Walking: How visual exploration informs step choice

Sukumar, Shruthi; Ahmed, Alaa A.

doi:10.1016/j.cub.2021.01.100

Cited by 3 publications

(6 citation statements)

References 13 publications

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“…For instance, the complex coordination dynamics among head, eyes, torso, hands, and legs [9, 10] will be extensively studied, for the purpose of wider application and generalization of the proposed causality philosophy. For another instance, the unidirectional flow of information from eye movement to head motion, which results from stimulus-driven shifts of attention [70, 71], will be investigated for its quantification by the exploitation of transfer entropy. Particularly, in this case, the complex task with multiple task-relevant distractors and targets competing for gaze allocation [64] would be considered, and the proposed causality philosophy may be formalized and generalized for situations involving complex human environment interactions.…”

Section: Discussionmentioning

confidence: 99%

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Zhang

Peng

et al. 2022

Preprint

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Visual scanning is achieved by eye movement control for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The specific coordination of the head and eyes, with head motions temporally preceding eye movements, is an important human behavior to make a key contribution to goal-directed visual scanning and sensorimotor driving. In this paper, we put forward a proposal of philosophy that this specific coordination of the head and eyes essentially indicates a unidirectional causality from head motion to eye movement. We propose to investigate transfer entropy for defining a quantitative measure of this unidirectional head-eye causality. A normalized version of the proposed causality measure is introduced for taking a role as an assessment proxy of driving. The plain transfer entropy-based definition has shown its statistical significance as the measure of causality and, the normalized version has demonstrated its good effectiveness for the evaluation of driving performance, with the verification in virtual reality-based psychophysical studies. This paper successfully suggests that the quantitative exploitation of causality based on the specific coordination of the head and eyes offers an effective approach to behaviometrics of visual scanning and sensorimotor activity.

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Section: Discussionmentioning

confidence: 99%

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Zhang

Peng

et al. 2022

Preprint

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“…Li et al [41] used structural equation modeling to conduct a cross-sectional survey of residential behavior in cities such as New York and Baltimore, exploring the impact of the residential built environment on the relationship between neighborhood and transportation walking behavior. In addition, as uncertainty about a neighborhood's natural environment increases, studies are showing that increased visual exploration during walking reflects the degree of environmental preference [42][43][44]. Franěk and Režn ỳ [45] combined walkability research with investigations of environmental preferences and psychological responses to the natural environment to explore how people's walking speed responds to the visual features of the walking environment.…”

Section: Related Workmentioning

confidence: 99%

Understanding Urban Residents’ Walking Exercise Preferences: An Empirical Study Using Street View Images and Trajectory Data

Zhu,

Li,

Ouyang

et al. 2024

Buildings

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Walking exercise is a prevalent physical activity in urban areas, with streetscapes playing a significant role in shaping preferences. Understanding this influence is essential for creating urban environments conducive to walking exercise and improving residents’ quality of life. In this study, we utilize scenic beauty estimation and deep learning methods, leveraging street view images and walking exercise trajectories to analyze this influence from a human-centric perspective. We begin by generating sampling points along streets covered by trajectories and acquiring street view images. Subsequently, we apply a deep learning model to segment the images, yielding six visual indicators. Additionally, we use scenic beauty estimation to derive the seventh visual indicator. Finally, we match these indicators with trajectory data to implement preference analysis. The main findings are: (1) preferences for walking and running exercises differ on multiple indicators; (2) there are gender distinctions, with males preferring openness and females prioritizing enclosed spaces; (3) age plays a role, with those aged 30–40 preferring openness and those aged 40–50 preferring enclosed spaces; (4) preferences for different indicators vary over time and across different locations. These insights can inform policymakers in tailoring urban planning and design to specific population segments and promoting sustainable residential landscapes.

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“…However, several studies emphasise the importance of local, precise measurements about the sections of the trails (Leung & Marion, 1999;Ólafsdóttir & Runnström, 2013;Tomczyk & Ewertowski, 2013;Kanoulas et al, 2019;Zürcher et al, 2020;Eagleston & Marion, 2020) to obtain reliable information and make relevant decisions about hiking. Examples of more applied research are seeking solutions for personalised, even real-time, delivery of relevant information to a hiker, such as already mentioned online platform for difficulty assessment and recommendation of hiking trails (Calbimonte et al, 2020 and2021), or aiming at optimising trail or activity selection, studying walkability or ecometrics (Gauvin et al, 2005;Kanoulas et al, 2019;Frank et al, 2021;Wolek et al, 2021). Important findings from such studies include evidence of interpersonal differences among hikers and of various difficulties while hiking in different directions of a trail, e.g., uphill or downhill (Campbell et al, 2019).…”

Section: Mountain Trails and Hiking In Research: The Contextmentioning

confidence: 99%

“…One of the main problems to be solved in modern approaches to providing such information is the definition of the relevant features (or characteristics) of trail difficulty to be measured and efficiently provided to hikers. Calbimonte et al (2020;2021), building on the methodology used by French Hiking Federation (FFRandonnée, 2023), highlight three particularly important dimensions (also aspects or criteria, as named by Calbimonte et al, 2020) of such features which are also, directly, or indirectly, taken into consideration in majority of trail categorisations discussed in the section 1.3:…”

Section: Features Dimensions and Objectivity In Trail Difficulty Asse...mentioning

confidence: 99%

“…Nowadays, in addition to the printed form or signposting, categorisations of mountain trails are also conveyed in digital form, e.g., on online websites presenting mountain trails (e.g., Hribi.net, 2023;maPZS, 2023;Valrando, 2023). Among the outstanding examples that take advantage of the multiple opportunities offered by current advances in technology and geospatial information is the Syris platform (Calbimonte et al, 2020;2021) for assessing and recommending trails. The authors' rationale for developing such a complex tool is that a given hiker requires more personalised, detailed, diverse, and situational information than a simple categorisation of mountain trails can provide.…”

Section: Introductionmentioning

confidence: 99%

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Reconsidering the basics of mountain trail categorisation: case study in Slovenia

Krevs¹,

Repe²,

Mazej³

2023

EJG

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Mountain trail categorisation remains an important piece of information for hikers when selecting a trail and preparing for a hike, despite new developments in trail difficulty assessment and trail recommendation systems and tools. National or regional trail categorisations should maintain a simple, mainly descriptive form to remain a useful guide for the majority of hikers. However, they are often still based on subjective expert assessments of trail difficulty. In this paper, we combine findings from our own empirical research and from the literature to support two arguments: available or obtainable geographical information and GIS -based methods can contribute to the objectivity of mountain trail categorisation, and improved categorisation contributes to increased hiking safety. An experimental GIS-based categorisation of mountain trails in two areas in Slovenia is conducted to support the above arguments. The comparison of the experimental categorisation with the existing official categorisation and the identification of potentially dangerous trail sections support perhaps the most important of the two arguments, namely the potential to improve hiking safety. However, it also stimulates further discussion on the establishment and implementation of an international standard-isation of trail categorisation.

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Walking: How visual exploration informs step choice

Cited by 3 publications

References 13 publications

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Understanding Urban Residents’ Walking Exercise Preferences: An Empirical Study Using Street View Images and Trajectory Data

Reconsidering the basics of mountain trail categorisation: case study in Slovenia

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