Smart Assisting for Visually Impaired People

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.

Visually impaired people face a lot of challenges in their day by day life. Due to blindness most of the time they depend on others for their daily movements. Many assistive technologies have been developed for blind people; most of them are expensive and designed in a complicated way. So in this paper, we represent a complete wearable navigation system for blind people based on the low expanse and truly subtle sensors, for example, Pi camera and Ultrasonic sensor. Live video analysis has been done to detect human faces and ultrasonic sensors are used to detect objects as obstacles. Raspberry Pi has been used as the main controller board. The indoor path has been pre-trained and saved in a database for blind assistance by voice command using Google Text To Speech (gTTS) API so that blind people can navigate independently. In an emergency, the blind person can seek help from the specific person by sending SOS short message service (SMS) through pressing an integrated button. This system has been tested continuously by both blindfolded and visually impaired people at various indoor locations. The outcome shows that it operates more efficiently than other assistive systems.

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 paper presents the development of a real time American Sign Language (ASL) detection system using Raspberry Pi and a Sequential Convolutional Neural Network (CNN) model. The system aims to bridge the communication gap between the Deaf and Hard of Hearing (DHH) community and the hearing population by translating ASL gestures into text and audio outputs. The proposed system leverages the Raspberry Pi 5, a cost effective and scalable hardware platform, combined with deep learning techniques to achieve high accuracy in gesture recognition. The system utilizes the custom dataset for training and employs hand landmark detection using MediaPipe for real-time gesture analysis. The results demonstrate an 85% accuracy in recognizing ASL gestures, with real-time text and audio outputs. The system is designed for personal, educational, and public applications, offering a practical solution for enhancing communication accessibility for the DHH community. Key Words: American Sign Language(ASL), Raspberry Pi, Machine Learning, Gesture recognition, Sign Language Translation, Text-to-Speech conversion

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

The global expansion of visual impairment is a very sensitive subject. Living in constant uncertainty has always had a negative impact on many aspects of their lives. The digital revolution is accelerating in the fast-paced culture, and it has had a good impact on blind people's life as well. This study intends to develop a support system for those who are blind or visually impaired. People with visual impairments from the partially unsighted to the completely blind can use the image recognition and text navigation system to support and direct them via auditory commands. The tool includes the sequential operations of indoor obstacle detection and recognition. The presence of an object is identified using an ultrasonic sensor. Here, a single chip known as a Raspberry Pi is being integrated with both the text navigation device and the identification system. The Raspberry Pi operates with great accuracy and speed. An object is photographed by the navigation system's camera module, processed using the single shot detection (SSD) algorithm, and then converted to text by an optical character recognition (OCR) engine before being communicated over the speaker using the text-to-speech (TTS) converter tool. The object recognition and the text navigation system are specially designed for blind individuals to operate the tool conveniently in a solitary manner.

Visually impaired and blind people face several difficulties in their daily life. This was the primary motivation of this work as to create and assemble an object detector that can assist people with visual impairments using OpenCV and TensorFlow API on Raspberry Pi and provide an audio output for the detected objects using Espeak; Text-to-Speech Synthesizer. Single Shot Detector (SSD) model with MobileNet v2 has been employed to perform the detection with high accuracy and processing speed. The scripts are written in Python which utilizes the model to recognize the objects with boxes and provide class of the objects. The recognized image category is extracted and stored in a text file. The developed system provides aid to a visually impaired person for performing tasks independently using real-time object detection and identification technology. Developed system can successfully provide information about detected object in the form of an audio output to the visually impaired person.KeywordsRaspberry PiTensorFlowFaster-RCNNOpenCVPythonText-To-Speech Synthesizer

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.

Objectives/Aim: The present paper deals with building a smart assistant with the aim of assisting the visually impaired people in mobility with confidence by realizing the nearby obstacles and also implement image processing techniques to recognize people. Methods: We use Raspberry Pi 4 which has increased computing performance and is interfaced to the picamera, GPS and GSM modules. Arduino pro mini with buzzer, ultrasonic sensors and vibration motors are used for obstacle detection. Certain libraries important for image processing are used such as OpenCV, Dlib, face detection (Haar cascades, HOG + Linear SVM, or CNNs), espeak for converting text to speech. Programming was implemented through Python and Arduino compiler. Findings: Analysis was carried out using the proposed system for the blind and visually impaired people who could move around comfortably with confidence and were also able to detect objects and recognize people. Hence the proposed system removes the use for the cumbersome white cane in exchange for small and handy modules which can be used in the form of wearables mounted on shoulders, hands and legs to detect the obstacles from multiple directions and provide for a comfortable wear. It was determined that the Raspberry pi 4 was incapable of running CNN detection for which ideally computer GPU was required, so HOG detection method was used instead. Application/Improvements: This project can be implemented mainly in the commercial field of helping visually impaired people with poor eyesight or being completely blind. Industrial applications can be devised and enhanced like robots and machineries along with Security, Identifying and Tracking. Keywords: Face Recognition; Obstacle Detection; Open Source Computer Vision (OpenCV); Deep Learning; Histogram of Oriented Gradients (HOG); Support Vector Machine (SVM); Convolutional Neural Network (CNN)

