Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives

Salah, Albert Ali; Lepri, Bruno; Pianesi, Fabio; Pentland, Alex

doi:10.1007/978-3-642-25446-8_1

Cited by 18 publications

(16 citation statements)

References 81 publications

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“…Mobile applications can achieve this easily, through the installation or a single application that uses the numerous sensors on the phone. Another benefit is that the application, while monitoring the elder, can take actions and provide notifications, for instance to induce behavior change [32].…”

Section: Mobile Solutionsmentioning

confidence: 99%

Behavior Analysis for Elderly

Salah

Kröse

Cook

2015

Human Behavior Understanding

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Abstract. Ubiquitous computing, new sensor technologies, and increasingly available and accessible algorithms for pattern recognition and machine learning enable automatic analysis and modeling of human behavior in many novel ways. In this introductory paper of the 6th International Workshop on Human Behavior Understanding (HBU'15), we seek to critically assess how HBU technology can be used for elderly. We describe and exemplify some of the challenges that come with the involvement of aging subjects, but we also point out to the great potential for expanding the use of ICT to create many applications to provide a better life for elderly.

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Section: Mobile Solutionsmentioning

confidence: 99%

Behavior Analysis for Elderly

Salah

Kröse

Cook

2015

Human Behavior Understanding

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“…In recent years, tourism research has focused more on tourists than on tourism resources, particularly tourist movements and behaviours. However, human behaviours are complex; they may be prompted by intentions or habits; modified by skill, affect and attitude; and affected by physical and contextual conditions [11]. Many methods either are based on certain assumptions to simulate human behaviours or are unable to consider all the factors that influence human behaviour [12].…”

Section: Introductionmentioning

confidence: 99%

Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

Han

Ren

et al. 2018

IJGI

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Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

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“…A large body of work exists in the field of computational human-behavior understanding, and the International Workshop of Human Behavior Understanding, previously organized with a focus on pattern recognition and ambient intelligence, brings together scientific and technological responses to some of the challenges in this field [57,58]. While some of the proposed methods can be readily re-used for robots, novel scientific and technological challenges arise when one considers achieving human behavior understanding in the context of human-robot interaction:…”

Section: Introductionmentioning

confidence: 99%

Human Behavior Understanding for Robotics

Salah

Ruiz‐del‐Solar²,

Meriçli

et al. 2012

Human Behavior Understanding

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Abstract. Human behavior is complex, but structured along individual and social lines. Robotic systems interacting with people in uncontrolled environments need capabilities to correctly interpret, predict and respond to human behaviors. This paper discusses the scientific, technological and application challenges that arise from the mutual interaction of robotics and computational human behavior understanding. We supply a short survey of the area to provide a contextual framework and describe the most recent research in this area.

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Human Behavior Understanding for Inducing Behavioral Change: Application Perspectives

Cited by 18 publications

References 81 publications

Behavior Analysis for Elderly

Behavior Analysis for Elderly

Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data

Human Behavior Understanding for Robotics

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