A multidimensional model for selecting personalised landmarks

Nuhn, Eva; Timpf, Sabine

doi:10.1080/17489725.2017.1401129

Cited by 17 publications

(14 citation statements)

References 37 publications

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“…Among these dimensions, the personal dimension plays the most important role in person-centric navigation, and it is in this dimension that the most differences in wayfinding occur. For instance, Nuhn [8] identified the personal dimension and its attributes by taking into account five dimensions: personal knowledge, personal interests, personal goals, personal background and individual traits. This author proposed a personal landmark salience model based on these dimensions.…”

Section: Differences In Landmark Salience During Wayfindingmentioning

confidence: 99%

“…For example, the original salience model sets all weights as equal [7], and this method has been adapted by different salience models [17,26]. Nuhn [8] proposed a salience model that weights results using expert knowledge based on formal research. Zhu [19] constructed an instance-based scoring system to evaluate indoor landmark salience.…”

Section: Compared With Previous Researchmentioning

confidence: 99%

“…Prior studies have considered landmark salience differences in personal dimensions, time dimensions (day and night) and environmental dimensions (indoor and outdoor), but few researchers have determined the differences in salience among various wayfinding tasks. For example, Nuhn and Timpf [8] defined the personal dimensions of landmarks, including spatial knowledge, interests, goals and backgrounds. Additionally, the salience of an object is different across people with various personal dimensions.…”

Section: Improvements For Current Studiesmentioning

confidence: 99%

“…2020, 9, 97 2 of 26 sharp contrast to their surroundings. Subsequently, researchers have gradually proposed various methods for analyzing landmark attractiveness, such as expert knowledge [8], eye-tracking [9] and electroencephalography (EGG) [10]. Among these methods, eye movements directly reflect users' visual behaviors and precisely measure landmark attractiveness.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Differences In Landmark Salience During Wayfindingmentioning

confidence: 99%

Section: Compared With Previous Researchmentioning

confidence: 99%

Section: Improvements For Current Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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“…Based on the above research, this article proposes the use of a relative semantic image to organize and manage the relative semantics of landmarks in pedestrian navigation. From the perspective of implementation, the model incorporates essential and easily quantified information, such as relative distance, relative orientation, visibility, visual, semantic, structural significance, etc., but does not include personal dimensions (Nuhn & Timpf, 2017), such as route and survey knowledge, which are not easily measured and need to be adjusted to specific individuals.…”

Section: Related Workmentioning

confidence: 99%

A data model for organizing relative semantics as images to support pedestrian navigation computations

Fang

Yang

Guan

et al. 2020

Transactions in GIS

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Pedestrian navigation systems often use the relative semantics of pedestrians and their environments to provide navigation guidance. Relative semantics include spatial and visual semantics. However, most navigation data models are based on an absolute reference frame and do not support the organization of relative semantics. To address this deficiency, we propose a pedestrian navigation data model based on relative semantic images that organizes the relative semantics of landmarks and environments directly in the image channels. Using geographic data for a university campus, we compared the data file size, data access time, and memory usage to confirm that the proposed approach outperforms the geodatabase approach in storing and accessing the relative semantic data. Two examples, self‐localization and route guidance, demonstrate the feasibility of the proposed data model. This model can support fast pedestrian navigation on mobile devices in small and medium‐sized areas.

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How to Model (Personalised) Landmarks?

Nuhn

Timpf

2020

Lecture Notes in Computer Science

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A multidimensional model for selecting personalised landmarks

Cited by 17 publications

References 37 publications

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

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

A data model for organizing relative semantics as images to support pedestrian navigation computations

How to Model (Personalised) Landmarks?

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