scholarly journals Steering a Robotic Wheelchair Based on Voice Recognition System Using Convolutional Neural Networks

Electronics ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 168
Author(s):  
Mohsen Bakouri ◽  
Mohammed Alsehaimi ◽  
Husham Farouk Ismail ◽  
Khaled Alshareef ◽  
Ali Ganoun ◽  
...  
Keyword(s):  
Performance Test ◽  
Low Cost ◽  
Voice Recognition ◽  
Recognition System ◽  
Mobile App ◽  
Raspberry Pi ◽  
Speech Corpus ◽  
Dc Motor Drives ◽  
Software And Hardware ◽  
Robotic Wheelchair

Many wheelchair people depend on others to control the movement of their wheelchairs, which significantly influences their independence and quality of life. Smart wheelchairs offer a degree of self-dependence and freedom to drive their own vehicles. In this work, we designed and implemented a low-cost software and hardware method to steer a robotic wheelchair. Moreover, from our method, we developed our own Android mobile app based on Flutter software. A convolutional neural network (CNN)-based network-in-network (NIN) structure approach integrated with a voice recognition model was also developed and configured to build the mobile app. The technique was also implemented and configured using an offline Wi-Fi network hotspot between software and hardware components. Five voice commands (yes, no, left, right, and stop) guided and controlled the wheelchair through the Raspberry Pi and DC motor drives. The overall system was evaluated based on a trained and validated English speech corpus by Arabic native speakers for isolated words to assess the performance of the Android OS application. The maneuverability performance of indoor and outdoor navigation was also evaluated in terms of accuracy. The results indicated a degree of accuracy of approximately 87.2% of the accurate prediction of some of the five voice commands. Additionally, in the real-time performance test, the root-mean-square deviation (RMSD) values between the planned and actual nodes for indoor/outdoor maneuvering were 1.721 × 10−5 and 1.743 × 10−5, respectively.

scholarly journals Extending Smart Phone Based Techniques to Provide AI Flavored Interaction with DIY Robots, over Wi-Fi and LoRa interfaces

2019 ◽  
Vol 9 (3) ◽  
pp. 224 ◽  
Author(s):  
Dimitrios Loukatos ◽  
Konstantinos G. Arvanitis
Keyword(s):  
Credit Card ◽  
Learning Experience ◽  
Low Cost ◽  
Voice Recognition ◽  
Smart Phone ◽  
Learning Activity ◽  
Raspberry Pi ◽  
Programming Environments ◽  
Robotic Vehicle ◽  
Educational Applications

Inspired by the mobile phone market boost, several low cost credit card-sized computers have made the scene, able to support educational applications with artificial intelligence features, intended for students of various levels. This paper describes the learning experience and highlights the technologies used to improve the function of DIY robots. The paper also reports on the students’ perceptions of this experience. The students participating in this problem based learning activity, despite having a weak programming background and a confined time schedule, tried to find efficient ways to improve the DIY robotic vehicle construction and better interact with it. Scenario cases under investigation, mainly via smart phones or tablets, involved from touch button to gesture and voice recognition methods exploiting modern AI techniques. The robotic platform used generic hardware, namely arduino and raspberry pi units, and incorporated basic automatic control functionality. Several programming environments, from MIT app inventor to C and python, were used. Apart from cloud based methods to tackle the voice recognition issues, locally running software alternatives were assessed to provide better autonomy. Typically, scenarios were performed through Wi-Fi interfaces, while the whole functionality was extended by using LoRa interfaces, to improve the robot’s controlling distance. Through experimentation, students were able to apply cutting edge technologies, to construct, integrate, evaluate and improve interaction with custom robotic vehicle solutions. The whole activity involved technologies similar to the ones making the scene in the modern agriculture era that students need to be familiar with, as future professionals.

scholarly journals Implementation of Voice Recognition Via CNN and LSTM

2020 ◽  
Vol 9 (4) ◽  
pp. 1842-1844
Keyword(s):  
Feature Extraction ◽  
Low Cost ◽  
Voice Recognition ◽  
Recognition System ◽  
Computational Costs ◽  
The Voice

