Interaction techniques in mobile Augmented Reality: State-of-the-art

Ahmed, Loubna; Hamdy, Salma; Hegazy, Doaa; Elarif, Taha

doi:10.1109/intelcis.2015.7397255

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…For example, in [ 37 ], authors recognized new HCI scenarios, such as smart homes and driverless cars. In [ 124 ], augmented reality (AR) and the third generation of AI technology are investigated. However, both studies lack a systematic review (a lower number of literature units indicate limited research space in these specific topics).…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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To equip computers with human communication skills and to enable natural interaction between the computer and a human, intelligent solutions are required based on artificial intelligence (AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence, and identify potential directions for future research. We conduct a systematic mapping study of the HCII body of research. Four hundred fifty-four studies published in various journals and conferences between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and others. Researchers most often apply deep-learning and instance-based AI methods and algorithms. The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition, primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN) is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture recognition solutions.

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Section: Backgrounds and Related Workmentioning

confidence: 99%

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

Šumak

Brdnik

Pušnik

2021

Sensors

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“…1 Reality-Virtuality Continuum [2] There are several interaction techniques in AR that are often discussed. Interaction techniques in the AR environment focused on how users interact with virtual objects that appear in the AR environment [3]. There are several types of AR interaction techniques, such as 3D interactions, natural interactions, tangible interactions, spatial awareness interactions and multimodal interactions.…”

Section: Introductionmentioning

confidence: 99%

A Review of Multimodal Interaction Technique in Augmented Reality Environment

Nizam¹,

Abidin²,

Hashim³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Augmented Reality (AR) has proposed several types of interaction techniques such as 3D interactions, natural interactions, tangible interactions, spatial awareness interactions and multimodal interactions. Usually, interaction technique in AR involve unimodal interaction technique that only allows user to interact with AR content by using one modality such as gesture, speech, click, etc. Meanwhile, the combination of more than one modality is called multimodal. Multimodal can contribute to human and computer interaction more efficient and will enhance better user experience. This is because, there are a lot of issues have been found when user use unimodal interaction technique in AR environment such as fat fingers. Recent research has shown that multimodal interface (MMI) has been explored in AR environment and has been applied in various domain. This paper presents an empirical study of some of the key aspects and issues in multimodal interaction augmented reality, touching on the interaction technique and system framework. We reviewed the question of what are the interaction techniques that have been used to perform a multimodal interaction in AR environment and what are the integrated components applied in multimodal interaction AR frameworks. These two questions were used to be analysed in order to find the trends in multimodal field as a main contribution of this paper. We found that gesture, speech and touch are frequently used to manipulate virtual object. Most of the integrated component in MMI AR framework discussed only on the concept of the framework components or the information centred design between the components. Finally, we conclude this paper by providing ideas for future work involving this field.

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