Optimizing Virtual Reality Eye Tracking Fixation Algorithm Thresholds Based on Shopper Behavior and Age

Khatri, Jaikishan; Moghaddasi, Masoud; Llanes-Jurado, José; Spinella, Luciano; Marín‐Morales, Javier; Guixeres, Jaime; Alcañíz, Mariano

doi:10.1007/978-3-030-50729-9_9

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Classifying consumers’ personalities will help marketers produce better and more personalized VR products. Several studies have used sensors such as ET ( Bigné et al, 2016 ; Khatri et al, 2020 ), NAV ( Liang et al, 2019 ), POS and INT ( Ramsøy et al, 2017 ) to conduct an experiment in a VR environment. There might be an increase in VR shopping in the coming years due to technological advancements and reduced costs of consumer-grade VR headsets: this will result in marketers focusing on personalizing VR products for the consumer based on their behavior.…”

Section: Discussionmentioning

confidence: 99%

“…Several studies have used ET to examine, for example, the effect of smell on eye-catching behavior and memory ( Tomono et al, 2011 ), information processing and attention ( Pfeiffer et al, 2017 ), customer’s impulsivity ( Moghaddasi et al, 2021 ), perceived realism ( Carlson et al, 2011 ; Meißner et al, 2017 ) and perceived presence ( Tonkin et al, 2011 ), product or brand choice ( Siegrist et al, 2019 ), and purchase decisions ( Bigné et al, 2016 ). Khatri et al (2020) also used eye gazes to classify consumers’ ages in a virtual store.…”

Section: Literature Reviewmentioning

confidence: 99%

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Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

et al. 2022

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Virtual reality (VR) is a useful tool to study consumer behavior while they are immersed in a realistic scenario. Among several other factors, personality traits have been shown to have a substantial influence on purchasing behavior. The primary objective of this study was to classify consumers based on the Big Five personality domains using their behavior while performing different tasks in a virtual shop. The personality recognition was ascertained using behavioral measures received from VR hardware, including eye-tracking, navigation, posture and interaction. Responses from 60 participants were collected while performing free and directed search tasks in a virtual hypermarket. A set of behavioral features was processed, and the personality domains were recognized using a statistical supervised machine learning classifier algorithm via a support vector machine. The results suggest that the open-mindedness personality type can be classified using eye gaze patterns, while extraversion is related to posture and interactions. However, a combination of signals must be exhibited to detect conscientiousness and negative emotionality. The combination of all measures and tasks provides better classification accuracy for all personality domains. The study indicates that a consumer’s personality can be recognized using the behavioral sensors included in commercial VR devices during a purchase in a virtual retail store.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

et al. 2022

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“…head movement data required for head-based rendering can be easily recorded. In addition to the rotational and translational metadata, that can be accessed through the build-in sensors of the HMD and its motion-tracked controllers, eye tracking in VR is applied in many areas of cognitive research: For instance focusing on spatial orientation (König et al, 2021), medical training (Lu et al, 2020) or marketing (Khatri et al, 2020). Combining spatial and temporal eye data allows for the allocation of eye movement events such as fixations (i.e.…”

Section: Introductionmentioning

confidence: 99%

Let’s get it started: Eye tracking in VR with the Pupil Labs eye tracking add-on for the HTC Vive

Josupeit

2023

JEMR

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Combining eye tracking and virtual reality (VR) is a promising approach to tackle various applied research questions. As this approach is relatively new, routines are not established yet and the first steps can be full of potential pitfalls. The present paper gives a practice example to lower the boundaries for getting started. More specifically, I focus on an affordable add-on technology, the Pupil Labs eye tracking add-on for the HTC Vive. As add-on technology with all relevant source code available on GitHub, a high degree of freedom in preprocessing, visualizing, and analyzing eye tracking data in VR can be achieved. At the same time, some extra preparatory steps for the setup of hardware and software are necessary. Therefore, specifics of eye tracking in VR from unboxing, software integration, and procedures to analyzing the data and maintaining the hardware will be addressed. The Pupil Labs eye tracking add-on for the HTC Vive represents a highly transparent approach to existing alternatives. Characteristics of eye tracking in VR in contrast to other head-mounded and remote eye trackers applied in the physical world will be discussed. In conclusion, the paper contributes to the idea of open science in two ways: First, by making the necessary routines transparent and therefore reproducible. Second, by stressing the benefits of using open source software.

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“…As an instance, Bigné et al [16] compared subjects' gaze patterns by presenting a 360 • video in an HMD and a 3D display. This relatively new technology has been used in several studies of consumer behavior [17][18][19][20][21][22][23]; however, extensive research should still be performed to shed light on every aspect of consumer behavior in VEM.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

et al. 2021

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Virtual reality (VR) in retailing (V-commerce) has been proven to enhance the consumer experience. Thus, this technology is beneficial to study behavioral patterns by offering the opportunity to infer customers’ personality traits based on their behavior. This study aims to recognize impulsivity using behavioral patterns. For this goal, 60 subjects performed three tasks—one exploration task and two planned tasks—in a virtual market. Four noninvasive signals (eye-tracking, navigation, posture, and interactions), which are available in commercial VR devices, were recorded, and a set of features were extracted and categorized into zonal, general, kinematic, temporal, and spatial types. They were input into a support vector machine classifier to recognize the impulsivity of the subjects based on the I-8 questionnaire, achieving an accuracy of 87%. The results suggest that, while the exploration task can reveal general impulsivity, other subscales such as perseverance and sensation-seeking are more related to planned tasks. The results also show that posture and interaction are the most informative signals. Our findings validate the recognition of customer impulsivity using sensors incorporated into commercial VR devices. Such information can provide a personalized shopping experience in future virtual shops.

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Optimizing Virtual Reality Eye Tracking Fixation Algorithm Thresholds Based on Shopper Behavior and Age

Cited by 3 publications

References 6 publications

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

Recognizing Personality Traits Using Consumer Behavior Patterns in a Virtual Retail Store

Let’s get it started: Eye tracking in VR with the Pupil Labs eye tracking add-on for the HTC Vive

Recognition of Customers’ Impulsivity from Behavioral Patterns in Virtual Reality

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