Algorithm for decoding visual gestures for an assistive virtual keyboard

Silva, Rafael Augusto da; Veiga, Antônio Cláudio Paschoarelli

doi:10.1109/tla.2020.9398632

Cited by 2 publications

(3 citation statements)

References 41 publications

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“…The development of an assistive virtual keyboard has been carried out in various studies such as in [2], [4], [6], [19], where, in addition to creating the assistive keyboard, these works aimed to optimize their results by improving typing speed, reducing errors, and minimizing user effort.…”

Section: E High-tech Augmentative and Alternative Communication Systemsmentioning

confidence: 99%

“…Moreover, technology plays an essential role in promoting social inclusion, introducing the concept of assistive technol-ogy, whose goal is to assist in resolving functional problems encountered by people with disabilities, or to expedite and promote essential daily skills for these individuals [1]. To fulfill the interaction needs of some individuals with restrictions on movement and motor activities with communication devices, the use of new modalities of interaction such as gesture or tangible object input, voice commands, and various other types of sensors is becoming increasingly common [2].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, there is a large number and variety of assistive interfaces, and recent studies related to Augmentative and Alternative Communication (AAC) seek to optimize existing devices, looking to reduce visual fatigue, increase typing speed and find more efficient ways to input information through eye movements or gaze direction [2].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Alternative Communication System for People with Severe Neuromotor Disorders using Artificial Neural Networks

Floriano,

Brito,

Rocha

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Communication difficulties are frequent for many people with severe motor disabilities, making it difficult for them to interact with their families, caregivers and society in general. Augmentative and Alternative Communication (AAC) then aims to compensate for the communication deficit of these people, providing the individual with a better quality of life. However, these individuals with severe neuromotor disorders who have severe movement restrictions find great challenges in the use of several current assistive technologies. In this context, the objective of this article is to present an Alternative Communication System based on Artificial Neural Networks with a user-centered approach and their needs, for use by this public. The input and processing of signals is performed by reading the facial reference points, using the MediaPipe FaceMesh library, and the development of the classifier of gestures/facial expressions is carried out by implementing a Recurrent Neural Network Model, using long memory units term (LSTM) and dense layers. Real-time experimental results indicate that the proposed system has a good performance, with an average accuracy of 91.8%, demonstrating recognized results in the recognition of registered gestures.

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Section: E High-tech Augmentative and Alternative Communication Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Alternative Communication System for People with Severe Neuromotor Disorders using Artificial Neural Networks

Floriano,

Brito,

Rocha

2024

Anais Do XVI Congresso Brasileiro De Inteligência Computacional

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Hand Gestures Recognition For Virtual Mouse and Keyboard :A Novel Human- Computer Interaction Approach

Prabu¹,

Arivazhagi²,

Deepika³

et al. 2023

IJARSCT

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Today's computer vision technology is so good that a machine can recognize its owner by using a simple picture-processing programme.. People use this vision in many parts of daily life in the current technological age, including face recognition, color detection, autonomous vehicles, etc. The One of the marvels of Human-Computer Interaction (HCI) technology is the mouse. Since a wireless mouse or Bluetooth mouse still need a battery for power and a dongle to connect it to the PC, they are not entirely device-free at this time. This study suggests an interactive computer system that converts hand motions into a virtual keyboard and mouse using computer vision. To carry out the task, we can use an internal camera or an external camera. To operate the mouse and keyboard, we can use a built-in camera or an external camera. The computer's camera will scan the image of various hand gestures made by a person, and in accordance with the motions, the mouse pointer will move and even carry out various functions using various gestures. Other motions can also be used to access the keyboard's features. It will work as a virtual mouse and keyboard in the absence of any wiring or other hardware. The project's webcam is the only piece of hardware, and Python is used for all development on the PyCharm platform. Modern machine learning and computer vision techniques are used in this research to recognize hand gestures, and they operate without the need for any additional hardware.

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Algorithm for decoding visual gestures for an assistive virtual keyboard

Cited by 2 publications

References 41 publications

Alternative Communication System for People with Severe Neuromotor Disorders using Artificial Neural Networks

Alternative Communication System for People with Severe Neuromotor Disorders using Artificial Neural Networks

Hand Gestures Recognition For Virtual Mouse and Keyboard :A Novel Human- Computer Interaction Approach

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