scholarly journals The economical design of a hand-gesture and bluetooth controlled wheel-chair by integrating indigenous components: mobility aid for the disabled

2019 ◽  
Vol 473 ◽  
pp. 012004
Author(s):  
H A Khan ◽  
R M S U Islam ◽  
A W Attari ◽  
S I Mirza ◽  
M Ahmed
Keyword(s):  
Hand Gesture ◽  
Economical Design ◽  
The Disabled ◽  
Wheel Chair ◽  
Mobility Aid

scholarly journals Hand Gesture Vocalizer for Dumb and Deaf People

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 22-29
Author(s):  
Sanish Manandhar ◽  
Sushana Bajracharya ◽  
Sanjeev Karki ◽  
Ashish Kumar Jha
Keyword(s):  
Random Forest ◽  
Hand Movement ◽  
Training Model ◽  
Hand Gesture ◽  
Deaf People ◽  
Random Forest Algorithm ◽  
The Disabled ◽  
Flex Sensors ◽  
Is Design ◽  
Pictorial Form

The main purpose of this paper is to confer the system that converts a given sign used by disabled person into its appropriate textual, audio, and pictorial form using components such as Arduino Mega, Flex sensors, Accelerometer, which could be under standby a common person. A wearable glove controller is design with fl ex sensors attached on each finger, which allows the system to sense the finger movements, and aGy-61 accelerometer, which are uses to sense the hand movement of the disabled person. The wearable input glove controller sends the collected input signal to the system for processing. The system uses Random forest algorithm to predict the correct output to an accuracy of 85% on current training model.

scholarly journals Smart Wheel Chair to Assist Physically Challenged

2019 ◽  
Vol 8 (6S) ◽  
pp. 297-299
Keyword(s):  
Smart Phone ◽  
Forward Direction ◽  
Disabled Persons ◽  
Ir Sensors ◽  
The Disabled ◽  
Wheel Chair ◽  
Physically Challenged ◽  
The Cost ◽  
Time And Energy

Paper describes a smart, key and a motor controlled wheel chair. This is been designed for the disabled persons and aged ones. It is a “Key-controlled Wheel chair” that follows the commands given. The commands are given by a smart phone which has Bluetooth and the command is transmitted to the Line follower Arduino board. For example, when the user gives command like “Go to room number 2‟ then chair will move in forward direction then goes to room number 2 and likewise, when the user gives command like “Go to Back position‟ it moves towards back likewise “Left‟, “Right” and “Stop‟ for making the wheelchair to stop. This is designed for saving the cost, time and energy of the disabled persons. IR sensors are used and they help in detecting the obstacles in the passage.

scholarly journals Development of an Autonomous Wheelchair for The Disabled and Performance Analysis Using ANFIS Model

2020 ◽  
Author(s):  
Modestus O. Okwu ◽  
Lagouge K. Tartibu ◽  
Michael Ayomoh ◽  
Daniel Ighalo
Keyword(s):  
Fuzzy Inference ◽  
Research Work ◽  
Obstacle Detection ◽  
Educational Goals ◽  
Inference System ◽  
Research Fields ◽  
The Disabled ◽  
Wheel Chair ◽  
And Performance ◽  
Set Up

Abstract Navigation for persons with physical disability very often poses a major challenge to both the victims and their dedicated navigation assistance-provider. This age-long problem has been a major concern in diverse research fields in the literature ranging from assistive medicine to applied intelligence amongst others. This research work is a build-up to the literature, hence, has presented an automated wheelchair system designed, fabricated and enhanced with joystick capability for obstacle detection and autonomous stoppage. A microcontroller unit known as Arduino uno was built into the system architecture to synchronise the entire set-up by driving the DC motor for directional and linear motion of the wheel chair. The developed system would greatly improve the community of people who have lost some means of independent mobility thereby leading to an improvement in their self-esteem enabling them pursue their vocational and educational goals. Conclusively, the developed system was tested using Adaptive Neuro-Fuzzy Inference System (ANFIS), the sensitivity rule viewer at the first trial gave a total intelligence of 63.8%, further improvement was made and a second trial gave a rating of 75% and the final gave a value of approximately 80%. This shows that the system is efficient, effective and of excellent performance.

Coronavirus Disease of 2019 and Special Considerations for Wheel Chair Users

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Asif Hanif
Keyword(s):  
Muscular Dystrophy ◽  
Cell Phones ◽  
Care Givers ◽  
Walking Aids ◽  
The Disabled ◽  
Wheel Chair

The disabled/special persons are at higher risk of coronavirus disease of 2019 (COVID-19) and may have worst outcome. So these people must opt exceptional attentions and prevention specically from care givers. There are few special considerations for special persons/ wheel chair users and others who need assistance People with Muscular dystrophy (specifically) or with similar or higher disability must need to do extra care to boost the immunity and to reduce the risk. If you have an assistant to make you shift or handle, make sure he/she has properly sanitized his/her hands and have covered his/her face (in case he/she is going outside) before shifting/ handling you.When you sanitize/ wash your hands, also sanitize/disinfect the part(s) of wheel chair where you mostly touch (like joy stick, wheel bars, foot rest and etc) Before going in car/bike (in case of dire need) make sure to sanitize/disinfect the part of vehicle/car you are going to touch. Take best care my dear, blessed and beautiful people.Regularly sanitize your gadgets (like cell phones, laptops, walking aids, wearing glasses, etc.)

SEMG for Human Computer Interface Using Ann to Navigate Wheel Chair

2012 ◽  
pp. 180-187
Author(s):  
V. Rajesh ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Networks ◽  
Independent Component Analysis ◽  
Muscle Activity ◽  
Back Propagation ◽  
Independent Component ◽  
Experimental Results ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Propagation Analysis ◽  
Wheel Chair

This paper presents an approach to identify hand gestures with muscle activity separated from electromyogram (EMG) using Back Propagation analysis with the goal of using hand gestures for human-computer interaction. While there are a number of previous reported works where EMG has been used to identify movement, the limitation of these works is that the systems are suitable for gross actions and when there is one prime-mover muscle involved. This paper reports overcoming the difficulty by using independent component analysis to separate muscle activity from different muscles and classified using back propagation neural networks. The experimental results show that the system was accurately able to identify the hand gesture using this technique (95%). The advantage of this system is that it is easy to train one to use it and can easily be implemented in real time.

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