Shortest Path Based Trained Indoor Smart Jacket Navigation System for Visually Impaired Person

In today’s life with the influence and vast usage of Internet of Things (IoT), a surveillance system become an essential needs (no longer a luxury facility) to home residents, buildings and other important premises. This project deals with web-based and mobile app surveillance system using Raspberry Pi and its supporting components i.e., Pi Camera, PIR motion sensor, Ultrasonic sensor, web-based & mobile application. This proposed solution can be implemented over the internet using any computer and mobile devices from anywhere and anytime. The use of Raspberry Pi has given the ability to operate and control the motion detectors, distance of the intruders and video cameras for remote sensing and surveillance. The cameras automatically stream live video and the raspberry pi device will send an alert via email and SMS to the facility owners’ computer or mobile devices. It is considered as a cost-effective solution, customizable and easy to implement by the home residents outside their home residents in comparison with other commercial surveillance system products such as CCTV, IP Camera, etc.

The paper proposes a self-driving car model also called autonomous, robotic or driver-less car is one that operates and navigates using its intelligence. The basic idea behind the paper is to develop a 1/10 scale RC car to portray an automated car. The model consists of the following software and hardware components such as CNN (Convolutional neural network), Monocular vision algorithm, Haar cascade classifier, Raspberry Pi Board model B+, Pi camera, Arduino, and an Ultrasonic sensor. The Pi camera and ultrasonic sensor are attached to the raspberry pi board to collect input images along with sensor data to stream these data to the server which in our case is the laptop. The (CNN) convolutional neural network running on the server will be used to enable lane detection to provide steering predictions that are left, right, forward based on the input image. The haar cascade classifier will be used to detect signals and stop sign and monocular vision algorithms to calculate distance from them. The ultrasonic sensor will be used for front collision avoidance by stopping the car at a certain distance before the obstacle ahead. The navigation commands such as right, left, forward, stop will be sent to the car through Arduino which is connected to the RC car's remote, this will make the car drive autonomously based on neural network predictions and some hard coded rules. Thus this model will enable autonomous driving by self-navigation via lane detection, stopping at detection of stop signs, red lights and moving on green signal and front collision avoidance in a cost-effective manner. Hardcoded rules, obstacle avoidance mechanisms, machine learning models, and smart object discrimination will help the system follow traffic rules and navigate the car using artificial intelligence.

Project addresses the integration of a complete Text Read-out system designed for the visually challenged. The system consists of a web cam interfaced with Raspberry Pi which accepts a page of printed text. The OCR package installed in Raspberry Pi, scans it into a digital document which is then subjected to image processing like skew correction, segmentation to classify the text that is captured. After the classification, the text is readout by a text to speech conversion unit (TTS engine) installed in Raspberry Pi. The output is fed to an audio amplifier then it is read out. The simulation is just an initiation of the image to text conversion and text to speech conversion done by the OCR software installed in Raspberry Pi. The system finds interesting applications in libraries, auditoriums, offices where instructions and notices are to be read and also in the assisted paper documents. By using ultrasonic sensor, we will measure the distance between the blind people and obstacle then the distance will be played through audio output..

This paper introduces a new approach of an electronic cane for navigation through the environment, using only vision system to help the blind people. By computing weights between already stored images and the real scene of the environment. The system gives advices for the blind person to select the right direction. This advice depends on a weighted topological map in the form of an appearance graph. Navigation on this graph involves Importance value from one node to other until the goal node is reached. Where a mono camera cane-held gives information in front of the blind person. The system will give a wide range indoor navigation and may be used to outdoor navigation. The identification of different scenes to the blind person has done with in a session. These sessions divide the image database into parts like bedroom, corridor,....etc. The proposed scheme employs SIFT features to represent scene containing many objects in the environment.

Indonesian Blind Union said that the number of blind people in Indonesia reached 3.75 million and 40% in school-age children, and this number will continue to increase each year. Blind people will need the tool to help their day to day activities. The research that has been developed still have flaws, whereas they do not provide the sound of information to the people with visual impairment about the obstacle, included no scientific method used in the research, especially about how the appliance works. This research does not only provide ‘beep’ sound when obstacles are detected, but also provides audio information through a headset to the blind people. There are three obstacles detected, they are holes, bumps, and walls, and it can help the blind people to decide whether to dodge or to step high. To support the audio output and the design processing speed of the appliance, this research uses Raspberry Pi 3 mini PC, three ultrasonic sensors that are used to detect obstruction objects upright, hole and bump, and to initialize the initial values before it detects the obstruction. Tahani fuzzy logic method used to different obstacles such as the bumps, flat surfaces, or holes so the blind people feel much safer while walking.

