A Review of Augmented Reality-Based Human-Computer Interaction Applications of Gesture-Based Interaction

Kerdvibulvech, Chutisant

doi:10.1007/978-3-030-30033-3_18

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…Advancements in computer vision technology enable the implementation of gesture recognition in MR devices with minimal hardware costs [17]. Consequently, many MR devices, such as HoloLens and Meta Quest, predominantly utilize gesture-based interactions [13].…”

Section: Natural Interaction Methods In Mixed Realitymentioning

confidence: 99%

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Zhang,

Chen,

Gong

et al. 2024

Future Internet

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Data input within mixed reality environments poses significant interaction challenges, notably in immersive visual analytics applications. This study assesses five numerical input techniques: three benchmark methods (Touch-Slider, Keyboard, Pinch-Slider) and two innovative multimodal techniques (Bimanual Scaling, Gesture and Voice). An experimental design was employed to compare these techniques’ input efficiency, accuracy, and user experience across varying precision and distance conditions. The findings reveal that multimodal techniques surpass slider methods in input efficiency yet are comparable to keyboards; the voice method excels in reducing cognitive load but falls short in accuracy; and the scaling method marginally leads in user satisfaction but imposes a higher physical load. Furthermore, this study outlines these techniques’ pros and cons and offers design guidelines and future research directions.

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Section: Natural Interaction Methods In Mixed Realitymentioning

confidence: 99%

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Zhang,

Chen,

Gong

et al. 2024

Future Internet

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“…Wang et al [42] proposed an efficient framework: instead of treating motion boundaries and motion magnitudes in first-person videos as noise, this framework leverages these cues as motion cues, effectively integrating target and motion features. Augmented reality systems can extract information from physical hand movements by recognizing gestures [43]. In our work, we employ the SlowFast network [44] to recognize the human-object interactions in video playback recordings of the process of object placement.…”

Section: First-person Vision Methodsmentioning

confidence: 99%

Exploring the Role of Video Playback Visual Cues in Object Retrieval Tasks

Qin,

Su,

Qin

et al. 2024

Sensors

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Searching for objects is a common task in daily life and work. For augmented reality (AR) devices without spatial perception systems, the image of the object’s last appearance serves as a common search assistance. Compared to using only images as visual cues, videos capturing the process of object placement can provide procedural guidance, potentially enhancing users’ search efficiency. However, complete video playback capturing the entire object placement process as visual cues can be excessively lengthy, requiring users to invest significant viewing time. To explore whether segmented or accelerated video playback can still assist users in object retrieval tasks effectively, we conducted a user study. The results indicated that when video playback is covering the first appearance of the object’s destination to the object’s final appearance (referred to as the destination appearance, DA) and playing at normal speed, search time and cognitive load were significantly reduced. Subsequently, we designed a second user study to evaluate the performance of video playback compared to image cues in object retrieval tasks. The results showed that combining the DA playback starting point with images of the object’s last appearance further reduced search time and cognitive load.

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“…Human action recognition is widely used in many scenarios, such as human-computer interaction [1], video retrieval [2], and medical treatment security [3]. In recent years, with the development of deep learning technology, human skeleton action recognition based on joint type, frame index, and 3D position identification has been widely studied.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition

Lee

2020

Symmetry

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Traditional convolution neural networks have achieved great success in human action recognition. However, it is challenging to establish effective associations between different human bone nodes to capture detailed information. In this paper, we propose a dual attention-guided multiscale dynamic aggregate graph convolution neural network (DAG-GCN) for skeleton-based human action recognition. Our goal is to explore the best correlation and determine high-level semantic features. First, a multiscale dynamic aggregate GCN module is used to capture important semantic information and to establish dependence relationships for different bone nodes. Second, the higher level semantic feature is further refined, and the semantic relevance is emphasized through a dual attention guidance module. In addition, we exploit the relationship of joints hierarchically and the spatial temporal correlations through two modules. Experiments with the DAG-GCN method result in good performance on the NTU-60-RGB+D and NTU-120-RGB+D datasets. The accuracy is 95.76% and 90.01%, respectively, for the cross (X)-View and X-Subon the NTU60dataset.

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A Review of Augmented Reality-Based Human-Computer Interaction Applications of Gesture-Based Interaction

Cited by 8 publications

References 25 publications

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Exploring Data Input Problems in Mixed Reality Environments: Proposal and Evaluation of Natural Interaction Techniques

Exploring the Role of Video Playback Visual Cues in Object Retrieval Tasks

Dual Attention-Guided Multiscale Dynamic Aggregate Graph Convolutional Networks for Skeleton-Based Human Action Recognition

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