A Smart Wearable Navigation System for Visually Impaired

Trent, Michael; Abdelgawad, Ahmed; Yelamarthi, Kumar

doi:10.1007/978-3-319-61949-1_35

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…The authors indicated that the prototype needs some improvements such as obstacle detection in function of the walker speed, employment of RFID tags for indoor settings, and the usage of reflective materials or leds for low light environments. Also, for navigation in outdoor settings, the researchers in [148] proposed a system based on iBeacons. In this technology, the system reads the current location of the users according to the ID of the iBeacon and compares it with Google Maps, providing feedback with voice commands.…”

Section: Educational Devices and Materialsmentioning

confidence: 99%

“…As a recommendation, the authors of the studies that used ultrasonic sensors mentioned that the white canes or smart sticks should have an angle between 15°to 45°regarding the floor to get better results for obstacle and pothole recognition. In general terms, sensor-based walking assistants are in their majority white or smart canes with ultrasonic or infrared sensors and GPS or RFID support that have been built with Arduino boards or in a few cases with Raspberry Pi [143], [145], [147], [148].…”

Section: Educational Devices and Materialsmentioning

confidence: 99%

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Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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Disabled People deal with a series of barriers that limit their inclusion, empowerment, wellbeing, and role in society with a special emphasis in low and medium-income countries. One of these barriers is concerning the accessibility and affordability of assistive technologies (ATs) that help to enhance the quality of life of these persons. In this context, this systematic literature review (SLR) analyzes and describes how free and open-source hardware (OSHW) and open software (OSS) are employed in the design, development, and deployment of low-cost ATs. In the SLR process, different ATs were analyzed for disabilities such as visual, mobility, upper body, prostheses, hearing & speaking, daily living, and participation in society. The ATs were designed with diverse OSHW and OSS technologies such as Arduino, Raspberry Pi, NVidia Jeston, OpenCV, YOLO, MobileNet, EEG and EMG signal conditioning devices, actuators, and sensors such as ultrasonic, LiDar, or flex. 809 studies were collected and analyzed from the database Web of Science, GitHub, and the specialized journals in OSHW HardwareX and the Journal of Open Hardware during the years 2013-2022. In the first part of the SLR, the bibliometric trends and topic clusters regarding the selected studies are described. Secondly, the ATs identified with open source technologies, e.g., sensor-based or computer vision-based, are described along with a complete state-ofart about these based on each disability recognized. Finally, the issues and challenges to this approach are explored including technical factors, documentation, government policies, and the inclusion of disabled people in open source co-creation. The purpose of this study is to inform practitioners, designers, or stakeholders about low-cost (frugal) ATs with OSHW and OSS, and thus promote their development, accessibility, and affordability, contributing to benefit the community of disabled people. INDEX TERMS Assistive technologies, disabled people, persons with disabilities, adaptive aids, open hardware, open-source hardware, open-source software, systematic literature review.

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Section: Educational Devices and Materialsmentioning

confidence: 99%

Section: Educational Devices and Materialsmentioning

confidence: 99%

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Ariza

Pearce

2022

IEEE Access

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“…Edge IoT devices exchange data via IoT protocols such as MQTT, CoAP, XMPP, DDS, AMQP [17] using WiFi, Bluetooth, and Ethernet networking technologies. On a short distance of up to 10 m, most IoT hardware employs Bluetooth classes 2 or 3 to be connected directly [18]. WiFi and Ethernet connections are mainly applied for longer distances.…”

Section: Review Of the Literaturementioning

confidence: 99%

“…The EEG signals are transmitted via Bluetooth to the HC-05 module connected to the Arduino Mega board. The information about edge IoT devices [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] is pre-sented to the disabled on the LCD 1602 display with an IIC adapter wired to the same Arduino Mega microcontroller. The double blink is used to detect an event where the disabled person chooses the option shown on display.…”

Section: Introductionmentioning

confidence: 99%

Prototyping Smart Home for Immobilized People: Eeg/MQTT-Based Brain-to-Thing Communication

