A systematic literature review on vision based gesture recognition techniques

Al-Shamayleh, Ahmad Sami; Ahmad, Rodina; Abushariah, Mohammad A. M.; Alam, Khubaib Amjad; Jomhari, Nazean

doi:10.1007/s11042-018-5971-z

Cited by 96 publications

(53 citation statements)

References 116 publications

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“…Reviews by Hasan and Mishra (2012), Suarez and Murphy (2012), and Pisharady and Saerbeck (2015) focus on recognition approaches. Three systematic reviews have been identified: one that focuses on usability guidelines for "health serious games" (Milani et al, 2017), one that focuses on data exchange formats (Santos¹ et al, 2015), and one that focuses on vision based gesture systems and algorithms for gesture recognition (Al-Shamayleh et al, 2018). The first one is in Portuguese and reports on only 16 studies.…”

Section: Introductionmentioning

confidence: 99%

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

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

131

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Section: Introductionmentioning

confidence: 99%

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

Vuletic

Duffy

Hay

et al. 2019

International Journal of Human-Computer Studies

131

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“…The gestural design has been the focal point of much research in recent years [ 32 ]. Gestures are being analyzed in greater depth now, as a result of the increase in the number of touch devices, wearables, urban interactive kiosks, touchscreens, and voice–user interfaces gestures are being analyzed more thoroughly to identify those that prove more intuitive, and thus more suitable for natural interface design.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

Rio

Martín-Gutiérrez

2020

Sensors

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The ever-growing and widespread use of touch, face, full-body, and 3D mid-air gesture recognition sensors in domestic and industrial settings is serving to highlight whether interactive gestures are sufficiently inclusive, and whether or not they can be executed by all users. The purpose of this study was to analyze full-body gestures from the point of view of user experience using the Microsoft Kinect sensor, to identify which gestures are easy for individuals living with Down syndrome. With this information, app developers can satisfy Design for All (DfA) requirements by selecting suitable gestures from existing lists of gesture sets. A set of twenty full-body gestures were analyzed in this study; to do so, the research team developed an application to measure the success/failure rates and execution times of each gesture. The results show that the failure rate for gesture execution is greater than the success rate, and that there is no difference between male and female participants in terms of execution times or the successful execution of gestures. Through this study, we conclude that, in general, people living with Down syndrome are not able to perform certain full-body gestures correctly. This is a direct consequence of limitations resulting from characteristic physical and motor impairments. As a consequence, the Microsoft Kinect sensor cannot identify the gestures. It is important to remember this fact when developing gesture-based on Human Computer Interaction (HCI) applications that use the Kinect sensor as an input device when the apps are going to be used by people who have such disabilities.

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“…During this process, the analysis of the detection and recognition of finger motion plays a key role. Pioneering studies [30] have involved several techniques, for example, Raheja et al [31] proposed controlling robots using hand gestures captured by a live camera with an imaging processing method; meanwhile, Li et al collected sEMG signals and designed a prosthetic hand with Principal Component Analysis and Deep Learning methods [32]. However, there is little research considering hand gesture recognition with the inherent physiological mechanism of muscle synergies.…”

Section: Introductionmentioning

confidence: 99%

Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

Luo

Chen³

et al. 2019

Sensors

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Currently, surface electromyography (sEMG) features of the forearm multi-tendon muscles are widely used in gesture recognition, however, there are few investigations on the inherent physiological mechanism of muscle synergies. We aimed to study whether the muscle synergies could be used for gesture recognition. Five healthy participants executed five gestures of daily life (pinch, fist, open hand, grip, and extension) and the sEMG activity was acquired from six forearm muscles. A non-negative matrix factorization (NMF) algorithm was employed to decompose the pre-treated six-channel sEMG data to obtain the muscle synergy matrixes, in which the weights of each muscle channel determined the feature set for hand gesture classification. The results showed that the synergistic features of forearm muscles could be successfully clustered in the feature space, which enabled hand gestures to be recognized with high efficiency. By augmenting the number of participants, the mean recognition rate remained at more than 96% and reflected high robustness. We showed that muscle synergies can be well applied to gesture recognition.

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A systematic literature review on vision based gesture recognition techniques

Cited by 96 publications

References 116 publications

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

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

Evaluation of Full-Body Gestures Performed by Individuals with Down Syndrome: Proposal for Designing User Interfaces for All Based on Kinect Sensor

Synergistic Myoelectrical Activities of Forearm Muscles Improving Robust Recognition of Multi-Fingered Gestures

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