Voice Recognition based intelligent Wheelchair and GPS Tracking System

Aktar, Nasrin; Jaharr, Israt; Lala, Bijoya

doi:10.1109/ecace.2019.8679163

Cited by 37 publications

(22 citation statements)

References 7 publications

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“…In the work of Aktar et al [47], a voice recognition-based intelligent wheelchair and GPS tracking system was developed for physically handicapped people who are unable to drive a wheelchair by hand so that they can operate the wheelchair using voice commands, and the location of patients/handicapped people can be traced using a GPS module in the wheelchair that tracks and sends the information to a smartphone application via Firebase. Voice module V3 is used to record the patient' s voice and recognize that voice to follow the instructions of the patient.…”

Section: 8mentioning

confidence: 99%

Automatically Moving Robot Intended for the Elderly with Voice Control

Piyaneeranart

Ketcham

2021

Int. J. Onl. Eng.

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This research aims at an automatically moving robot intended for the elderly with voice control using voice recognition that gives a direction to the robot by a voice command. The development of this robot employed an algorithm that converts voice to text to control the robot mobility. The performance testing of the automatically moving robot intended for the elderly with Thai language voice commands revealed an accuracy of voice-to-text conversion for commanding the robot to move in designated directions of 100 percent, and the accuracy of voice-to-text conversion for commanding the robot to move in designated directions using English language was 80 percent, while that for both Thai and English commands was 70 percent. Errors in voice-to-text conversion occurred when commands were not clearly spoken or pronounced or when commands were issued from people located in a very noisy place.

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Section: 8mentioning

confidence: 99%

Automatically Moving Robot Intended for the Elderly with Voice Control

Piyaneeranart

Ketcham

2021

Int. J. Onl. Eng.

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“…Impaired people is increasing every year due to ageing, accidents and also diseases such as paralysis, spinal cord injuries (SCI), amputation and quadriplegia [1]. This group of people require a special wheelchair to move such as a motorised wheelchair.…”

Section: Introductionmentioning

confidence: 99%

“…However, disabled people with paralysed hand or having a problem with the hand cannot operate joystick on the motorised wheelchair to move. Therefore, a lot of researches studied to help the impaired people move the wheelchair-using voice recognition [1][2][3]. This wheelchair can be called smart or intelligent wheelchair because it can be moved using only the voice.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of CNNs and SVM for voice control wheelchair

Sharifuddin

Nordin

Ali

2020

IJ-AI

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In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time.

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“…N. Aktar et. al [6] proposed for a voice recognition-based intelligent wheelchair and GPS tracking system for physically handicapped people who are incapable of driving the wheelchair by hand. The location of the patient can be tracked using the GPS module through a mobile app.…”

Section: Introductionmentioning

confidence: 99%

ECHO: Hands-Free Computer Interaction using Speech Recognition System for the Debilitated

Matel¹,

Aquines²,

Guzman³

et al. 2019

IJRTE

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Computer nowadays is a must-have tool for most people. However, it is not a tool to be used by people with physical disabilities, especially the ones lacking an arm or two. The goal of this paper is to introduce a system that will help computer users perform tasks and make use of computer features and functions despite their physical limitations through the Speech Recognition System (SRS) in the English language. Ideally, this aims to provide users with an alternative way of interacting with the computer system and navigate through its functions using SRS in place of peripheral devices. It can be used to navigate through menus, open and manage applications, open certain websites, browse the internet, and type words, letters, numbers, and symbols using the dictation mode. For the testing phase, the following test cases were used: Functionality Testing, Stress Testing, and Compatibility. The testing phase yielded a result of 94.79% for the functional, 100% for stress, and 100% for compatibility, effectively ensuring that the software is working as intended. The evaluation results conforming to the standards of the ISO/IEC 9126-1 yielded a mean of 3.57 with a standard deviation of 0.52 interpreted as ‘Highly Acceptable', which means that the software can be used as an effective alternative to peripheral devices and can even be used to complement its usage.

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Voice Recognition based intelligent Wheelchair and GPS Tracking System

Cited by 37 publications

References 7 publications

Automatically Moving Robot Intended for the Elderly with Voice Control

Automatically Moving Robot Intended for the Elderly with Voice Control

Comparison of CNNs and SVM for voice control wheelchair

ECHO: Hands-Free Computer Interaction using Speech Recognition System for the Debilitated

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