Zubov

Qureshi²,

Köse³

et al. 2022

RIC

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Context. Immobilized people face additional barriers in almost all areas of life, including simple operations like turning the light on/off and controlling the air conditioner. The object of the study was to develop the brain-to-thin communication of affordable priceto control the smart home appliances by immobilized people from neck to toes. Objective. The goal of the work is to manage smart home appliances via brain-to-thing communication with EEG non-invasive electrodes, edge IoT devices, and MQTT protocol if the brain and eye control of the disabled work normally. Method. A non-invasive Sichiray TGAM brainwave EEG sensor kit captures signals and then transmit them via Bluetooth to the HC-05 module connected to the Arduino Mega microcontroller. Information about edge IoT devices is presented to the disabled on the LCD 1602 display wired to the same Arduino Mega. The disabled person chooses the option shown on display via the double blink that is detected if the quality of signal equals zero and low/mid gamma waves are less than ten in three consecutive Bluetooth packets. Control commands are sent from Arduino Mega (MQTT publisher) to the edge IoT devices (MQTT subscribers) that analyze them and start a specific operation like opening a door and turning the alarm on/off. Results. Five females and five males of different ages from 8 to 59 years old examined the control of smart home appliances with the Sichiray TGAM brainwave sensor kit. Everyone successfully handled the Sichiray headset and showed satisfaction with the brain-to-thing system. Conclusions. In this work, a smart home concept for immobilized people was developed using the brain-to-thing approach and the MQTT communication between the MQTT publisher, Sichiray TGAM brainwave EEG sensor kit connected via Bluetooth to the Arduino Mega microcontroller, and edge IoT devices total priced at USD 150. The most likely prospect of the presented work is to produce the sample that is ready to market.

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“…Similarly, Vyavahare et al [16] used a combination of ultrasonic sensors and computer vision techniques to build a wearable assistant that can perform obstacle detection and image description. Laubhan et al [17] and Trent et al [18] designed a wearable Electronic Travel Aid for the blind, which uses an array of ultrasonic sensors to survey the scene. Bai et al [19] proposed a depth image and multi-sensor-based algorithm to solve the problem of transparent and small obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

A Google Glass Based Real-Time Scene Analysis for the Visually Impaired

et al. 2021

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Blind and Visually Impaired People (BVIP) are likely to experience difficulties with tasks that involve scene recognition. Wearable technology has played a significant role in researching and evaluating systems developed for and with the BVIP community. This paper presents a system based on Google Glass designed to assist BVIP with scene recognition tasks, thereby using it as a visual assistant. The camera embedded in the smart glasses is used to capture the image of the surroundings, which is analyzed using the Custom Vision Application Programming Interface (Vision API) from Azure Cognitive Services by Microsoft. The output of the Vision API is converted to speech, which is heard by the BVIP user wearing the Google Glass. A dataset of 5000 newly annotated images is created to improve the performance of the scene description task in Indian scenarios. The Vision API is trained and tested on this dataset, increasing the mean Average Precision (mAP) score from 63% to 84%, with an IoU > 0.5. The overall response time of the proposed application was measured to be less than 1 second, thereby providing accurate results in realtime. A Likert scale analysis was performed with the help of the BVIP teachers and students at the "Roman Catherine Lobo School for the Visually Impaired" at Mangalore, Karnataka, India. From their response, it can be concluded that the application helps the BVIP better recognize their surrounding environment in real-time, proving the device effective as a potential assistant for the BVIP.

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A Smart Wearable Navigation System for Visually Impaired

Cited by 12 publications

References 8 publications

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Low-Cost Assistive Technologies for Disabled People Using Open-Source Hardware and Software: A Systematic Literature Review

Prototyping Smart Home for Immobilized People: Eeg/MQTT-Based Brain-to-Thing Communication

A Google Glass Based Real-Time Scene Analysis for the Visually Impaired

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