Towards robust ego-centric hand gesture analysis for robot control

Song, Hui; Feng, Weijiang; Guan, Naiyang; Huang, Xuhui; Luo, Zhigang

doi:10.1109/siprocess.2016.7888345

Cited by 5 publications

(8 citation statements)

References 16 publications

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“…Unlike pose estimation, only the fingertips of one or multiple fingers are detected. These key-points alone do not allow reconstructing the articulated hand pose, but can be used as input to HCI/HRI systems such as [105], [106], [107] (Section 5). If the objective is to estimate the key-points of a single finger, the most common solution is to regress the coordinates of these points (usually the tip and knuckle of the index finger) from a previously detected hand ROI.…”

Section: Fingertip Detectionmentioning

confidence: 99%

“…However, two main differences exist between these two topics: 1) Grasp analysis looks at the hand posture during hand-object manipulations, whereas hand gesture recognition is usually performed on hands free of any manipulations; 2) grasp analysis aims at recognizing only static hand postures [110], whereas hand gesture recognition can also be generalized to dynamic gestures. According to the literature, hand gestures can be static or dynamic [70]: static hand gesture recognition (Section 4.1.3) aims at recognizing gestures that do not depend on the motion of the hands, thus relying on appearance and hand posture information only [33], [70], [105], [123], [124], [125], [126]; dynamic hand gesture recognition (Section 4.1.4) is performed using temporal information (e.g., hand tracking), in order to capture the motion cues that allow generating specific gestures [124], [126], [127], [128], [93], [94].…”

Section: Hand Grasp Analysis and Gesture Recognitionmentioning

confidence: 99%

“…Ji et al [125] used a hybrid CNN-SVM approach, where the CNN was implemented as feature extractor and the SVM as gesture recognizer. In [105], the authors proposed a CNN architecture to directly classify the binary hand masks into multiple gestures.…”

Section: Static Hand Gesture Recognitionmentioning

confidence: 99%

“…In [149], the robot used the tracked hand locations of a human to learn the hand's future position and predict trajectories of the hands when a particular action has to be executed. By contrast, robot control approaches mainly relied on hand gesture recognition to give specific realtime commands to the robots [105], [125]. The hand gestures are seen as means of communication between the human and the robot and can encode specific commands such as the action to be performed by a robot arm [105] or the direction to be taken by a reconnaissance robot [125].…”

Section: Hci/ Hrimentioning

confidence: 99%

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Analysis of the Hands in Egocentric Vision: A Survey

Bandini

Zariffa

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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Egocentric vision (a.k.a. first-person vision -FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or part of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided.

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Section: Fingertip Detectionmentioning

confidence: 99%

Section: Hand Grasp Analysis and Gesture Recognitionmentioning

confidence: 99%

Section: Static Hand Gesture Recognitionmentioning

confidence: 99%

Section: Hci/ Hrimentioning

confidence: 99%

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Analysis of the Hands in Egocentric Vision: A Survey

Bandini

Zariffa

2023

IEEE Trans. Pattern Anal. Mach. Intell.

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“…The simultaneous view of both real reality and VR through the optical seethrough display preserves visual experience of the real world, even if the device malfunctions (Qian et al 2017). Mid-air gestures, gaze and vocal commands reflect a more natural user interaction with reality than is represented by manipulation of a mouse (Hasan and Yu 2017;Muser 2015;Song et al 2016). User's sense of presence in MR is enhanced by the HoloLens real-time processing of input (Hasan and Yu 2017).…”

Section: Human-computer Interaction In Mixed Realitymentioning

confidence: 99%

Exploring mixed reality based on self-efficacy and motivation of users

Essmiller

Asino

Ibukun

et al. 2020

Research in Learning Technology

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This study addresses the question of how to facilitate instruction and practice with virtual reality to mitigate the detrimental impact of cognitive load associated with use in simple procedural tasks. The study collected data from 63 college students aged 18 years and above from a university in the southern part of the USA. Each study participant completed a questionnaire that consisted of 22 questions using a seven-point Likert scale. The results show that there are no significant differences between motivation and self-efficacy as it relates to three selected activities: Roboraid, Tutorial and Freeplay. The opportunity for meaningful learning through the use of the mixed reality is enticing; there is value in exploring facilitation of these learning opportunities through redistribution of cognitive load.

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