Towards affective touch interaction: predicting mobile user emotion from finger strokes

Shah, Sachin A; Teja, J. Narasimha; Bhattacharya, Samit

doi:10.1186/s40166-015-0013-z

Cited by 20 publications

(12 citation statements)

References 35 publications

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“…Currently smartphones cannot directly infer users mood, however, it may be possible to infer this from how they are interacting with their smartphones (e.g. finger stroke [68], motion gestures [11]) or from other sensors [57]. This presents an interesting avenue for further research.…”

Section: Discussionmentioning

confidence: 99%

How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Smartphones frequently notify users about newly available messages or other notifications. It can be very disruptive when these notifications interrupt users while they are busy. Our work here is based on the observation that people usually exhibit different levels of busyness at different contexts. This means that classifying users' interruptibility as a binary status, interruptible or not interruptible, is not sufficient to accurately measure their availability towards smartphone interruptions. In this paper, we propose, implement and evaluate a two-stage hierarchical model to predict people's interruptibility intensity. Our work is the first to introduce personality traits into interruptibility prediction model, and we found that personality data improves the prediction significantly. Our model bootstraps the prediction with similar people's data, and provides a good initial prediction for users whose individual models have not been trained on their own data yet. Overall prediction accuracy of our model can reach 66.1%.We present the following four major contributions: 1) We propose a two-stage hierarchical interruptibility prediction model. In the first stage, our model predicts (with 75% accuracy) whether a user will react to an interruption

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Section: Discussionmentioning

confidence: 99%

How Busy Are You?

Yuan

Gao

Lindqvist

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…By means of an SVM algorithm they obtained an accuracy of 69%. In [153], inger strokes were studied. These strokes were assumed to be indirect indications to the user's emotional state.…”

Section: Motion Sensorsmentioning

confidence: 99%

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

et al. 2022

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The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions are presented for further advancements in the field.

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“…Thus, accelerometers are used to detect whether a user is mobile or not before the stress level inference is performed. Besides stress, affects [101] and depression [79] can also be inferred unobtrusively thanks to mobile sensing. -Specific considerations in evaluation study design.…”

Section: Towards Longitudinal Studies Of the Effects Of Attention Manmentioning

confidence: 99%

A Survey of Attention Management Systems in Ubiquitous Computing Environments

Anderson

Hübener

Seipp

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.:2 • C. Anderson et al. Key takeaway points⋆ Attention is captured and steered by external and internal stimuli, while stimulus properties, such as its duration, location, intensity, etc., impact the user's reaction to the stimulus. Multimodal alert types, e.g., sound, light, vibration, and multiple device environments call for a coordinated judicious use of alerting in ubiquitous computing. ⋆ Limited cognitive capacities and threaded task processing imply that, in order to minimize disruptions, interruptions need to arrive at task boundaries or during routine tasks, should allow for task state rehearsal, and support context retrieval through hints presented to the user after an interruption. ⋆ Sensor data from ubiquitous computing devices reveals a user's location, physical activity, collocation with other people, and other information of a user's context that can be related to interruptibility. Next generation wearable devices and personalized machine learning models promise to bring us closer to direct inference of a user's cognitive processes. ATTENTION AND INTERRUPTION: DEFINITIONS AND STRATEGIESIn this section, we review definitions of the terms attention and interruption. We examine the connection between attention shifting and interruptions and discuss how interruptions can be handled through attention management systems in ubiquitous environments. What is Attention?There is no common understanding of attention in the literature. Attention is often considered as selective processing of incoming sensory information [33], with limited capacity [23] and reactive and deliberate processes [92]. Attention is also referred to as the ability to ignore irrelevant information [24]. The process of selecting stimuli can be voluntary or be steered by external events. The former ty...

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Towards affective touch interaction: predicting mobile user emotion from finger strokes

Cited by 20 publications

References 35 publications

How Busy Are You?

How Busy Are You?

A Survey of Privacy Vulnerabilities of Mobile Device Sensors

A Survey of Attention Management Systems in Ubiquitous Computing Environments

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