Maps, vibration or gaze? Comparison of novel navigation assistance in indoor and outdoor environments

Gkonos, Charalampos; Giannopoulos, Ioannis; Raubal, Martin

doi:10.1080/17489725.2017.1323125

Cited by 14 publications

(8 citation statements)

References 34 publications

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“…Novel interaction modalities and paradigms, and context-aware user interfaces, are available nowadays. In addition to traditional user interfaces through which people can interact with textbased information or cartographic maps, novel interaction modes, such as audio, gesture, gaze, or vibration (Gkonos et al 2017), and displays integrating augmented and virtual reality exist (Rudi et al 2016).…”

Section: Geosmartnessmentioning

confidence: 99%

“…When a decision point with different options is approached, the user starts to examine the possible ones to follow. At the moment when the user's gaze is aligned with the correct street, the system automatically provides feedback to convey this, for example through a vibrotactile belt or, more effectively, its combination with gaze information (Gkonos et al 2017). Systems for real-time gaze tracking in outdoor environments, which map the gazes from a mobile eye tracker to a georeferenced view using computer vision methods, allow for such personalized gaze-based decision support (Anagnostopoulos et al 2017).…”

Section: Personalized Gaze-based Decision Supportmentioning

confidence: 99%

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Geosmartness for Personalized and Sustainable Future Urban Mobility

2021

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Urban mobility and the transport of people have been increasing in volume inexorably for decades. Despite the advantages and opportunities mobility has brought to our society, there are also severe drawbacks such as the transport sector’s role as one of the main contributors to greenhouse-gas emissions and traffic jams. In the future, an increasing number of people will be living in large urban settings, and therefore, these problems must be solved to assure livable environments. The rapid progress of information and communication, and geographic information technologies, has paved the way for urban informatics and smart cities, which allow for large-scale urban analytics as well as supporting people in their complex mobile decision making. This chapter demonstrates how geosmartness, a combination of novel spatial-data sources, computational methods, and geospatial technologies, provides opportunities for scientists to perform large-scale spatio-temporal analyses of mobility patterns as well as to investigate people’s mobile decision making. Mobility-pattern analysis is necessary for evaluating real-time situations and for making predictions regarding future states. These analyses can also help detect behavioral changes, such as the impact of people’s travel habits or novel travel options, possibly leading to more sustainable forms of transport. Mobile technologies provide novel ways of user support. Examples cover movement-data analysis within the context of multi-modal and energy-efficient mobility, as well as mobile decision-making support through gaze-based interaction.

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Section: Geosmartnessmentioning

confidence: 99%

Section: Personalized Gaze-based Decision Supportmentioning

confidence: 99%

Geosmartness for Personalized and Sustainable Future Urban Mobility

2021

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“…maps and voices) to achieve better user experience in LBS. Research attention has also been drawn to compare the effectiveness of different interfaces in LBS (Huang, Schmidt, and Gartner 2012;Gkonos, Giannopoulos, and Raubal 2017). In terms of interface technologies and devices, smartphones are not the only mobile client in LBS.…”

Section: Towards Non-intrusive User Interfacesmentioning

confidence: 99%

Location based services: ongoing evolution and research agenda

Huang

Gärtner

Krisp

et al. 2018

Journal of Location Based Services

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132

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We are now living in a mobile information era, which is fundamentally changing science and society. Location Based Services (LBS), which deliver information depending on the location of the (mobile) device and user, play a key role in this mobile information era. This article first reviews the ongoing evolution and research trends of the scientific field of LBS in the past years. To motivate further LBS research and stimulate collective efforts, this article then presents a series of key research challenges that are essential to advance the development of LBS, setting a research agenda for LBS to 'positively' shape the future of our mobile information society. These research challenges cover issues related to the core of LBS development (e.g. positioning, modelling, and communication), evaluation, and analysis of LBS-generated data, as well as social, ethical, and behavioural issues that rise as LBS enter into people's daily lives.

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“…On the other hand, the spatial arrangement of indoor spaces differs from that of outdoor spaces. For example, multiple kinds of object can be regarded as landmarks in outdoor environments [25], such as churches, shopping malls, and bridges. However, these objects cannot be regarded as landmarks in indoor environments [26].…”

Section: Indoor Landmark Salience Modelsmentioning

confidence: 99%

Research on Generating an Indoor Landmark Salience Model for Self-Location and Spatial Orientation from Eye-Tracking Data

Wang

Chen

Zheng

et al. 2020

IJGI

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Landmarks play an essential role in wayfinding and are closely related to cognitive processes. Eye-tracking data contain massive amounts of information that can be applied to discover the cognitive behaviors during wayfinding; however, little attention has been paid to applying such data to calculating landmark salience models. This study proposes a method for constructing an indoor landmark salience model based on eye-tracking data. First, eye-tracking data are taken to calculate landmark salience for self-location and spatial orientation tasks through partial least squares regression (PLSR). Then, indoor landmark salience attractiveness (visual, semantic and structural) is selected and trained by landmark salience based on the eye-tracking data. Lastly, the indoor landmark salience model is generated by landmark salience attractiveness. Recruiting 32 participants, we designed a laboratory eye-tracking experiment to construct and test the model. Finding 1 proves that our eye-tracking data-based modelling method is more accurate than current weighting methods. Finding 2 shows that significant differences in landmark salience occur between two tasks; thus, it is necessary to generate a landmark salience model for different tasks. Our results can contribute to providing indoor maps for different tasks.

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Maps, vibration or gaze? Comparison of novel navigation assistance in indoor and outdoor environments

Cited by 14 publications

References 34 publications

Geosmartness for Personalized and Sustainable Future Urban Mobility

Geosmartness for Personalized and Sustainable Future Urban Mobility

Location based services: ongoing evolution and research agenda

Research on Generating an Indoor Landmark Salience Model for Self-Location and Spatial Orientation from Eye-Tracking Data

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