An IoT and Wearables-Based Smart Home for ALS Patients

Chen, Xuhang; Fu, Zhe; Song, Zhiyong; Yang, Lijing; Ndifon, Ajeck M.; Su, Zhiwei; Liu, Zheng; Gao, Shuo

doi:10.1109/jiot.2022.3176202

Cited by 7 publications

(4 citation statements)

References 45 publications

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“…Synthesizing the objective function and constraints, the control node selection model is given as shown in (7).…”

Section: Modeling Of Control Node Selectionmentioning

confidence: 99%

“…The transport layer is responsible for transmitting the data acquired by the perception layer to the application layer over long distances. The application level is oriented to user needs, including intelligent transportation, 6 smart home, 7 smart factory, 8 battlefield intelligence perception and control, 9 and other rich and diverse applications.…”

Section: Introductionmentioning

confidence: 99%

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HIC: A high‐reliable in‐band control network for perception layer of software‐defined IoT

Huang

Zhou

et al. 2023

Trans Emerging Tel Tech

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With the rapid development of the Internet of Things (IoT) and the popularity of the software‐defined idea, the software‐defined Internet of Things (SD‐IoT) came into being. However, the transmission conflict between control flow and data flow in the perception layer with poor channel resources has become an important constraint for SD‐IoT. Thus, this paper adopts the idea of isolated forwarding control flow based on an in‐band channel, that is, selecting some sensor nodes to be responsible for forwarding control flow, and proposes high‐reliable in‐band control network (HIC), a high‐reliability in‐band control network. In order to support real‐time tasks in the IoT environment, HIC adopts a divided control area to support parallel responses of multiple partially overlapping tasks. Further, in each control area, the graph theory is used to model the control node selection problem, and an algorithm for efficiently solving the model is designed based on the community detection method. Finally, we developed the HIC experimental platform composed of HIC‐topology and HIC‐simulation and carried out simulation experiments and analysis from topology generation, maintenance, and operation. The experimental results show that HIC could better adapt to the SD‐IoT under multi‐task deployment, and has great advantages in terms of the success rate and time of topology rapid maintenance compared with similar solutions.

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“…Synthesizing the objective function and constraints, the control node selection model is given as shown in (7).…”

Section: Modeling Of Control Node Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HIC: A high‐reliable in‐band control network for perception layer of software‐defined IoT

Huang

Zhou

et al. 2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The electronics as fuel of the Internet of Things technology is surely leading us in a new way of conducting our lives and cities [2], also allowing the optimization of the production processes of companies and industries and the management of services and infrastructures, limiting the consumption of resources and pollution. Management of public lighting [3][4][5][6][7], air quality [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] and noise pollution monitoring [23][24][25][26][27][28][29], smart home [30][31][32][33][34][35][36][37][38][39][40][41][42][43], smart roads, smart cars, urban mobility and transport [44][45][46][47][48]…”

Section: Introductionmentioning

confidence: 99%

A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers

Scandurra

Arena

Ciofi

2023

Sensors

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The Internet of Things (IoT) is gaining more and more popularity and it is establishing itself in all areas, from industry to everyday life. Given its pervasiveness and considering the problems that afflict today’s world, that must be carefully monitored and addressed to guarantee a future for the new generations, the sustainability of technological solutions must be a focal point in the activities of researchers in the field. Many of these solutions are based on flexible, printed or wearable electronics. The choice of materials therefore becomes fundamental, just as it is crucial to provide the necessary power supply in a green way. In this paper we want to analyze the state of the art of flexible electronics for the IoT, paying particular attention to the issue of sustainability. Furthermore, considerations will be made on how the skills required for the designers of such flexible circuits, the features required to the new design tools and the characterization of electronic circuits are changing.

