Systematic literature review of hand gestures used in human computer interaction interfaces

Vuletic, Tijana; Duffy, Alex H. B.; Hay, Laura; McTeague, Chris; Campbell, Gerard M.; Grealy, Madeleine

doi:10.1016/j.ijhcs.2019.03.011

Cited by 133 publications

(68 citation statements)

References 140 publications

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“…These can be seen in the forms of video processing libraries and algorithms, an array of machine learning frameworks and architectures and to an extent, relevant corpora. Technology facilitating gesture-based interfaces, relevant also to the automated translation of sign languages, can be split into two overall groupings: those relying primarily on wearables such as gloves, rings, accelerometers, etc., and those relying primarily on a camera or sensor-based tracking, where users' hand gestures are recorded at a distance [3]. In the first group, there are solutions such as the one presented by Cheng and colleagues [4], where an accelerometer is used to obtain 3D information of the hand movement with the aim of controlling an entertainment robot.…”

Section: State-of-the-art In Gesture Recognition and Automated Sign Lmentioning

confidence: 99%

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Jantunen¹,

Rousi²,

Rainò³

et al. 2021

Multilingual Facilitation

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This article discusses the prerequisites for the machine translation of sign languages. The topic is complex, including questions relating to technology, interaction design, linguistics and culture. At the moment, despite the affordances provided by the technology, automated translation between signed and spoken languages – or between sign languages – is not possible. The very need of such translation and its associated technology can also be questioned. Yet, we believe that contributing to the improvement of sign language detection, processing and even sign language translation to spoken languages in the future is a matter that should not be abandoned. However, we argue that this work should focus on all necessary aspects of sign languages and sign language user communities. Thus, a more diverse and critical perspective towards these issues is needed in order to avoid generalisations and bias that is often manifested within dominant research paradigms particularly in the fields of spoken language research and speech community.

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Section: State-of-the-art In Gesture Recognition and Automated Sign Lmentioning

confidence: 99%

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Jantunen¹,

Rousi²,

Rainò³

et al. 2021

Multilingual Facilitation

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show abstract

“…Hand gesture and/or pose tracking was required to perform at least one of the following: classify static gesture categories, estimate dynamic finger flexing angles, or classify hand movement trajectories. Articles based on computer vision methods or based on data gloves for hand gesture recognition were excluded as these have been the topic of recent reviews [8], [19], [20]. In Section II, we provide an overview of applications of wearable hand gesture recognition based on hand function.…”

Section: Introductionmentioning

confidence: 99%

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Jiang

Kang

Song

et al. 2022

IEEE Rev. Biomed. Eng.

111

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Hands are vital in a wide range of fundamental daily activities, and neurological diseases that impede hand function can significantly affect quality of life. Wearable hand gesture interfaces hold promise to restore and assist hand function and to enhance human-human and human-computer communication. The purpose of this review was to synthesize current novel sensing interfaces and algorithms for hand gesture recognition, and the scope of applications covers rehabilitation, prosthesis control, sign language recognition, and human-computer interaction. Results showed that electrical, dynamic, acoustical/vibratory, and optical sensing were the primary input modalities in gesture recognition interfaces. Two categories of algorithms were identified: 1) classification algorithms for predefined, fixed hand poses and 2) regression algorithms for continuous finger and wrist joint angles. Conventional machine learning algorithms, including linear discriminant analysis, support vector machines, random forests, and non-negative matrix factorization, have been widely used for a variety of gesture recognition applications, and deep learning algorithms have more recently been applied to further facilitate the complex relationship between sensor signals and multi-articulated hand postures. Future research should focus on increasing recognition accuracy with larger hand gesture datasets, improving reliability and robustness for daily use outside of the laboratory, and developing softer, less obtrusive interfaces.

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“…Users are almost always connected to computer interfaces in their everyday lives [1]. Accordingly, such user interactions for computerbased applications have evolved in diverse ways, for example, from visual interaction [2] and gesture interaction [3], to voice interaction [4] and motion capture [5]. ese interaction ways or methods are collectively referred to as interaction modalities [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Analyzing the Synergy between HCI and TRIZ in Product Innovation through a Systematic Review of the Literature

Chen

Kamarudin

Yan

2021

Advances in Human-Computer Interaction

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The boundary between tangible and digital products is getting more fused while rapidly evolving systems for interaction require novel processes that allow for rapidly developed designs, evaluations, and interaction strategies to facilitate efficient and unique user interactions with computer systems. Accordingly, the literature suggests combining creativity enhancement tools or methods with human-computer interaction (HCI) design. The TRIZ base of knowledge appears to be one of the viable options, as shown in the fragmental indications reported in well-acknowledged design textbooks. The goal of this paper is to present a systematic review of the literature to identify and analyze the published approaches and recommendations to support the synergy between HCI and TRIZ from the perspective of product innovation related to HCI, with the aim of providing a first comprehensive classification and discussing about observable differences and gaps. The method followed is the guidelines related to systematic literature review methods. As results, out of 444 initial results, only 17 studies reported the outcomes of the synergy between HCI and TRIZ. The 7 of these studies explored the feasibility of the combination of HCI and TRIZ. The 10 studies attempted to combine and derive approaches in these two fields, and the outcomes defined 3 different integration strategies between HCI and TRIZ. Some conclusions achieved are that the generic solutions to support the synergy between HCI and TRIZ are still rare in the literature. The extraction and combination of different tools caused the randomization of the evaluation criteria, and the performance of the proposals has not been comprehensively evaluated. However, the findings can help inform future developments and provide valuable information about the benefits and drawbacks of different approaches.

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Systematic literature review of hand gestures used in human computer interaction interfaces

Cited by 133 publications

References 140 publications

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Emerging Wearable Interfaces and Algorithms for Hand Gesture Recognition: A Survey

Analyzing the Synergy between HCI and TRIZ in Product Innovation through a Systematic Review of the Literature

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