Putze, Felix
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Item type:Publication, Platform for Studying Self-Repairing Auto-Corrections in Mobile Text Entry based on Brain Activity, Gaze, and ContextAuto-correction is a standard feature of mobile text entry. While the performance of state-of-the-art auto-correct methods is usually relatively high, any errors that occur are cumbersome to repair, interrupt the flow of text entry, and challenge the user's agency over the process. In this paper, we describe a system that aims to automatically identify and repair auto-correction errors. This system comprises a multi-modal classifier for detecting auto-correction errors from brain activity, eye gaze, and context information, as well as a strategy to repair such errors by replacing the erroneous correction or suggesting alternatives. We integrated both parts in a generic Android component and thus present a research platform for studying self-repairing end-to-end systems. To demonstrate its feasibility, we performed a user study to evaluate the classification performance and usability of our approach.journal article136 169 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Augmented Reality Interface for Smart Home Control using SSVEP-BCI and Eye GazeIn this paper, we investigate the integration of eyetracking and a Brain-Computer Interface into an Augmented Reality system to control a smart home environment. Through a head-mounted display, we present context-dependent control elements which the user selects by directing attention towards them. We show that the combination of both modalities leads to the most robust detection of selections and an interface which is accepted by its users.conference paper112 106 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Multimodal Recognition of Cognitive Workload for Multitasking in the Car(2010); ; This work describes the development and evaluation of a recognizer for different levels of cognitive workload in the car. We collected multiple biosignal streams (skin conductance, pulse, respiration, EEG) during an experiment in a driving simulator in which the drivers performed a primary driving task and several secondary tasks of varying difficulty. From this data, an SVM based workload classifier was trained and evaluated.conference paper71 84 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, From Human to Robot Everyday ActivityThe Everyday Activities Science and Engineering (EASE) Collaborative Research Consortium’s mission to enhance the performance of cognition-enabled robots establishes its foundation in the EASE Human Activities Data Analysis Pipeline. Through collection of diverse human activity information resources, enrichment with contextually relevant annotations, and subsequent multimodal analysis of the combined data sources, the pipeline described will provide a rich resource for robot planning researchers, through incorporation in the OpenEASE cloud platform.conference paper101 126