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“…Создание и внедрение эффективных и точных систем распознавания жестов рук способствует развитию инновационных технологий человеко-машинного взаимодействия (a human-machine interaction, HMI) [1]. Системы HMI на основе жестов рук применяются в компьютерных играх [2] и приложениях с виртуальной реальностью [3], при управлении интеллектуальными устройствами «умного» дома [4], во взаимодействиях человека и робота (a human-robot interaction, HRI) [5] или человека с беспилотным летательным аппаратом. Для этих целей распознавание жестов рук представляет собой процесс отслеживания человеческих жестов, идентификации и преобразования их в семантически значимые команды для управления устройствами.…”

Section: Introductionunclassified

Бейнелерді Классификациялау Үшін Нейрондық Желілерге Трансферттік Оқытуды Қолдану

Сатыбалдина

2023

BULLETIN Series Physical and Mathematical Sciences

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В последние годы машинное обучение применяется в различных областях и приложениях, и его использование продолжает увеличиваться. Сложные задачи требуют большого количества данных и времени для обучения, и данные нужно пометить для обучения с учителем. Трансферное обучение позволяет сократить объем исходных данных и время обучения или повысить точность решения интеллектуальной задачи. В работе представлены результаты экспериментальных исследований по распознаванию статических жестов рук на основе предлагаемой нами модели глубокой нейронной сети, с традиционным полным обучением на всех параметрах, и сверточной нейронной сети архитектуры VGG-16, предобученной с использованием концепции трансфера обучения. Программная реализация системы распознавания жестов выполнена с использованием Python- библиотек обработки изображений, полученных с глубинного сенсора захвата изображений. Производительность предлагаемой в работе модели глубокой нейронной сети сравнивается с моделью трансферного обучения для модифицированной архитектуры VGG-16. Ключевые слова: глубокое обучение, сверточная нейронная сеть, трансфер обучения. Соңғы жылдары машиналық оқыту әртүрлі салаларда және қолданбаларда қолданылады және оны пайдалану артып келеді. Күрделі тапсырмаларды шешу үшін көп деректер мен уақыт қажет, ал деректер бақыланатын оқыту үшін белгіленуі керек. Трансферттік оқыту бастапқы деректер көлемін және оқу уақытын қысқартуға немесе интеллектуалды мәселені шешудің дәлдігін арттыруға мүмкіндік береді. Бұл мақалада біз ұсынған терең нейрондық желі үлгісіне негізделген статикалық қол қимылдарын тану бойынша эксперименталды зерттеулердің нәтижелері келтірілінген, барлық параметрлер бойынша дәстүрлі толық оқыту және трансферттік оқыту тұжырымдаманы пайдалана отырып алдын ала дайындалған VGG-16 архитектурасының конволюциялық нейрондық желісі алынып отыр. Қимылдарды тану жүйесін бағдарламалық қамтамасыз етулуі Python кескінді өңдеу кітапханалары арқылы терең суретке түсіру сенсорынан алынып жасалыңды. Ұсынылған терең нейрондық желі моделінің өнімділігі модификацияланған VGG-16 архитектурасы үшін трансферттік оқыту моделімен салыстырылады. Түйін сөздер: терең оқыту, конволюциялық нейрондық желі, трансферттік оқыту. In recent years, machine learning has been applied in various fields and applications, and its use continues to increase. Complex tasks require a lot of data and time to train, and the data needs to be labeled for supervised training. Transfer learning allows reducing the amount of raw data and learning time, and improving the accuracy of solving an intellectual problem. This paper presents the results of experimental studies on the static hand gesture recognition based on our proposed deep neural network model, with traditional full learning on all parameters, and a convolutional neural network of the VGG-16 architecture, pre-trained using the concept of transfer learning. Software implementation of the gesture recognition system was made using Python image processing libraries obtained from the image capture sensor. The performance of the proposed deep neural network model is compared with the model for the modified VGG-16 architecture and the transfer learning. Keywords: deep learning, convolutional neural network, transfer learning.

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An IoT and Wearables-Based Smart Home for ALS Patients

Cited by 7 publications

References 45 publications

HIC: A high‐reliable in‐band control network for perception layer of software‐defined IoT

HIC: A high‐reliable in‐band control network for perception layer of software‐defined IoT

A Brief Review on Flexible Electronics for IoT: Solutions for Sustainability and New Perspectives for Designers

Бейнелерді Классификациялау Үшін Нейрондық Желілерге Трансферттік Оқытуды Қолдану

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