Mouse Assistance for Motor-Disabled People Using Computer Vision

This paper proposes a novel object selection technique for a robotized kitchen which provides autonomy to elderly and disabled people while working in kitchen. The interface uses touch screen technology, managed from a central location. The design consists of a robotic arm whose movement is controlled according to the selection of items made by the user. We have used RFID technology for unique identification of the objects and ‘Reverse Code’ for tracking purpose. Based on the response of RFID interrogator and ‘Reverse code’, the robotic arm picks and places the object at the desired location. The interface does not use computer vision technology and provides flexibility with the size of kitchen room. The design also provides several liberalities to the user in context to manual placing and aims at minimizing the errors and rectifying them autonomously to its maximum extent. The arm can also be programmed to select and place various objects required for a particular task, in a predefined manner. At the end of this paper, we have discussed the effectiveness of this system in countering some common errors.

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MAKEMESEE –UMA SOLUÇÃO PARA O AUXÍLIO DE DEFICIENTES VISUAIS

Colloquium Exactarum ◽

10.5747/ce.2021.v13.n2.e359 ◽

2021 ◽

Vol 13 (2) ◽

pp. 57-66

Author(s):

Henrique Ramos Ricci ◽

Francisco Assis da Silva ◽

Mário Augusto Pazoti

Keyword(s):

Artificial Intelligence ◽

Computer Vision ◽

Visual Impairment ◽

Financial Market ◽

New Technologies ◽

Medical Images ◽

Disabled People ◽

Autonomous Cars ◽

Computer Power

Solutions involving artificial intelligence has become increasingly common in the last years because the increase of computer power and emergence of new technologies. These works include many humansneeds, like autonomous cars, segmentation of medical images or financial market predictions. Since accessibility is a very important area and the techniques like artificial intelligence and computer vision can make solutions to help disabled people, this paper is showing an aid that can detect, calculate and narrate obstacles to help visual impairment people. With a hardware compound by two webcams, responsible by to get different images from a scene, and a software that can processing the images, classifying, and detecting the obstacles, the system can informthe user what are ahead.

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Machine Learning Based Computer Vision Application for Visually Disabled People

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit2173130 ◽

2021 ◽

pp. 488-494

Author(s):

Shubhada Mone ◽

Nihar Salunke ◽

Omkar Jadhav ◽

Arjun Barge ◽

Nikhil Magar

Keyword(s):

Computer Vision ◽

Distance Estimation ◽

Depth Map ◽

Depth Estimation ◽

Indoor Navigation ◽

Disabled People ◽

Automatic Driving ◽

Object Distance ◽

Physical Tools ◽

Visually Disabled

With the easy availability of technology, smartphones are playing an important role in every person’s life. Also, with the advancements in computer vision based research, Automatic Driving cars, Object Recognition, Depth Map Prediction, Object Distance Estimation, have reached commendable levels of intelligence and accuracy. Combining the research and technological advancements, we can be hopeful in creating a computer vision based mobile-application which will help guide visually disabled people in performing their day to day tasks with easily available mobile applications. With our study, the visually disabled can perform simple tasks like outdoor/indoor navigation without encountering obstacles, also they can avoid accidental collisions with objects in their surroundings. Currently, there are very few applications which provide the same assistance to the visually impaired. Using physical tools like sticks is a very common practice when it comes to avoiding obstacles in a visually disabled person’s path. Our study will be focused on object detection and depth estimation techniques- two of the most popular and advanced fields in Intelligent Computer vision studies. We have explored more on the traditional challenges and future hopes of incorporating these techniques on embedded devices.

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