Assistive device using computer vision and image processing for visually impaired; review and current status

Patel, Karan; Parmar, Bhavesh

doi:10.1080/17483107.2020.1786731

Cited by 18 publications

(7 citation statements)

References 14 publications

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“…Its application scope involves biomedicine, remote sensing aviation, industrial automation, and other fields. In recent years, the field of clothing gradually uses image extraction technology to extract clothing manual environmental features and process all kinds of clothing, and has made preliminary progress, which also shows that the use of AI technology to solve the problems in the clothing industry has attracted more and more attention and attention of AI research and clothing industry personnel [ 14 ]. From the perspective of garment contour and pattern extraction, many new garment styles are designed every year in the world, but few people have studied the extraction and analysis methods of environmental factors in traditional garment handicraft driven by AI technology.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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The application of artificial intelligence (AI) technology in the field of clothes can provide a good development mode and system under the social context of AI technology development. AI provides help for the development of intelligent clothing. Intelligent clothing is a high-tech product that integrates intelligent technology and clothing. It combines cutting-edge technologies in electronic information technology, sensor technology, textile science, and material science. In the extraction and analysis of environmental factors in clothing handicraft, AI technology has a considerable application prospect and a certain development potential. In order to improve the accuracy of environmental factors extraction in clothing handicraft, this paper uses convolutional neural network (CNN) to extract and analyze environmental factors in traditional clothing handicraft. We carried out experiments on the extraction of environmental factors in clothing handicrafts with pure color, few patterns, patterns, and complex background. The experimental results show that the CNN has a good effect on the extraction of environmental factors in clothing handicraft under different backgrounds. In addition, the model in this paper has good stability, accuracy, and feature extraction speed, which has high practical value and research significance.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Tao

Guo

2022

Journal of Environmental and Public Health

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“…As mentioned in Section 1 , previously proposed navigation assistive technology for people with disabilities, as well as recently proposed crowd monitoring systems, rely on image processing and computer vision [ 32 , 33 ]. These require an on-device high computational power and face high privacy concerns.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

Falcon-Caro,

Peytchev,

Sanei

2024

Bioengineering

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Here, we present an effective application of adaptive cooperative networks, namely assisting disables in navigating in a crowd in a pandemic or emergency situation. To achieve this, we model crowd movement and introduce a cooperative learning approach to enable cooperation and self-organization of the crowd members with impaired health or on wheelchairs to ensure their safe movement in the crowd. Here, it is assumed that the movement path and the varying locations of the other crowd members can be estimated by each agent. Therefore, the network nodes (agents) should continuously reorganize themselves by varying their speeds and distances from each other, from the surrounding walls, and from obstacles within a predefined limit. It is also demonstrated how the available wireless trackers such as AirTags can be used for this purpose. The model effectiveness is examined with respect to the real-time changes in environmental parameters and its efficacy is verified.

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“…Illustrative of this are the following applications: a) Natural Language Processing (NLP) algorithms capable of reading and comprehending human language have been adapted as virtual assistants for social service users, practitioners, or administrators (Chui et al, 2023); b) Computer Vision (CV)algorithms that process visual information can enhance public safety by detecting instances of violence, locating missing persons (Trilupaitytė, 2022), aiding the visually impaired (Patel & Parmar, 2022), and assisting seniors with dementia (Bucholc et al, 2023;Chignell et al, 2020); c) Roboticsmobile systems capable of autonomous movement and environmental interaction (Burgard, 2023) can significantly aid in the care and support of the elderly (Wellman & Rajan, 2017); d) Machine Learning (ML)this method uses historical or real-time data to predict future trends (Engin & Treleaven, 2019), assisting in identifying vulnerable tenant issues (Yeung, 2018), modelling migration patterns (Robinson & Dilkina, 2018), supporting the homeless (Abelson et al, 2014), developing victimization models (Shah et al, 2021), and optimizing food distribution and usage (Shi et al, 2020).…”

Section: Exploring the Range Of Ai Applications In Social Welfarementioning

confidence: 99%

Conceptual framework for ethical artificial intelligence development in social services sector

Seniutis,

Gružauskas,

Lileikiene

et al. 2024

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This research explores the domain of Artificial Intelligence (AI) for social good, with a particular emphasis on its application in social welfare and service delivery. The study seeks to establish a universal conceptual framework for ethically integrating AI into the social services sector, recognizing the sector's significant yet underexplored potential for AI utilization. The objective is to develop a comprehensive framework applicable to the ethical deployment of AI in social services, using Lithuania as a case study to illustrate its practicality. This involves analysing the political discourse on AI, examining its applications in social welfare, identifying ethical challenges, evaluating the digitalization progress in Lithuania's public services, and formulating guidelines for AI integration at various stages of delivering social services. Our methodology is rooted in document analysis, encompassing a thorough review of both normative and scientific literature pertinent to the ethical application of AI in social welfare. Key findings reveal that AI's anticipated positive impacts on diverse social and economic areas, as highlighted in political declarations, are being partially realized, as corroborated by scientific studies. Although the global application of AI in social welfare is expanding, Lithuania presents a unique case with its strategic planning gaps in this sector. The developed conceptual framework offers vital criteria for the ethical implementation of AI systems designed to be universally applicable to various stages of social services, accommodating different AI applications, client groups, and institutional environments.

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Assistive device using computer vision and image processing for visually impaired; review and current status

Cited by 18 publications

References 14 publications

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

[Retracted] Artificial Intelligence Technology Driven Environmental Factors Extraction and Analysis Method in Traditional Clothing Handicraft

Adaptive Network Model for Assisting People with Disabilities through Crowd Monitoring and Control

Conceptual framework for ethical artificial intelligence development in social services sector

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