WITHDRAWN: Design and implementation assertive structure aimed at visually impaired people using artificial intelligence techniques

Gurumoorthy, Sasikumar; Padmavathy, T. V.; Jayasree, L.; Radhika, G

doi:10.1016/j.matpr.2020.12.1138

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…Gurumoorthy et al [ 81 ] proposed a technique using the rear camera of a mobile phone to capture and analyze the image in front of the visually impaired. To execute tasks related to computer vision, this device uses Microsoft Cognitive Services.…”

Section: Related Workmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

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Over a billion people around the world are disabled, among whom 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, and poor environments and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required in order to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach using a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on the smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image-processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system’s hardware and software design, having constructed their prototype implementations and tested them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than USD 80. Essentially, we provide designs of an inexpensive, miniature green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach enables faster inference and decision-making using relatively low energy with smaller data sizes, as well as faster communications for edge, fog, and cloud computing.

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Section: Related Workmentioning

confidence: 99%

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed

Katib

Albeshri

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Gurumoorthy et al [81] proposed a technique using the rear camera of a mobile phone to capture and analyze the image in front of the visually impaired. To execute tasks related to computer vision, they benefit from Microsoft Cognitive Services.…”

Section: Camerasmentioning

confidence: 99%

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Busaeed¹,

Katib²,

Albeshri³

et al. 2022

Preprint

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Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

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“…Relying solely on their hearing senses for steering is detrimental to their safety (Gouda et al, 2022). According to the latest worldwide statistics, it is reported that hundreds of millions of people worldwide are visually impaired, many of them blind (Gurumoorthy et al, 2021). According to WHO estimates, the world's blind population is between 40 and 45 million (Gbenga et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Innovative Blind Stick for The Visually Impaired

Khan,

Qureshi,

Zahid

et al. 2024

ABBDM

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Visual impairment is an appellation that alludes to the state of being visionless. Individuals with optical blockage are likely to have limitations in their independence and movement, as well as a higher risk of falling, fractures, injuries, poor mental health, and social isolation. Thus, we propose the design of a blind stick to help blind people and assist them in familiarizing themselves with a clear path. In our proposed prototype, multiple sensors are attached to the individual's stick. The sensor detects an obstruction, the receiver's output activates, and the microcontroller detects this change since the receiver's outputs are inputs to the microcontroller. This stick detects the impediment in front and reacts by vibrating or mandating the user. Furthermore, a smoke sensor, fall detection sensor, heart rate, SPO2 level sensor, and Temperature sensor are utilized to detect multiple parameters and not only indicate the individual using the blind stick but to the caregiver as well through an application connected to the blind stick. Moreover, GPS tracking is also present which helps the individual with the stick and also informs the caregiver about the location of the blind person. The results of our proposed prototype demonstrate that this electronic device is a sufficient solution for overcoming the difficulties faced by visually impaired people and their caregivers and allowing them to live their lives independently. In conclusion, our innovative blind stick offers a transformative solution for visually impaired individuals, enhancing their mobility and independence. We invite readers to delve into the full paper to discover the full extent of its capabilities and its potential to make a meaningful difference in the lives of millions worldwide.

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WITHDRAWN: Design and implementation assertive structure aimed at visually impaired people using artificial intelligence techniques

Cited by 5 publications

References 1 publication

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

LidSonic V2.0: A LiDAR and Deep-Learning-Based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

LidSonic V2.0: LiDAR and Deep Learning-based Green Assistive Edge Device to Enhance Mobility for the Visually Impaired

Innovative Blind Stick for The Visually Impaired

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