A framework for tracking system aiding disabilities

Mohanraj, I.; Siddharth, S.

doi:10.1109/icctac.2017.8249992

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…A number of papers addressed issues arising when using wheelchairs in challenging environments with early works by Madarasz et al (1986) demonstrating a self-navigating wheelchair within an office building. Later works mounted sensors on the chair to automatically stop the chair when physical obstacles were detected (Patel and Prakash 2015) and provide obstacle detection and avoidance (Mohanraj and Siddharth 2017). The problem of navigating wheelchairs autonomously was considered using 'low cost' navigation systems (Des Handicaps 2016) and more recently 'smart wheelchairs' were developed (Gomez Torres et al 2019).…”

Section: Disability and Autonomous Systems Researchmentioning

confidence: 99%

Trustworthy Autonomous Systems and Disabled Young People: A Critical Literature Review

White¹,

Picardi²,

Paterson³

et al. 2023

Preprint

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Autonomous robotic and AI assistive systems are becoming increasingly prevalent in public and private spheres—often heralded as having the potential to revolutionise the everyday lives of disabled people. The reality could not be more different: disabled young people are routinely excluded from such spheres and, significantly, from the recent efforts to ensure that autonomous systems are trustworthy. Our critical literature review explores how trustworthy autonomous systems (TAS) can be framed within a critical disability studies perspective. We introduce the field of TAS and explore the interdisciplinary field of critical disability studies. This brings us to a key question: how is disability conceptualised, methodologically explored and represented in the TAS literature? We address this question by considering the ways in which disability and TAS have hitherto connected in the literature. We conclude with suggestions for future research and innovation that place disabled young people at the core of research and innovation in TAS.

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Section: Disability and Autonomous Systems Researchmentioning

confidence: 99%

Trustworthy Autonomous Systems and Disabled Young People: A Critical Literature Review

White¹,

Picardi²,

Paterson³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Communication is critical, especially when uploading images and transmitting output inferences back to the user. Implication of Violation: in scenarios of slow or unstable internet [37], there could be significant delays in system responses due to the time taken to upload images and relay output inferences, potentially undermining the real-time requirements of the model. A complete loss of internet connectivity would result in the full failure of the proposed system, rendering it non-operational.…”

Section: Assumptions and Justificationsmentioning

confidence: 99%

An Assistive Model for the Visually Impaired Integrating the Domains of IoT, Blockchain and Deep Learning

Jadon,

Taluri,

Birthi

et al. 2023

Symmetry

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Internet of Things, blockchain and deep learning are emerging technologies that have recently gained popularity due to their various benefits and applications. All three domains have had success independently in various applications such as automation, agriculture, travel, finance, image recognition, speech recognition, and many others. This paper proposes an efficient, lightweight, and user-friendly solution to help visually impaired individuals navigate their way by taking advantage of modern technologies. The proposed method involves the usage of a camera lens attached to a Raspberry Pi device to capture live video frames of the user’s environment, which are then transmitted to cloud storage. The link to access these images is stored within a symmetrical private blockchain network (no superior access), where all deep learning servers act as nodes. The deep learning model deployed on these servers analyses the video frames to detect objects and feeds the output back to the cloud service. Ultimately, the user receives audio notifications about obstacles through an earphone plugged into the Raspberry Pi. In particular, when running the model on a high-performing network and an RTX 3090 GPU, the average obstacle notification time is reported within 2 s, highlighting the proposed system’s responsiveness and effectiveness in aiding visually impaired individuals.

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