The mobile robotics industry is related to creating mobile robots that can move around in physical environments. Different types of mobile robot designs for obstacle avoidance have been experimented in the past based on different sensors, trajectory algorithms, etc. The chapter presents implementation details of different obstacle avoiding robots (OARs) using sensors, Bluetooth module, and IoT modules. The sensor-based obstacle-avoiding robots are designed using ultrasonic sensors and Arduino microcontrollers. Bluetooth-based obstacle-avoiding robots have been designed using Arduino mega and Bluetooth module and an Android application. IoT-based obstacle-avoiding robots can be designed in three different ways, using ethernet shield, node MCU, or Raspberry Pi. The IoT-based obstacle-avoiding robot using Raspberry Pi is the most popular mobile robot model that uses maximum on-chip modules in comparison to other designs, and also, the design can be extended by using cameras to use images for sensing the objects in order to avoid collisions.

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.

Blind people face the problem in daily life. They can't even walk without any aid. Many times they rely on others for help. Several technologies for the assistance of visually impaired people have been developed. Among the various technologies being utilized to assist the blind, Computer Vision-based solutions are emerging as one of the most promising options due to their affordability and accessibility. This paper proposes a system for visually impaired people. The proposed system aims to create a wearable visual aid for visually impaired people in which speech commands are accepted by the user. Its functionality addresses the identification of objects and signboards. This will help the visually impaired person to manage day-to-day activities and navigate through his/her surroundings. Raspberry Pi is used to implement artificial vision using python language on the Open CV platform.

Blind people face significant challenges, including difficulty reading, recognizing objects, and navigating safely. According to WHO, 30 million are permanently blind, and 285 million have vision impairment. Visually impaired individuals struggle to detect obstacles, making walking dangerous. Special equipment like voice services and electronic sticks help improve their daily lives. This paper proposes a wearable system for blind people that uses a Raspberry Pi, webcam, and ultrasonic sensor to detect obstacles. The system provides audio feedback through earphones, alerting users to nearby objects. It aims to create a low-cost, compact solution for obstacle detection, offering a sense of artificial vision to help blind people navigate independently. The system uses Python and OpenCV for object detection, providing feedback through speech and warning sounds

VisAssist is an innovative, portable, and low-cost system for enhancing independence and mobility in the visually impaired. The integrated hardware-software architecture will ensure real-time environmental awareness and detection of obstacles. A Raspberry Pi micro-controller acts as a central processing and control unit, while ultrasonic sensors and a camera module are interfaced to it for data acquisition and environmental analysis. These ultrasonic sensors estimate object distances from the individual, and the camera provides relevant information for contextual understanding of those objects. As soon as an obstacle is detected within the threshold range, immediate audio feedback is provided through earphones or a speaker linked with a TTS engine. The design ensures that the system can work exclusively offline without any dependency on internet connectivity and has been tested for both indoor and outdoor environments. Compactness, lightweight, and energy efficiency further support its suitability for daily use. With embedded system design, sensor fusion, and speech processing, VisAssist enhances user safety, situational awareness, and self-reliance, hence adding a new dimension to assistive technology for visually impaired people.

The current chapter demonstrates the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and fuzzy proportional integral derivative (PID) controller to control the position of a 6-DoF custom 3D-printed feeding robotic arm. The chapter uses an open-source platform to show how machine vision, robot kinematics, and positional control may be coordinated. Throughout its function, the robotic arm travels through several intermediary locations in the 3D Cartesian space. The robot is designed to collect the food from the bowl and bring it up close to the user’s face. The destination point, which is dependent on where the patient’s face is, is in this instance the most important component. The vision camera provides the location of the face, and the inverse kinematics algorithm gives the joint angles for that location. To control the position of six servo actuators for the destination point, we employed an ANFIS and FPID-based positional controllers. The last three medium-torque servos are controlled by FPID, and the initial three high-torque servos are controlled by ANFIS. The FPID offered an average 15% overshoot, whereas the ANFIS offered a mere 5% overshoot. It was decided to control just high-torque servos using ANFIS and the rest actuators using FPID because the complete system was running on an affordable open-source platform like an Arduino or Raspberry Pi, and we wanted to reduce the computational overload on the Raspberry Pi.