AIoT-based Audio Recognition System for Smart Home Applications

2021 ◽  
Author(s):  
Bo-Wei Chen ◽  
Yu-Syuan Jhang ◽  
Hao-Ting Pai ◽  
Szu-Hong Wang ◽  
Ming-Hwa Sheu ◽  
...  
Keyword(s):  
Smart Home ◽  
Recognition System

scholarly journals Introduction to the Special Issue: Applications of Internet of Things

Symmetry ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 374 ◽  
Author(s):  
Chi-Hua Chen ◽  
Eyhab Al-Masri ◽  
Feng-Jang Hwang ◽  
Despo Ktoridou ◽  
Kuen-Rong Lo
Keyword(s):  
Internet Of Things ◽  
Smart Home ◽  
Data Science ◽  
Cooperative Spectrum Sensing ◽  
Smart Homes ◽  
Recognition System ◽  
Special Issue ◽  
Smart Transportation ◽  
User Classification ◽  
Novel Approach

This editorial introduces the special issue, entitled “Applications of Internet of Things”, of Symmetry. The topics covered in this issue fall under four main parts: (I) communication techniques and applications, (II) data science techniques and applications, (III) smart transportation, and (IV) smart homes. Four papers on sensing techniques and applications are included as follows: (1) “Reliability of improved cooperative communication over wireless sensor networks”, by Chen et al.; (2) “User classification in crowdsourcing-based cooperative spectrum sensing”, by Zhai and Wang; (3) “IoT’s tiny steps towards 5G: Telco’s perspective”, by Cero et al.; and (4) “An Internet of things area coverage analyzer (ITHACA) for complex topographical scenarios”, by Parada et al. One paper on data science techniques and applications is as follows: “Internet of things: a scientometric review”, by Ruiz-Rosero et al. Two papers on smart transportation are as follows: (1) “An Internet of things approach for extracting featured data using an AIS database: an application based on the viewpoint of connected ships”, by He et al.; and (2) “The development of key technologies in applications of vessels connected to the Internet”, by Tian et al. Two papers on smart home are as follows: (1) “A novel approach based on time cluster for activity recognition of daily living in smart homes”, by Liu et al.; and (2) “IoT-based image recognition system for smart home-delivered meal services”, by Tseng et al.

scholarly journals A Wearable Textile 3D Gesture Recognition Sensor Based on Screen-Printing Technology

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5068 ◽  
Author(s):  
Ferri ◽  
Llopis ◽  
Moreno ◽  
Ibañez Civera ◽  
Garcia-Breijo
Keyword(s):  
Gesture Recognition ◽  
Automotive Industry ◽  
Direct Contact ◽  
Smart Home ◽  
Screen Printing ◽  
Complete Solution ◽  
Recognition System ◽  
Printing Technology ◽  
Machine Interface ◽  
Screen Printing Technology

Research has developed various solutions in order for computers to recognize hand gestures in the context of human machine interface (HMI). The design of a successful hand gesture recognition system must address functionality and usability. The gesture recognition market has evolved from touchpads to touchless sensors, which do not need direct contact. Their application in textiles ranges from the field of medical environments to smart home applications and the automotive industry. In this paper, a textile capacitive touchless sensor has been developed by using screen-printing technology. Two different designs were developed to obtain the best configuration, obtaining good results in both cases. Finally, as a real application, a complete solution of the sensor with wireless communications is presented to be used as an interface for a mobile phone.

scholarly journals An Acoustic-Based Smart Home System for People Suffering from Dementia

Technologies ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 29 ◽  
Author(s):  
Eleni Boumpa ◽  
Anargyros Gkogkidis ◽  
Ioanna Charalampou ◽  
Argyro Ntaliani ◽  
Athanasios Kakarountas ◽  
...  
Keyword(s):  
Smart Home ◽  
Aging In Place ◽  
Recognition System ◽  
Smart Devices ◽  
Home Support ◽  
Person Recognition ◽  
Dementia Syndrome ◽  
Neurological Research ◽  
Familiar Person

Aging-in-place can reduce the progress of dementia syndrome and improve the quality of life of the sufferers and their families. Taking into consideration the fact that numerous neurological research results suggest the use of sound as a stimulus for empowering the memory of the sufferer, an innovative information home support system for people suffering from dementia is proposed. The innovation of the proposed system is found in its application, that is to integrate a home system for assisting with person recognition via a sound-based memory aid service. Furthermore, the system addresses the needs of people suffering from dementia to recognize their familiars and have better interaction and collaboration, without the need for training. The system offers a ubiquitous recognition system, using smart devices like smart-phones or smart-wristbands. When a familiar person is detected in the house, then a sound is reproduced on the smart speakers, in order to stimulate the sufferer’s memory. The system identified all users and reproduced the appropriate sound in 100% of the cases. To the best of the authors’ knowledge, this is the first system of its kind for assisting person recognition via sound ever reported in the literature.

scholarly journals Smart Home Automation using Hand Gesture Recognition System

2019 ◽  
Vol 9 (2) ◽  
pp. 18-21
Keyword(s):  
Machine Learning ◽  
Gesture Recognition ◽  
Personal Computer ◽  
Smart Home ◽  
Recognition System ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Home Automation ◽  
Proof Of Concept ◽  
Visual Interpretation

Visual interpretation of hand gestures is a natural method of achieving Human-Computer Interaction (HCI). In this paper, we present an approach to setting up of a smart home where the appliances can be controlled by an implementation of a Hand Gesture Recognition System. More specifically, this recognition system uses Transfer learning, which is a technique of Machine Learning, to successfully distinguish between pre-trained gestures and identify them properly to control the appliances. The gestures are sequentially identified as commands which are used to actuate the appliances. The proof of concept is demonstrated by controlling a set of LEDs that represent the appliances, which are connected to an Arduino Uno Microcontroller, which in turn is connected to the personal computer where the actual gesture recognition is implemented

scholarly journals PETS RECOGNITION SYSTEM FOR SMART HOME

2019 ◽  
Vol 6 (1) ◽  
pp. 133-137
Author(s):  
I.O. Palamarchuk ◽  
◽  
Yu.A. Bazaka ◽  
◽  
Keyword(s):  
Smart Home ◽  
Recognition System

scholarly journals Towards Activity Recognition through Multidimensional Mobile Data Fusion with a Smartphone and Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Junkuo Cao ◽  
Mingcai Lin ◽  
Han Wang ◽  
Jiacheng Fang ◽  
Yueshen Xu
Keyword(s):  
Deep Learning ◽  
Activity Recognition ◽  
Smart Home ◽  
Recognition System ◽  
Smart Phones ◽  
Test Evaluation ◽  
Mobile Data ◽  
Related Research ◽  
Model Training ◽  
Medicine Education

The field of activity recognition has evolved relatively early and has attracted countless researchers. With the continuous development of science and technology, people’s research on human activity recognition is also deepening and becoming richer. Nowadays, whether it is medicine, education, sports, or smart home, various fields have developed a strong interest in activity recognition, and a series of research results have also been put into people’s real production and life. Nowadays, smart phones have become quite popular, and the technology is becoming more and more mature, and various sensors have emerged at the historic moment, so the related research on activity recognition based on mobile phone sensors has its necessity and possibility. This article will use an Android smartphone to collect the data of six basic behaviors of human, which are walking, running, standing, sitting, going upstairs, and going downstairs, through its acceleration sensor, and use the classic model of deep learning CNN (convolutional neural network) to fuse those multidimensional mobile data, using TensorFlow for model training and test evaluation. The generated model is finally transplanted to an Android phone to complete the mobile-end activity recognition system.

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