An embedded framework enabling access of elderly and disabled persons to IP-based emergency communications

Fanariotis, Anastasios; Andriopoulou, Foteini; Orphanoudakis, Theofanis; Politis, Ilias

doi:10.1016/j.micpro.2019.05.006

Cited by 10 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, we adopted and extended the SmartHub platform [7] combined with updated specifications of WiLIFE [8] and advances in responder technology components [1,9,10,12,21,24,57].…”

Section: Composite Ngfr Platformmentioning

confidence: 99%

“…− Data from assistive devices focusing on Interacting with People with Disabilities (IPD) [60]. − Next Generation (NG) of PSAP such as projects [10,12], that will be able to provide sensor health data to the first responders as well as data from assistive devices, e.g., speech synthesizers and symbol dashboards. Using the NG PSAP channel, new functions of the NGFR platform can be integrated.…”

Section: Specification Of the Architectural Frameworkmentioning

confidence: 99%

“…Note that the development of personalized assistants to support first responders in emergency situations in which disabled people are involved was drastically accelerated in the past decade, e.g., [12,60].…”

Section: Integration Into the Ngfr Hubmentioning

confidence: 99%

“…Since then, many advanced studies such as [8][9][10][11][12] created a technological framework for the NGFR. Nowadays, it is a standard R&D practice to explore a wide spectrum of tools, from cloud and Internet of Things (IoT) resources [2,4,13], to devices for detecting humans and animals behind the walls [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Next Generation Computing and Communication Hub for First Responders in Smart Cities

Shaposhnyk,

Lai,

Wolbring

et al. 2024

Sensors

View full text Add to dashboard Cite

This paper contributes to the development of a Next Generation First Responder (NGFR) communication platform with the key goal of embedding it into a smart city technology infrastructure. The framework of this approach is a concept known as SmartHub, developed by the US Department of Homeland Security. The proposed embedding methodology complies with the standard categories and indicators of smart city performance. This paper offers two practice-centered extensions of the NGFR hub, which are also the main results: first, a cognitive workload monitoring of first responders as a basis for their performance assessment, monitoring, and improvement; and second, a highly sensitive problem of human society, the emergency assistance tools for individuals with disabilities. Both extensions explore various technological-societal dimensions of smart cities, including interoperability, standardization, and accessibility to assistive technologies for people with disabilities. Regarding cognitive workload monitoring, the core result is a novel AI formalism, an ensemble of machine learning processes aggregated using machine reasoning. This ensemble enables predictive situation assessment and self-aware computing, which is the basis of the digital twin concept. We experimentally demonstrate a specific component of a digital twin of an NGFR, a near-real-time monitoring of the NGFR cognitive workload. Regarding our second result, a problem of emergency assistance for individuals with disabilities that originated as accessibility to assistive technologies to promote disability inclusion, we provide the NGFR specification focusing on interactions based on AI formalism and using a unified hub platform. This paper also discusses a technology roadmap using the notion of the Emergency Management Cycle (EMC), a commonly accepted doctrine for managing disasters through the steps of mitigation, preparedness, response, and recovery. It positions the NGFR hub as a benchmark of the smart city emergency service.

show abstract

“…In our study, we adopted and extended the SmartHub platform [7] combined with updated specifications of WiLIFE [8] and advances in responder technology components [1,9,10,12,21,24,57].…”

Section: Composite Ngfr Platformmentioning

confidence: 99%

Section: Specification Of the Architectural Frameworkmentioning

confidence: 99%

Section: Integration Into the Ngfr Hubmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Next Generation Computing and Communication Hub for First Responders in Smart Cities

Shaposhnyk,

Lai,

Wolbring

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…The LCD used in this study is a 3.5-inch LCD specifically for the Raspberry Pi. The LCD used has a resolution of 480x320 pixels [38]. After getting a suitable block diagram for this learning media, the next step is to make a schematic design that describes the shape of the electronic circuit of this learning media.…”

Section: B Hardware and Software Designmentioning

confidence: 99%

3D Geometric Shape and Colors Interactive Learning Media using Raspberry Pi, OpenCV, and TensorFlow Lite

Wicaksono,

Rahmatya

2023

Int. J. Adv. Sci. Eng. Inf. Technol.

View full text Add to dashboard Cite

This study aims to create learning media for 3D geometric shapes and colors for early childhood. Early childhood can enter 3D objects into the device. Then, the device will mention and explain the shape and color of the object in question. As a contribution, this research provides a brief introduction and learning related to geometric shapes for early childhood. The method used is experimental. The hardware components of this system are Raspberry Pi 3, RPi Camera, PIR Sensor, 3.5inch LCD, and Speaker. Python, OpenCV, and TensorFlow Lite are used from the software side. OpenCV is used to detect colors. TensorFlow Lite is used to detect the shape of geometric objects. In this study, the model used is a custom model specifically for TensorFlow Lite, which was trained through Google Colab. This media has a learning mode and a question mode. In learning mode, early childhood only needs to insert objects into the learning media and will get information in the form of images and sounds related to the object. In question mode, the learning media will provide instructions and questions to enter objects with a specific color. After that, the learning media will determine whether the answer is right or wrong and give the score. The results of tests that cover both modes reach a success percentage of 100%, where this learning media can recognize and explain every shape and color of objects in both modes.

show abstract

Performance evaluation of CCOs for the optimization of low-power pressure-based implantable wireless systems

Miguel

Lechuga

Allende

et al. 2020

Microprocessors and Microsystems

View full text Add to dashboard Cite

An embedded framework enabling access of elderly and disabled persons to IP-based emergency communications

Cited by 10 publications

References 11 publications

Next Generation Computing and Communication Hub for First Responders in Smart Cities

Next Generation Computing and Communication Hub for First Responders in Smart Cities

3D Geometric Shape and Colors Interactive Learning Media using Raspberry Pi, OpenCV, and TensorFlow Lite

Performance evaluation of CCOs for the optimization of low-power pressure-based implantable wireless systems

Contact Info

Product

Resources

About