The voice recognition system uses CNN a lot. This is because CNN has the optimized ability to recognize and classify targets. CNN, however, has a problem that the bigger the object to be recognized, the more expensive the computational costs are. In this paper, we are going to solve these problems through MFCC feature extraction and model roll combining CNN and LSTM to present the possibility of performing voice recognition even through low-cost devices.

scholarly journals Remote Control using Voice Recognition based on Arduino

2019 ◽  
Vol 12 (3) ◽  
pp. 22-28
Author(s):  
Jinan N. Shehab
Keyword(s):  
Remote Control ◽  
Low Cost ◽  
Voice Recognition ◽  
Recognition System ◽  
Home Automation ◽  
Easy Method ◽  
C Language ◽  
Infrared Technology ◽  
Data Coordination ◽  
The Voice

Home automation becomes important, because it gives the user convenient and easy method to use home appliances. This paper aims to help people with special needs or physical disabilities and injuries by paralysis to control any device using infrared technology using voice commands based on the voice recognition system (voice recognition unit V3) system can recognize voice commands, convert them to desired data coordination and data transmission via IR transmitter and microcontroller (Arduino Uno) Receiving this signal by IR sensor to control TV receiver then get a full remote control that works by voice commands. The software consists of a Micro C language programmable microcontroller. This system is of low cost and flexible with growing variety of devices that can be controlled.

SIGN LANGUAGE TO VOICE RECOGNITION SYSTEM USING RASPBERRY Pi

2020 ◽  
Vol 8 (2) ◽  
pp. 14
Author(s):  
J. MANIKANDAN ◽  
M. THANKAM ◽  
K. P. AISHWARYA ◽  
S. RADHA ◽  
◽  
...  
Keyword(s):  
Sign Language ◽  
Voice Recognition ◽  
Recognition System ◽  
Raspberry Pi

scholarly journals A System for Controlling and Monitoring IoT Applications

2018 ◽  
Vol 1 (3) ◽  
pp. 26 ◽  
Author(s):  
Zebenzui Lima ◽  
Hugo García-Vázquez ◽  
Raúl Rodríguez ◽  
Sunil Khemchandani ◽  
Fortunato Dualibe ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Internet Of Things ◽  
Open Source Software ◽  
Remote Monitoring ◽  
Low Cost ◽  
Raspberry Pi ◽  
Design And Implementation ◽  
Iot Applications ◽  
Hardware System ◽  
Software And Hardware

In this work, the design and implementation of an open source software and hardware system for Internet of Things (IoT) applications is presented. This system permits the remote monitoring of supplied data from sensors and webcams and the control of different devices such as actuators, servomotors and LEDs. The parameters which have been monitored are brightness, temperature and relative humidity all of which constitute possible environmental factors. The control and monitoring of the installation is realised through a server which is managed by an administrator. The device which rules the installation is a Raspberry Pi, a small and powerful micro-computer in a single board with low consumption, low cost and reconfigurability.

scholarly journals IoT platform based Smart Assistant for Surveillance

2021 ◽  
Vol 20 ◽  
pp. 176-181
Author(s):  
Debalina Banerjee ◽  
Akashjyoti Banik ◽  
Sanjib Kumar Singh ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Low Cost ◽  
Recognition System ◽  
High Energy ◽  
Raspberry Pi ◽  
Audio Signals ◽  
Energy Materials ◽  
Time Data ◽  
Metal Detector ◽  
Virtual Interface ◽  
Robotic Vehicle

Surveillance operations designed to be carried out by a robotic vehicle for entry into an area of higher risks and perform hazardous tasks form the core of this work. The system is integrated with a robotic vehicle that is controlled through a virtual interface and well supported by live video streaming. Here, the motion detection sensor is used as a simple but powerful human presence detector and alarm trigger. Also, the design has a metal detector and gas detecting sensor that can provide precaution against potential landmines present in the operations area and presence of chemicals, high energy materials or poisonous gases on regular and event-based occurrence. The real-time data of the gas sensor is stored in the local machine and also uses a speech recognition system developed using Raspberry Pi microcomputer to detect audio signals. It generates routine alarms on special/unknown/ first time patterns of audio threats. The system is designed using low-cost components.

