Smart Eyewear for Interaction and Activity Recognition

Ishimaru, Shoya; Kunze, Kai; Tanaka, Katsuma; Uema, Yuji; Kise, Koichi; İnami, Masahiko

doi:10.1145/2702613.2725449

Cited by 36 publications

(21 citation statements)

References 7 publications

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“…Examples of hEOG and vEOG sequences are shown in Figure 8. It has recently become possible to easily collect such EOG sequences using a wearable device like intelligent glasses [9]. In [10], EOG sequences are collected to recognise activities related to eye movements.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…This allows continuous recording of sensor data related to the daily life of a person [4,5]. Especially, in addition to traditional accelerometers, gyroscopes and GPSs, state-of-the-art sensors can capture physiological signals such as blood volume pressure, heart rate, galvanic skin conductance, respiration rate and electrooculogram (EOG) [6][7][8][9][10]. This offers a possibility to recognise not only physical behaviours of the person, but also his/her mental states (i.e., emotions) and health conditions.…”

Section: Introductionmentioning

confidence: 99%

“…The second task is the recognition of physical activities (e.g., jumping, bending, sitting down and standing up) using accelerometer data of different body parts [22]. For example, body, hand and head movements can be measured using accelerometers installed in a smartphone, wristband [6,7] and intelligent glasses [9], respectively. The last task is the recognition of eye-based activities (e.g., reading, writing and watching TV) using EOG data [10], which characterise eye movements and can be captured by state-of-the-art intelligent glasses [9].…”

Section: Introductionmentioning

confidence: 99%

“…For example, body, hand and head movements can be measured using accelerometers installed in a smartphone, wristband [6,7] and intelligent glasses [9], respectively. The last task is the recognition of eye-based activities (e.g., reading, writing and watching TV) using EOG data [10], which characterise eye movements and can be captured by state-of-the-art intelligent glasses [9]. Through these experiments, we justify the generality of the codebook approach for recognising various activities in daily life settings.…”

Section: Introductionmentioning

confidence: 99%

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On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

Shirahama

Grzegorzek

2017

Electronics

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Abstract:With the recent spread of mobile devices equipped with different sensors, it is possible to continuously recognise and monitor activities in daily life. This sensor-based human activity recognition is formulated as sequence classification to categorise sequences of sensor values into appropriate activity classes. One crucial problem is how to model features that can precisely represent characteristics of each sequence and lead to accurate recognition. It is laborious and/or difficult to hand-craft such features based on prior knowledge and manual investigation about sensor data. To overcome this, we focus on a feature learning approach that extracts useful features from a large amount of data. In particular, we adopt a simple but effective one, called codebook approach, which groups numerous subsequences collected from sequences into clusters. Each cluster centre is called a codeword and represents a statistically distinctive subsequence. Then, a sequence is encoded as a feature expressing the distribution of codewords. The extensive experiments on different recognition tasks for physical, mental and eye-based activities validate the effectiveness, generality and usability of the codebook approach.

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Section: Dataset Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

Shirahama

Grzegorzek

2017

Electronics

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“…Eye gaze information is widely used for recognizing human's activity including cognitive tasks like reading [7] [8].…”

Section: A Eye Gaze Informationmentioning

confidence: 99%

Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Rigaud¹,

Le²,

Burie³

et al. 2016

2016 12th IAPR Workshop on Document Analysis Systems (DAS)

Self Cite

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International audienceThe popularity of storing, distributing and reading comic books electronically has made the task of comics analysis an interesting research problem. Different work have been carried out aiming at understanding their layout structure and the graphic content. However the results are still far from universally applicable, largely due to the huge variety in expression styles and page arrangement, especially in manga (Japanese comics). In this paper, we propose a comic image analysis approach using eye-tracking data recorded during manga reading sessions. As humans are extremely capable of interpreting the structured drawing content, and show different reading behaviors based on the nature of the content, their eye movements follow distinguishable patterns over text or graphic regions. Therefore, eye gaze data can add rich information to the understanding of the manga content. Experimental results show that the fixations and saccades indeed form consistent patterns among readers, and can be used for manga textual and graphical analysis

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Eye Movements and Human-Computer Interaction

Majaranta

Räihä

Hyrskykari

et al. 2019

Eye Movement Research

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Gaze provides an attractive input channel for human-computer interaction because of its capability to convey the focus of interest. Gaze input allows people with severe disabilities to communicate with eyes alone. The advances in eye tracking technology and its reduced cost make it an increasingly interesting option to be added to the conventional modalities in every day applications. For example, gaze-aware games can enhance the gaming experience by providing timely effects at the right location, knowing exactly where the player is focusing at each moment. However, using gaze both as a viewing organ as well as a control method poses some challenges. In this chapter, we will give an introduction to using gaze as an input method. We will show how to use gaze as an explicit control method and how to exploit it subtly in the background as an additional information channel. We will summarize research on the application of different types of eye movements in interaction and present research-based design guidelines for coping with typical challenges. We will also discuss the role of gaze in multimodal, pervasive and mobile interfaces and contemplate with ideas for future developments.

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Smart Eyewear for Interaction and Activity Recognition

Cited by 36 publications

References 7 publications

On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

On the Generality of Codebook Approach for Sensor-Based Human Activity Recognition

Semi-automatic Text and Graphics Extraction of Manga Using Eye Tracking Information

Eye Movements and Human-Computer Interaction

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