Continuous dynamic gesture spotting algorithm based on Dempster–Shafer Theory in the augmented reality human computer interaction

Huang, Caihong; Yao, Zhengwei; Chen, Yi-Min; Ma, Lizhuang

doi:10.1002/rcs.1931

Cited by 7 publications

(6 citation statements)

References 30 publications

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“…The design, deployment, and basic testing of the system, which is combination of smart devices, technology of AR, physical cube, and application of software, are developed to motivate social and cognitive functions of children in preschool [ 22 ]. A platform of an AR application design is presented for realizing natural human-computer interaction, which is based on gesture recognition [ 24 ]. The authors discussed the experimental design and compared the two types of system of optical tracking of augmented reality that is maker-based augmented reality and markerless augmented reality [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

“…The proposed research attempts an endeavour to find the available materials since 2009 to present about the use of augmented reality in the human body. The papers [ 10 , 11 , 13 , 22 , 24 – 31 , 35 – 38 , 41 – 52 , 56 – 58 , 61 – 71 ] are already discussed in Section 3.1 . However, some of the other research works are given here.…”

Section: Resultsmentioning

confidence: 99%

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Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Rahmat

Nazir

Khan

et al. 2020

Journal of Healthcare Engineering

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The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Rahmat

Nazir

Khan

et al. 2020

Journal of Healthcare Engineering

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“…4, the gesture can be divided into three different stages based on the change in the slope of the GPS: 'preparation', 'nucleus', and 'retraction'. Li et al stated that each stage can be distinguished by the change in the speed of the hand motion [12]. This supports the definition of the gesture stages using the GPS that is related to the speed of the finger joint angle vector.…”

Section: A Gesture Spotting Algorithmmentioning

confidence: 73%

“…One of the most common approaches to gesture spotting is to detect a moment when the hands are stationary [9]- [12]. Simao et al proposed a gesture segmentation method that involves analyzing velocity and acceleration and does not require previous training or knowledge of the gestures or the sequence in which they are made [9].…”

Section: Introductionmentioning

confidence: 99%

“…Neverova et al introduced a binary classifier to distinguish resting moments from periods of activity [11]. Li et al proposed a dynamic gesture spotting algorithm based on evidence theory after analyzing changes in the speed of hand movements during dynamic gestures [12]. A limitation of these approaches is a false-positive problem: when the hands are stationary and in a meaningless posture, these techniques may interpret this as the start or end of known dynamic gestures.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Based Real-Time Recognition of Dynamic Finger Gestures Using a Data Glove

Lee

Bae

2020

IEEE Access

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In this paper, a real-time dynamic finger gesture recognition using a soft sensor embedded data glove is presented, which measures the metacarpophalangeal (MCP) and proximal interphalangeal (PIP) joint angles of five fingers. In the gesture recognition field, a challenging problem is that of separating meaningful dynamic gestures from a continuous data stream. Unconscious hand motions or sudden tremors, which can easily lead to segmentation ambiguity, makes this problem difficult. Furthermore, the hand shapes and speeds of users differ when performing the same dynamic gesture, and even those made by one user often vary. To solve the problem of separating meaningful dynamic gestures, we propose a deep learningbased gesture spotting algorithm that detects the start/end of a gesture sequence in a continuous data stream. The gesture spotting algorithm takes window data and estimates a scalar value named gesture progress sequence (GPS). GPS is a quantity that represents gesture progress. Moreover, to solve the gesture variation problem, we propose a sequence simplification algorithm and a deep learning-based gesture recognition algorithm. The proposed three algorithms (gesture spotting algorithm, sequence simplification algorithm, and gesture recognition algorithm) are unified into the real-time gesture recognition system and the system was tested with 11 dynamic finger gestures in real-time. The proposed system took only 6 ms to estimate a GPS and no more than 12 ms to recognize the completed gesture in real-time. INDEX TERMS Artificial neural network, data glove, data compression, dynamic gesture recognition, human-computer interaction, pattern recognition, real time system, recurrent neural network

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Towards an end-to-end isolated and continuous deep gesture recognition process

Mahmoud

Belgacem

Omri

2022

Neural Comput & Applic

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Continuous dynamic gesture spotting algorithm based on Dempster–Shafer Theory in the augmented reality human computer interaction

Cited by 7 publications

References 30 publications

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Deep Learning Based Real-Time Recognition of Dynamic Finger Gestures Using a Data Glove

Towards an end-to-end isolated and continuous deep gesture recognition process

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