The project describes ultrasonic blind walking stick with the use of Arduino. According to World Health Organisation, 30 million peoples are permanently blind and 285 billion peoples with vision impairment. If u notice them, you can very well know about it they can’t walk without the help of other. One has to ask guidance to reach their destination. They have to face more struggles in their life daily life. Using this blind stick, a person can walk more confidently. This stick detects the object in front of the person and give response to the user by vibrating. So, the person can walk without any fear. This device will be best solution to overcome their difficulties. Generally, blind people use a traditional cane (known as white cane) for moving from one place to another. Activities like walking down the road, knowing and recognizing the presence of an obstacle in front of them, reading road signs etc. We represent a model of walking stick for blind people. This consists of GPS module, GPS Antenna, Arduino, ultrasonic sensor and buzzer. This stick can detect place and obstacles. The system employs a point-by-point approach to analyze the surrounding environment, providing real-time feedback to the user. Blind people consists of a large group of people in our society. Losing their eyesight has caused them inconvenience in performing daily tasks. Hence, smart cane had been developed in order to increase the life quality of a blind person. The purpose of this project is to design a smart cane with ultrasonic sensor and global system for mobile (GSM) for the blind. This embedded system mainly has mobility. For mobility system, it is equipped with ultrasonic sensor, HCSR04 and vibrating motor. Ultrasonic sensor will send the trigger pulse to detect obstacles. When an obstacle is detected, signals will be sent to vibrating motor and activate it. The vibrating motor will vibrate with different strengths according to the distance of the obstacle. The microcontroller used in this embedded system is Arduino UNO. The prototype of smart cane was built to increase the mobility of the blind people. Keywords— Signal processing, Internet of things, Ultrasonic sensor, visual impaired people

In this paper, a smart blind stick guidance model for visually disabled citizens has been presented. Ultrasonic sensor is added to the transmitter and receiver of the stick. If there is an obstacle in the path buzzer or voice module will alert the blind person. The pulse and spo2 sensor are used to monitor the pulse rate and oxygen level of the blind person. The Global Positioning System (GPS) is a radio navigation system that provides reliable positioning, navigation, and timing services to civilian users on a continuous worldwide basis. For anyone with a GPS receiver, the system will provide location and time information for an unlimited number of people in all weather, day and night, anywhere in the world. GPS based blind man device with user input interfacing (remote controlled based) intellectually finds the current location and gives the alert to the blind man if it was his destination area. Node MCU is the heart of the device. It stores the data of the current location which it receives from the GPS system, so that it can make use if the data stored to compare with the destination location of the user. By this it can trace out the distance from the destination and produce an alarm to alert the user in advance. Here instead of an alarm sound the blind man can directly hear the location recorded by the user itself.

To meet the growing demand of irrigation in India due to uncertain climatic conditions it is necessary to focus on sustainable irrigation approaches and improving the efficiency of the existing irrigation systems. The main purpose of this paper is to analyze the soil moisture level and to afford with auto irrigation to the crops. This system also senses Humidity, temperature and to detect if there are any obstacles in the concerned area and to reduce the human intervention (farmers) for complete automation of the system. The proposed system consist f Raspberry Pi, various sensors, Pi camera and motor driver. In smart supervising system the pi camera captures the video and transfers it to cloud through Raspberry Pi. Here Pi camera is used to provide live video streaming. The soil moisture sensor detects the moisture level and irrigates the various crops in the controlled manner. If any variation in moisture level is sensed then the sensor will update the observed value to the microcontroller and store in the cloud. Depending upon the observed value the crops are automatically provided with water to the preferred level which maintains the humidity of the soil. The moisture level of the industrial area is detected using the Temperature and Humidity sensor and transfer it to cloud through Raspberry Pi. The PIR sensor senses the entry of obstacles in the restricted area and updates it to cloud. The user can obtain these data's from cloud via mobile. Based on these data's the user can control the operation of motor by passing the command YES/NO through motor driver. Here Raspberry pi interfaces with sensors, camera, motor driver and controls the entire system.

In this chapter, a low-cost, efficient, and real-time wearable text-to-speech scanner has been proposed that can enable blind persons to hear the contents of a text material. The device captures the images of the text and converts them to speech. The hardware of the device has been realized using Raspberry Pi 3, Pi camera, and an earphone. Optical character recognition (OCR) and text-to-speech synthesis (TTS) have been implemented using Raspberry Pi 3 to accomplish the working of the device. OCR technology converted the captured text images to editable text, whereas the TTS technology scanned the alphanumeric characters in the processed image and converted them to speech. The proposed technology imitates the ability of the human sensory organs and the nervous system, where the camera mimics human eye and the image processing in Raspberry Pi 3 substitutes the human brain. This proposed device can also help people suffering from diseases like dyslexia and nyctalopia, and inability to see in dim light or at night.

