnected devices, including sensor nodes, mobile phones and conventional computers can help improve the overall health and wellbeing of its users. We describe ongoing work being carried by our research group both at PUC-Rio and at Lancaster University as well as the motivating background.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{vidal11_petmei,
title = {Analysing EOG Signal Features for the Discrimination of Eye Movements with Wearable Devices},
author = {Mélodie Vidal and Andreas Bulling and Hans Gellersen},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/vidal11_petmei.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 1st International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2011)},
pages = {15-20},
publisher = {ACM Press},
abstract = {Eye tracking research in human-computer interaction and experimental psychology traditionally focuses on stationary devices and a small number of common eye movements. The advent of pervasive eye tracking promises new applications, such as eye-based mental health monitoring or eye-based activity and context recognition. These applications might require further research on additional eye movement types such as smooth pursuits and the vestibulo-ocular reflex as these movements have not been studied as extensively as saccades, fixations and blinks. In this paper we report our first step towards an effective discrimination of these movements. In a user study we collect naturalistic eye movements from 19 people using the two most common measurement techniques (EOG and IR-based). We develop a set of basic signal features that we extract from the collected eye movement data and show that a feature-based approach has the potential to discriminate between saccades, smooth pursuits, and vestibulo-ocular reflex movements.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{zhang11_petmei,
title = {Discrimination of Gaze Directions Using Low-Level Eye Image Features},
author = {Yanxia Zhang and Andreas Bulling and Hans Gellersen},
url = {https://perceptual.mpi-inf.mpg.de/files/2013/03/zhang11_petmei.pdf},
year = {2011},
date = {2011-01-01},
booktitle = {Proc. of the 1st International Workshop on Pervasive Eye Tracking and Mobile Eye-Based Interaction (PETMEI 2011)},
pages = {9-13},
publisher = {ACM Press},
abstract = {In mobile daily life settings, video-based gaze tracking faces challenges associated with changes in lighting conditions and artefacts in the video images caused by head and body movements. These challenges call for the development of new methods that are robust to such influences. In this paper we investigate the problem of gaze estimation, more specifically how to discriminate different gaze directions from eye images. In a 17 participant user study we record eye images for 13 different gaze directions from a standard web- cam. We extract a total of 50 features from these images that encode information on color, intensity and orientations. Using mRMR feature selection and a k-nearest neighbor (kNN) classifier we show that we can estimate these gaze directions with a mean recognition performance of 86%.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}