A low-cost wearable support system for visually disabled people

Gonzalez-Delgado, Luis; Serpa-Andrade, Luis; Calle-Urgilez, K.; Guzhñay-Lucero, Antonny; Robles-Bykbaev, Vladimir; Mena-Salcedo, Ma. Fernanda

doi:10.1109/ropec.2016.7830606

Cited by 7 publications

(1 citation statement)

References 17 publications

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“…Pe¯geot et al [27] proposed a scene text global positioning framework utilized for finding and following text districts in video outlines caught by a wearable camera. Gonza¯les-Delgado et al [28] proposed a savvy gloves framework that aids in gathering a portion of the everyday necessities of the BVIP, for example, face acknowledgment, programmed mail perusing, and programmed location of items, among different capabilities. Reminder et al [29] fostered a head-mounted signal acknowledgment framework.…”

Section: Introductionmentioning

confidence: 99%

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

2024

IRJMETS

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Section: Introductionmentioning

confidence: 99%

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

2024

IRJMETS

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IoT based Assistive Device for Deaf, Dumb and Blind People

Karmel

Sharma

pandya

et al. 2019

Procedia Computer Science

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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 low-cost wearable support system for visually disabled people

Cited by 7 publications

References 17 publications

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

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

IoT based Assistive Device for Deaf, Dumb and Blind People

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

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