User intent perception by gesture and eye tracking

Yang, Xian; He, Hu; Yueming, Wu; Tang, Chaolan; Chen, Heen; Liang, Jianbin

doi:10.1080/23311916.2016.1221570

Cited by 9 publications

(6 citation statements)

References 22 publications

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“…The combination of ET with the monitoring of hand movements is to be found in Yang et al . (2016) too. The work aims to provide a major understanding of user intent when interacting with products or representations thereof.…”

Section: Design Evaluations Using Biometric Devicesmentioning

confidence: 97%

“…To the scope, the scholars hypothesize that cognitive demanding handling interactions are represented by long periods of constant hand-gaze distance, as, in these phases, the hand and the gaze are involved in the same action. The combination of ET with the monitoring of hand movements is to be found in Yang et al (2016) too. The work aims to provide a major understanding of user intent when interacting with products or representations thereof.…”

Section: Experiments Classified As "Alternative"mentioning

confidence: 99%

“…The inclusion of concepts from affective engineering (Balters and Steinert, 2017), emotional and Kansei engineering (Hsu et al ., 2017; Zhao et al ., 2017), so as to foster emotional design (Triberti et al ., 2017) or beauty through design (Khalighy et al ., 2015). The support of traditional methods by opening up new avenues in the understanding of usability (Hill and Bohil, 2016), user-product interaction (Jenkins et al ., 2009; Balters and Steinert, 2017; Mussgnug et al ., 2017), user experience (Mussgnug et al ., 2014; Jiao et al ., 2017), and user intent (Yang et al ., 2016). These are supposed to play an increasing role in design, for instance due to the growing emphasis attributed to human-centered design.The evaluation and benchmark of new products and designs by means of unconscious feedback (Abdipour et al ., 2016), the individuation of points and determinants of attraction (Zhang et al ., 2014; Georgiev et al ., 2017), or the understanding of the psychology of customers (Wang et al ., 2011).…”

Section: Introductionmentioning

confidence: 99%

“…The support of traditional methods by opening up new avenues in the understanding of usability (Hill and Bohil, 2016), user-product interaction (Jenkins et al ., 2009; Balters and Steinert, 2017; Mussgnug et al ., 2017), user experience (Mussgnug et al ., 2014; Jiao et al ., 2017), and user intent (Yang et al ., 2016). These are supposed to play an increasing role in design, for instance due to the growing emphasis attributed to human-centered design.…”

Section: Introductionmentioning

confidence: 99%

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Review of the use of neurophysiological and biometric measures in experimental design research

Borgianni

Maccioni

2020

AIEDAM

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AbstractDesign is inherently affected by human-related factors and it is of no surprise that the fine-tuning of instruments capable of measuring aspects of human behavior has attracted interest in the design field. The recalled instruments include a variety of devices that capture and quantitatively assess people's unintentional and unconscious reactions and that are generally referred as neurophysiological or biometric. The number of experimental applications of these instruments in design was extremely limited as of 2016, when Lohmeyer and Meboldt published a first report on relevant measures and their interpretation in design. In the last few years, the number of relevant publications has increased dramatically and this determines the opportunity to carry out a comprehensive review in the field. The reviewed contributions are analyzed and classified according to, among others, instruments used, the kind of stakeholders involved and the supported design research activities. The role of biometric measures with respect to traditional research methods is emphasized too. The discussed instruments can represent supports or substitutes for traditional approaches, as well as they are capable of exploring phenomena that could not be addressed hitherto. The intensity of research concerning experiments with biometric measurements is discussed too; a particular focus of the final discussion is the individuation of obstacles that prevent them from becoming commonplace in design research.

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Section: Design Evaluations Using Biometric Devicesmentioning

confidence: 97%

Section: Experiments Classified As "Alternative"mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Review of the use of neurophysiological and biometric measures in experimental design research

Borgianni

Maccioni

2020

AIEDAM

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“…They report that their model predicts user intent approximately 1.8 s prior to human verbal request with an accuracy of 76%. Yang et al proposed a task-based method that captured hand gestures and eye movement to recognize user intent, arguing that hand and eye movement represents a vast majority of nonverbal expressions of intent (Yang et al, 2016 ). Surendran and Wagner showed that surface cues in the form of upper body gestures could be used to determine if a human was trustworthy or not in the context of a card playing game involving deception (Surendran & Wagner, 2019 ).…”

Section: Related Workmentioning

confidence: 99%

That was not what I was aiming at! Differentiating human intent and outcome in a physically dynamic throwing task

Surendran

Wagner

2022

Auton Robot

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Recognising intent in collaborative human robot tasks can improve team performance and human perception of robots. Intent can differ from the observed outcome in the presence of mistakes which are likely in physically dynamic tasks. We created a dataset of 1227 throws of a ball at a target from 10 participants and observed that 47% of throws were mistakes with 16% completely missing the target. Our research leverages facial images capturing the person’s reaction to the outcome of a throw to predict when the resulting throw is a mistake and then we determine the actual intent of the throw. The approach we propose for outcome prediction performs 38% better than the two-stream architecture used previously for this task on front-on videos. In addition, we propose a 1D-CNN model which is used in conjunction with priors learned from the frequency of mistakes to provide an end-to-end pipeline for outcome and intent recognition in this throwing task.

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