scholarly journals Indoor Low Cost Assistive Device using 2D SLAM Based on LiDAR for Visually Impaired People

2019 ◽  
Vol 15 (2) ◽  
pp. 115-121
Author(s):  
Heba Hakim ◽  
Ali Marhoon
Keyword(s):  
Visually Impaired ◽  
Speech Synthesis ◽  
Low Cost ◽  
Optimal Path ◽  
Recognition System ◽  
Ultrasonic Sensor ◽  
Assistive Device ◽  
Raspberry Pi ◽  
Planning Algorithm ◽  
Indoor Scenarios

Many assistive devices have been developed for visually impaired (VI) person in recent years which solve the problems that face VI person in his/her daily moving. Most of researches try to solve the obstacle avoidance or navigation problem, and others focus on assisting VI person to recognize the objects in his/her surrounding environment. However, a few of them integrate both navigation and recognition capabilities in their system. According to above needs, an assistive device is presented in this paper that achieves both capabilities to aid the VI person to (1) navigate safely from his/her current location (pose) to a desired destination in unknown environment, and (2) recognize his/her surrounding objects. The proposed system consists of the low cost sensors Neato XV-11 LiDAR, ultrasonic sensor, Raspberry pi camera (CameraPi), which are hold on a white cane. Hector SLAM based on 2D LiDAR is used to construct a 2D-map of unfamiliar environment. While A* path planning algorithm generates an optimal path on the given 2D hector map. Moreover, the temporary obstacles in front of VI person are detected by an ultrasonic sensor. The recognition system based on Convolution Neural Networks (CNN) technique is implemented in this work to predict object class besides enhance the navigation system. The interaction between the VI person and an assistive system is done by audio module (speech recognition and speech synthesis). The proposed system performance has been evaluated on various real-time experiments conducted in indoor scenarios, showing the efficiency of the proposed system.

scholarly journals Blind's Mate - A Navigation System for Blind People

2019 ◽  
pp. 230-235 ◽  
Author(s):  
Dimpal Mohod ◽  
Pranalee Meshram ◽  
Sagarika Bhelkar ◽  
Shalaka Padhye ◽  
Srushti Kotangale ◽  
...  
Keyword(s):  
Low Cost ◽  
Effective Means ◽  
Fast Response ◽  
Daily Basis ◽  
Mobile App ◽  
Blind People ◽  
Warning Messages ◽  
Live Feed ◽  
Software And Hardware ◽  
Visually Challenged

Visually challenged people rely on companions around them on daily basis. Basic chores of day to day life pose a challenge for people with impaired vision. Walking on streets while dodging obstacles and navigating through roads to reach desired destination independently is one such challenge. Proposed system 'Blind's Mate' is a combination of software and hardware technology that can be used to improve the walking experience of blind people. Blind’s Mate is a combination of smart stick and a mobile app that can alert people of impending obstacles and water pits while providing audio feed to the users about obstacles encountered during walking. This stick is embedded with ultrasonic sensor to detect any obstacles in front of the user. The app gives live feed to the user continuously via earphones. Speech warning messages are activated when any obstacle is detected. The proposed stick is of low cost, has fast response, low power consumption, light weight and an effective means to help blind people finding their way on streets with ease.

Development of hardware and software system for access control and management

Ergodesign ◽  
2020 ◽  
Vol 2020 (1) ◽  
pp. 19-24
Author(s):  
Igor Pestov ◽  
Polina Shinkareva ◽  
Sofia Kosheleva ◽  
Maxim Burmistrov
Keyword(s):  
Access Control ◽  
High Mobility ◽  
Raspberry Pi ◽  
Software System ◽  
Hardware System ◽  
Software And Hardware ◽  
Arduino Uno ◽  
Control And Management ◽  
Hardware Platforms ◽  
Hardware And Software System

This article aims to develop a hardware-software system for access control and management based on the hardware platforms Arduino Uno and Raspberry Pi. The developed software and hardware system is designed to collect data and store them in the database. The presented complex can be carried and used anywhere, which explains its high mobility.

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