Computer vision-based hand gesture recognition for human-robot interaction: a review

Qi, Jing; Ma, Li; Cui, Zhenchao; Yu, Yushu

doi:10.1007/s40747-023-01173-6

Cited by 38 publications

(5 citation statements)

References 189 publications

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“…This enables intuitive communication between humans and robots without the need for physical interfaces. [4] One study gives the insight on Hand Motion Controller and Conversational Assistant using OpenCV, Machine Learning, Python,has potential applications in hazardous environments for enabling hand-free device operation, improves accessibility through human-computer interaction and improves the accessibility of machine interaction for users with somatic disabilities. The algorithm in this research paper utilizes OpenCV for recognizing hand gestures and translating them into mouse movements.Implements ML and Computer Vision algorithms, including CNN, for recognizing hand gestures and voice commands.…”

Section: Literature Reviewmentioning

confidence: 99%

Review on Desktop Assistant for Visually Impaired: MIME.AI

Dolzake

2024

IJSREM

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The study begins by examining the historical evolution of desktop assistants, highlighting key milestones and breakthroughs in assistive technology. It then delves into the core functionalities that make these desktop assistants valuable tools for the visually impaired. These functionalities include speech synthesis, screen reading, voice commands, and tactile feedback mechanisms. The review addresses the challenges and limitations associated with current desktop assistant technologies for the visually impaired. Mime.ai is a model which includes key aspects typically associated with desktop assistants for the visually impaired like Text-to-Speech technology, voice commands, AI and Machine Learning Integration, Web Accessibility and Compatibility with Other Assistive Technologies. It examines factors such as learnability, efficiency, memorability, errors, and user satisfaction, providing insights into the overall usability of the assistant. The review assesses seamless integration of the assistant with screen readers, braille displays, magnification software, and productivity tools. Finally, the review considers the impact of the desktop assistant on the daily lives of visually impaired users. It presents user feedback and testimonials regarding the utility, effectiveness, and overall satisfaction with the assistant, highlighting its potential to improve accessibility and productivity for this user group. Key Words: Natural Language Processing, Neural Network, Voice Commands, Text-to-Speech technology.

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Section: Literature Reviewmentioning

confidence: 99%

Review on Desktop Assistant for Visually Impaired: MIME.AI

Dolzake

2024

IJSREM

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“…Recognizers primarily function to classify testing gestures into their respective training classes. In order for the recognizer to find the corresponding trained gesture class, it must first rank the classes according to the available testing gesture [17,66,67]. The NHMMbased gesture recognition paradigm's final parameters are derived using the neutrosophic Baum-Welch recursive approach.…”

Section: B Recognition Phasementioning

confidence: 99%

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Al-Saidi,

Ballagi,

Hassen

et al. 2024

Computers

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In recent years, Sign Language Recognition (SLR) has become an additional topic of discussion in the human–computer interface (HCI) field. The most significant difficulty confronting SLR recognition is finding algorithms that will scale effectively with a growing vocabulary size and a limited supply of training data for signer-independent applications. Due to its sensitivity to shape information, automated SLR based on hidden Markov models (HMMs) cannot characterize the confusing distributions of the observations in gesture features with sufficiently precise parameters. In order to simulate uncertainty in hypothesis spaces, many scholars provide an extension of the HMMs, utilizing higher-order fuzzy sets to generate interval-type-2 fuzzy HMMs. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic sets are used in this work to deal with indeterminacy in a practical SLR setting. Existing interval-type-2 fuzzy HMMs cannot consider uncertain information that includes indeterminacy. However, the neutrosophic hidden Markov model successfully identifies the best route between states when there is vagueness. This expansion is helpful because it brings the uncertainty and fuzziness of conventional HMM mapping under control. The neutrosophic three membership functions (truth, indeterminate, and falsity grades) provide more layers of autonomy for assessing HMM’s uncertainty. This approach could be helpful for an extensive vocabulary and hence seeks to solve the scalability issue. In addition, it may function independently of the signer, without needing data gloves or any other input devices. The experimental results demonstrate that the neutrosophic HMM is nearly as computationally difficult as the fuzzy HMM but has a similar performance and is more robust to gesture variations.

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“…Visual systems cannot detect some dynamic gestures due to occlusion. In these cases, haptic devices could be a good option [38]. In addition, visual systems cannot offer force feedback, which is a fast and primitive method of communication needed in the real world.…”

Section: Haptic Systemmentioning

confidence: 99%

Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation

Herbert,

Pérez-Granados,

Ruiz

et al. 2024

Sensors

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This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures.

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Computer vision-based hand gesture recognition for human-robot interaction: a review

Cited by 38 publications

References 189 publications

Review on Desktop Assistant for Visually Impaired: MIME.AI

Review on Desktop Assistant for Visually Impaired: MIME.AI

Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm

Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation

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