Over-The-Top (OTT) Media Application using Flutter

John, Linda M.; Mistry, Nehal; Patel, Ritu J.; Nagvekar, Dipti

doi:10.2139/ssrn.3867569

Cited by 1 publication

(1 citation statement)

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“…The foundation of this achievement was built upon carefully annotated hand gesture dataset obtained from Kaggle, and the image was annotated for YOLOV8 using Roboflow for precise annotations. The YOLOv8 model, chosen for its efficiency, was trained on this dataset and seamlessly integrated into the Flutter Vision framework [95], enabling real-time sign language recognition on mobile devices with a remarkable accuracy close to 90%. The resulting application, marked by an intuitive user interface, represents a user-centric design approach that prioritizes ease of use, which is essential for individuals with disabilities.…”

Section: Discussion Of the Mobile Application Which Work Synchronousl...mentioning

confidence: 99%

Revolutionizing Accessibility: Smart Wheelchair Robot and Mobile Application for Mobility, Assistance, and Home Management

Jayasekara,

Kulathunge,

Premaratne

et al. 2023

Journal of Robotics and Control

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This research aims to advance accessibility and inclusivity for individuals with disabilities. We focus on specific daily challenges facing people with disabilities in communication, mobility, and daily task management and introduce AssistEase, a groundbreaking smart wheelchair solution designed to empower people with disabilities by improving mobility, communication capabilities, and daily task management. AssistEase will contribute to the disabled community around the world by allowing them to manage daily tasks and communicate more easily while ensuring mobility. AssistEase offers control options such as handsfree voice control, traditional manual control, smartphone-based Bluetooth control, or innovative gesture control, designed to cater to different user preferences and needs. This uses technologies such as speech recognition, computer vision, and haptic [92] feedback to help users navigate safely while avoiding obstacles. It integrates technologies like Flutter, TensorFlow, YOLOV8, Global Positioning System (GPS), Bluetooth, and Apple Home Kit, along with hardware components including Arduino and Raspberry PI. Preliminary trials have shown improvements in mobility, communication, and daily tasks for users in need. It achieves 95% precision in guiding wheelchair users while maintaining about 90% accuracy for the robotic arm and 89% for health monitoring and location tracking. Also, it provides a user-friendly app with 90% control accuracy. The communication device has 92% accuracy in facilitating user communication, while hand gesture control achieves 90% accuracy. To advance AssistEase smart wheelchair technology, further research, and development are required to enhance its adaptability for specific disabilities. AssistEase reflects a commitment to creating a more inclusive and thriving society, focusing on innovation and inclusion for individuals of all abilities.

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Section: Discussion Of the Mobile Application Which Work Synchronousl...mentioning

confidence: 99%