Hand gesture control of virtual object in augmented reality

Rani, S. Siji; Dhrisya, K. J.; Ahalyadas, M.

doi:10.1109/icacci.2017.8126053

Cited by 16 publications

(2 citation statements)

References 2 publications

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“…Especially for Augmented (AR), Virtual (VR), and Mixed Reality (MR) research and its applications, gesture recognition and hand-gesture-based interfaces are becoming increasingly important. In the earlier years of AR, the interaction was mostly based on physical objects with markers attached to them [1], while more recent applications are using hand tracking for interaction [2][3][4]. Hand-tracking technology is becoming more reliable and is built into various types of Head-Mounted Displays (HMDs) allowing hand interaction out of the box.…”

Section: Introductionmentioning

confidence: 99%

AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications

2022

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Natural user interfaces based on hand gestures are becoming increasingly popular. The need for expensive hardware left a wide range of interaction possibilities that hand tracking enables largely unexplored. Recently, hand tracking has been built into inexpensive and widely available hardware, allowing more and more people access to this technology. This work provides researchers and users with a simple yet effective way to implement various one-handed gestures to enable deeper exploration of gesture-based interactions and interfaces. To this end, this work provides a framework for design, prototyping, testing, and implementation of one-handed gestures. The proposed framework was implemented with two main goals: First, it should be able to recognize any one-handed gesture. Secondly, the design and implementation of gestures should be as simple as performing the gesture and pressing a button to record it. The contribution of this paper is a simple yet unique way to record and recognize static and dynamic one-handed gestures. A static gesture can be captured with a template matching approach, while dynamic gestures use previously captured spatial information. The presented approach was evaluated in a user study with 33 participants and the implementable gestures received high accuracy and user acceptance.

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Section: Introductionmentioning

confidence: 99%

AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications

2022

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“…Development of gesture recognition technology has found widespread applications, ranging from virtual reality, gaming, automotive, wearable devices, to smartphones and medical [1][2][3][4][5]. Recently, the COVID-19 pandemic highlighted further benefits of the contactless interface as a preventive measure against the spread of the virus.…”

Section: Introductionmentioning

confidence: 99%

MM-Wave Radar-Based Recognition of Multiple Hand Gestures Using Long Short-Term Memory (LSTM) Neural Network

Grobelny

Narbudowicz

2022

Electronics

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The paper proposes a simple machine learning solution for hand-gesture classification, based on processed MM-wave radar signal. It investigates the classification up to 12 different intuitive and ergonomic gestures, which are intended to serve as a contactless user interface. The system is based on AWR1642 boost Frequency-Modulated Continuous-Wave (FMCW) radar, which allows capturing standardized data to support the scalability of the proposed solution. More than 4000 samples were collected from 4 different people, with all signatures extracted from the radar hardware available in open-access database accompanying the publication. Collected data were processed and used to train Long short-term memory (LSTM) and artificial recurrent neural network (RNN) architecture. The work studies the impact of different input parameters, the number of hidden layers, and the number of neurons in those layers. The proposed LSTM network allows for classification of different gestures, with the total accuracy ranging from 94.4% to 100% depending on use-case scenario, with a relatively small architecture of only 2 hidden layers with 32 neurons in each. The solution is also tested with additional data recorded from subjects not involved in the original training set, resulting in an accuracy drop of no more than 2.24%. This demonstrates that the proposed solution is robust and scalable, allowing quick and reliable creation of larger databases of gestures to expand the use of machine learning with radar technologies.

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Vision-Based Dynamic Hand Gesture Recognition Techniques and Applications: A Review

Franslin

2022

Lecture Notes in Electrical Engineering

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Hand gesture control of virtual object in augmented reality

Cited by 16 publications

References 2 publications

AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications

AnyGesture: Arbitrary One-Handed Gestures for Augmented, Virtual, and Mixed Reality Applications

MM-Wave Radar-Based Recognition of Multiple Hand Gestures Using Long Short-Term Memory (LSTM) Neural Network

Vision-Based Dynamic Hand Gesture Recognition Techniques and Applications: A Review

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