Profile Recognition for Accessibility and Inclusivity in Smart Cities Using a Thermal Imaging Sensor in an Embedded System

Baghezza, Rani; Bouchard, Kévin; Bouzouane, Abdenour; Vallerand, Robert J.

doi:10.1109/jiot.2021.3127137

Cited by 5 publications

(3 citation statements)

References 77 publications

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“…Rather it is used with, mobile applications or big data. Varghese et al [50] use IoT with app development and AI for making an app to reduce food waste, while Baghezza et al [51] use IoT with machine learning to improve accessibility for people with reduced mobility. Finally, Bibri and Krogstie [52] highlight the potential opportunities offered by IoT and big data for environmental sustainability.…”

Section: Discussionmentioning

confidence: 99%

Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

Takaoka,

Ahlers,

Ådlandsvik

et al. 2023

2023 IEEE/ACM 7th International Workshop on Green and Sustainable Software (GREENS)

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Climate change is an existential threat to Europe and the world, and is impacting and influencing people. At the same time, the urbanization of the world is increasing, meaning that these challenges need to be solved mainly in cities. Cities are also increasingly using digital technologies to become smarter, which will be a crucial part of the future cities. The EU has initiated the European Green Deal in order to overcome these challenges, and to make Europe the first climate neutral continent by 2050. A part of this is the EU Cities Mission aiming for climate-neutral transitions in frontrunner cities by 2030. The EU-funded project Creating Actionable Futures (CrAFt) bridges these ambitions with the New European Bauhaus principles, to ensure that climate-neutral transitions in cities will be sustainable, inclusive, and beautiful. This systematic literature review was performed to understand digital tools in this specific context.

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Section: Discussionmentioning

confidence: 99%

Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

Takaoka,

Ahlers,

Ådlandsvik

et al. 2023

2023 IEEE/ACM 7th International Workshop on Green and Sustainable Software (GREENS)

View full text Add to dashboard Cite

show abstract

“…Smart cities are an increasingly used term within tech circles that refers to, among other things, the usage of machine learning and AI for the automation of many aspects of city management. Many of these applications are related to traffic management [ 14 ] or to the profiling of individuals [ 144 ]. It is very important for these models to be able to handle a large number of objects at any given time; for this reason, inference time is of a higher priority for these applications.…”

Section: Application Based System Comparisonmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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“…Intelligent cities use information and communication technologies to improve the quality of life of their citizens, the local economy, transportation, traffic management, the environment, and interaction with the government (Ismagilova et al, 2019). With the modernization of smart cities and the technological advancement of the Internet of things (IoT), we are now reaching a point where technology can be woven into the fabric of our cities (Dembsky et al, 2020;Baghezza et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Smart citizen in architecture and landscape. Method design based on spatial intelligence and universal accessibility learning by students with intellectual disabilities

Cantarero-García

Mollá

Lezcano

2023

Front. Built Environ.

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The Smart Citizen concept is at an early stage of research in the field of architecture and landscape architecture. ICTs and their use by citizens have been studied exclusively in the fields of engineering and telecommunications sciences as well as Artificial Intelligence. This study seeks to publicize the Smart Citizen concept in architecture and landscape from a spatial understanding and the perception of citizens with intellectual disabilities. A real case study developed by architects in collaboration with psychologists focused on the development of spatial intelligence for people with intellectual disabilities is presented in this study. “The students that participated in these activities belong to San Pablo CEU University course financed by ONCE Foundation (FONCE) and the European Social Fund.” The purpose is to question the effectiveness of the use of technologies for said cognitive development and how even the increased use of GPS navigator systems could be a detriment to the citizen when trying to orient him or herself in open spaces. The result to highlight in this review is to know if a person with a disability is equally capable of understanding a space and navigate it without the aid of GPS as a person without an intellectual disability. Both analogical tools (2D plan) and digital tools (GPS) are used under the same conditions. New lines of research are presented in the study of spatial intelligence through innovating tools or TICs in relation to urban elements where concepts like scale, proportion, light, and shade were identified.

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Profile Recognition for Accessibility and Inclusivity in Smart Cities Using a Thermal Imaging Sensor in an Embedded System

Cited by 5 publications

References 77 publications

Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

Towards understanding digital support contributing to climate neutral, inclusive, and beautiful cities: A systematic literature review

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Smart citizen in architecture and landscape. Method design based on spatial intelligence and universal accessibility learning by students with intellectual disabilities

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