Investigating the effectiveness of peephole interaction for smartwatches in a map navigation task

Kerber, Frederic; Krüger, Antonio; Löchtefeld, Markus

doi:10.1145/2628363.2628393

Cited by 28 publications

(7 citation statements)

References 10 publications

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“…Environmental familiarity can strongly bias the results of such experiments. For instance, when evaluating a navigation assistance system (Gartner et al, 2011, Kerber et al, 2014, Giannopoulos et al, 2015, Schirmer et al, 2015 if the users in one of the conditions are familiar with the environment (without explicitly stating it), they would have to rely less on the assistance, thus introducing a bias in the captured data that can alter the final results of the experiment.…”

Section: Related Workmentioning

confidence: 99%

Towards Urban Environment Familiarity Prediction

Gokl

Cutchan

Mazurkiewicz

et al. 2019

Adv. Cartogr. GIScience Int. Cartogr. Assoc.

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Abstract. Location Based Services (LBS) are definitely very helpful for people that interact within an unfamiliar environment, but also for those that already possess a certain level of familiarity with it. In order to avoid overwhelming familiar users with unnecessary information, the level of details offered by the LBS shall be adapted to the level of familiarity with the environment: providing more details to unfamiliar users and a lighter amount of information (that would be superfluous, if not even misleading) to the users that are more familiar with the current environment. Currently, the information exchange between the service and its users is not taking into account familiarity. Within this work, we investigate the potential of machine learning for a binary classification of environment familiarity (i.e., familiar vs unfamiliar) with the surrounding environment. For this purpose, a 3D virtual environment based on a part of Vienna, Austria was designed using datasets from the municipal government. During a navigation experiment with 22 participants we collected ground truth data in order to train four machine learning algorithms. The captured data included motion and orientation of the users as well as visual interaction with the surrounding buildings during navigation. This work demonstrates the potential of machine learning for predicting the state of familiarity as an enabling step for the implementation of LBS better tailored to the user.

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Section: Related Workmentioning

confidence: 99%

Towards Urban Environment Familiarity Prediction

Gokl

Cutchan

Mazurkiewicz

et al. 2019

Adv. Cartogr. GIScience Int. Cartogr. Assoc.

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“…Serious predictions are very difficult to engage since they depend on numerous factors, but many of them are oriented towards an emerging device that will be treated in this article: wearable technologies, specifically smartwatches (Kerber et al, 2014) (Rawassizadeh et al, 2014).…”

Section: Emerging Technologiesmentioning

confidence: 99%

The Potential of Smartwatches for Emotional Self-regulation of People with Autism Spectrum Disorder

Torrado

Montoro

Gómez

2016

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies

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Abstract:This paper focuses on the potential of smartwatchers as interventors in the process of emotional self-regulation on individuals with ASD. Parting from a model of assistance in their self-regulation tasks, we review the main advantages of smartwatches in terms of sensors and pervasive interaction potential. We argue the suitability of smartwatches for this kind of assistance, including studies that had used them for related purposes, and the relation of this idea with the affective computing area. Finally, we propose a technological approach for emotional self-regulation assistance that uses smartwatches and applies to the mentioned intervention model.

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“…This kind of navigation forces the user to spend attention on the map instead of letting her interact with the environment (e.g., to avoid obstacles or enjoy the view). In recent years, researchers have tried to overcome these problems by introducing novel interaction methods [17,26,27,37,38]. There have been approaches to support navigation through play [1], using augmented reality [31], through auditory interfaces [9], or even through music [7].…”

Section: Pedestrian Navigation Assistancementioning

confidence: 99%

GazeNav

Giannopoulos

Kiefer

Raubal

2015

Proceedings of the 17th International Conference on Human-Computer Interaction With Mobile Devices and Services

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Pedestrian navigation systems help us make a series of decisions that lead us to a destination. Most current pedestrian navigation systems communicate using map-based turn-byturn instructions. This interaction mode suffers from ambiguity, its user's ability to match the instruction with the environment, and it requires a redirection of visual attention from the environment to the screen. In this paper we present GazeNav, a novel gaze-based approach for pedestrian navigation. GazeNav communicates the route to take based on the user's gaze at a decision point. We evaluate GazeNav against the map-based turn-by-turn instructions. Based on an experiment conducted in a virtual environment with 32 participants we found a significantly improved user experience of GazeNav, compared to map-based instructions, and showed the effectiveness of GazeNav as well as evidence for better local spatial learning. We provide a complete comparison of navigation efficiency and effectiveness between the two approaches.

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Investigating the effectiveness of peephole interaction for smartwatches in a map navigation task

Cited by 28 publications

References 10 publications

Towards Urban Environment Familiarity Prediction

Towards Urban Environment Familiarity Prediction

The Potential of Smartwatches for Emotional Self-regulation of People with Autism Spectrum Disorder

GazeNav

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