Enhancing Gesture Recognition to Assist the Visually Impaired
Gesture recognition (GR) is a rapidly growing research area with broad applications including sign language interpretation and immersive gaming technology. Visually impaired individuals often struggle with tasks such as navigation, technology use, and social interaction. Balancing personal independence and safety remains a key challenge in their daily lives.
Communication for visually and hearing impaired people can be enhanced by analyzing speech and gesture data, comparing it with recent datasets to clarify communication goals. Traditional machine learning (ML) models rely on handcrafted features but typically fail to perform efficiently in real-time scenarios.
Recently, deep learning (DL) models have gained popularity among researchers, surpassing conventional ML methods in effectiveness. This study introduces the Enhancing Gesture Recognition for the Visually Impaired using Deep Learning and an Improved Snake Optimization Algorithm (EGRVI-DLISOA), a novel system operating within an Internet of Things (IoT) environment.
EGRVI-DLISOA Approach
This advanced GR system uses deep learning combined with an improved snake optimization algorithm to interpret gestures in real time, specifically aiding visually impaired users. The initial step in the system employs the Sobel filter (SF) technique to eliminate noise from input data, ensuring clearer gesture recognition.
Gesture recognition (GR) is an emerging and wide-ranging area of research. GR is extensively applied in sign language, Immersive game technology, and other computer interfaces, among others.
The EGRVI-DLISOA approach is an advanced GR system powered by DL in an IoT environment, designed to provide real-time interpretation of gestures to assist the visually impaired.
Significance
- Improves real-time gesture recognition performance using deep learning.
- Integrates noise reduction through the Sobel filter for clearer input data.
- Designed specifically to support visually impaired individuals in daily activities.
This approach reflects significant advancement by combining IoT frameworks with deep learning and optimization algorithms to enhance assistive technologies for the visually impeded.
Author’s Summary
The presented EGRVI-DLISOA system uses deep learning and improved optimization to deliver accurate real-time gesture recognition within IoT, fostering better independence for the visually impaired.
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