In this project, we develop and apply a Smart Water Dispenser system, which is combined with object recognition and fluid level control supported by Ultrasonic Sensors, Raspberry Pi, and also DC Motors. The essence of this system is to develop a system using the Raspberry Pi 4 Model B with other components that have been integrated and interrelated Hardware and programming using OpenCV, YOLO V8, and other components, the point is that the cup can be detected, and water filling is done precisely and automatically. The process carried out is the detection of cups automatically using Raspberry Pi which is in charge of controlling the DC Motor and also the Ultrasonic sensor (HC-SR04) and detecting based on the volume of water with precision. The dispenser functions to pump water based on the condition of the volume of water in the glass and stop pumping if the volume of the glass has been fulfilled, aka not spilling with a percentage of 90%. In the scenario process, the cup search process is first carried out by scanning three times until a cup is found, if a cup is found, then the sensor component and the valve for the release of water in the hose will stop right at the position of the cup and the water will fill the cup automatically. Otherwise, the system will move backward and the system will be turned off. The first testing process has been successful and shows the effectiveness of the system in the process of finding cups and managing water levels. This innovation shows hope for improving user comfort, especially for disabilities, utilizing advanced technology in object recognition, and of course, saving water usage. In the testing process obtained 95% to 97% accuracy in object detection with different types of cups.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

Visually impaired individuals usually depend on assistive devices like white canes, frequently equipped with ultrasonic sensors, for navigation. However, these devices face significant limitations, particularly in detecting specific hazards such as potholes and deep trenches on walkways. This gap in functionality increases the risk of accidents and impedes safe, independent navigation for the visually impaired. The research developed a prototype of a blind assistive system equipped with an array of ultrasonic sensors and a Raspberry Pi integrated with Firebase for loT capabilities. AI models, trained on the collected datasets of road images and ultrasonic sensor readings, were deployed on the Raspberry Pi. Testing in real-world scenarios was conducted to validate the prototype's effectiveness. The results showed that the AI model successfully detected potholes with an accuracy of 93%. The prototype could detect both large and small potholes using ultrasonic sensors and a camera but faced challenges in cases where potholes were filled with water or in complex environments.

Introduction to dual sensory loss issue.

People generally find themselves lost while visiting a new indoor location because they are unaware of the building’s architecture, especially when it is a large one or a shopping mall. The Global Position System (GPS) does not work properly in the indoor environment because of the satellite signal attenuation. In this paper, to assist people in finding their path, a Li-Fi (Light-Fidelity) based Indoor Positioning System (IPS) is proposed. A framework is developed based on a Li-Fi LED lamp transmitter and a dongle receiver connected to an Android smartphone to decode the received sequence. The pathfinding graph-based algorithm is proceeded in a REST architecture by a Web service consulting the graph-path database both installed on a Raspberry pi 4. The proposed solution is a low cost and does not require any additional infrastructure. It is easy to implement in most indoor environments like hospitals, buildings, and campuses. A short survey of techniques and algorithms for indoor positioning and navigation with the help of smartphones is also presented.

Purpose This study presents several accounts of user experiences with assistive technology (AT). Although previous studies on dyslexic students reported promising results from using audiobooks, text-to-speech (TTS), and speech-to-text (STT), qualitative research is relatively sparse and short-term, and little is known about adolescents’ long-term experiences of using AT in schools. Therefore, this five-year follow-up study aimed to describe dyslexic students’ experiences of AT. Materials and methods Nine students with dyslexia were interviewed using a semi-structured framework and a descriptive pattern-based thematic analysis. The students had previously participated in an AT intervention in Sweden. Results Three main themes are reported: Contextual factors: facilitators or barriers; Emotional responses in the learning environment; Developing meaningful strategies. The results align with previous research findings that audiobooks are beneficial throughout the school years, while STT is of mixed utility. TTS was mainly used in learning to decode texts. Continued AT use is discussed thematically, concretising experiences connected to schools’ multilevel support. Conclusions This study can contribute to the development of AT academic practices enriched by users’ views. The findings reveal factors enabling or hindering students’ continued AT usage. Contextual factors in schools concern organisational elements rather than AT accessibility. Students’ emotional responses (using AT in the classroom) are influenced by dyslexia self-acceptance and AT attitudes. Students’ understanding of how and why to use AT may limit the development of meaningful strategies. Implications and suggested further research are provided to improve dyslexic students’ AT experiences and success in school.