Deep Learning-Based Activity Monitoring for Smart Environment Using Radar

2021 ◽  
pp. 91-123
Author(s):  
N. Susithra ◽  
G. Santhanamari ◽  
M. Deepa ◽  
P. Reba ◽  
K. C. Ramya ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Monitoring ◽  
Smart Environment

Using a distributed deep learning algorithm for analyzing big data in smart cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammed Anouar Naoui ◽  
Brahim Lejdel ◽  
Mouloud Ayad ◽  
Abdelfattah Amamra ◽  
Okba kazar
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Data Storage ◽  
Smart City ◽  
Smart Cities ◽  
Smart Environment ◽  
Data Systems ◽  
Content Type ◽  
Big Data Systems

PurposeThe purpose of this paper is to propose a distributed deep learning architecture for smart cities in big data systems.Design/methodology/approachWe have proposed an architectural multilayer to describe the distributed deep learning for smart cities in big data systems. The components of our system are Smart city layer, big data layer, and deep learning layer. The Smart city layer responsible for the question of Smart city components, its Internet of things, sensors and effectors, and its integration in the system, big data layer concerns data characteristics 10, and its distribution over the system. The deep learning layer is the model of our system. It is responsible for data analysis.FindingsWe apply our proposed architecture in a Smart environment and Smart energy. 10; In a Smart environment, we study the Toluene forecasting in Madrid Smart city. For Smart energy, we study wind energy foresting in Australia. Our proposed architecture can reduce the time of execution and improve the deep learning model, such as Long Term Short Memory10;.Research limitations/implicationsThis research needs the application of other deep learning models, such as convolution neuronal network and autoencoder.Practical implicationsFindings of the research will be helpful in Smart city architecture. It can provide a clear view into a Smart city, data storage, and data analysis. The 10; Toluene forecasting in a Smart environment can help the decision-maker to ensure environmental safety. The Smart energy of our proposed model can give a clear prediction of power generation.Originality/valueThe findings of this study are expected to contribute valuable information to decision-makers for a better understanding of the key to Smart city architecture. Its relation with data storage, processing, and data analysis.

scholarly journals Activity Monitoring of Islamic Prayer (Salat) Postures using Deep Learning

2020 ◽  
Author(s):  
Anis Koubaa ◽  
Adel Ammar ◽  
Bilel Benjdira ◽  
Abdullatif Al-Hadid ◽  
Belal Kawaf ◽  
...  
Keyword(s):  
Deep Learning ◽  
Activity Monitoring ◽  
Islamic Prayer

scholarly journals 5G-enabled contactless multi-user presence and activity detection for independent assisted living

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aboajeila Milad Ashleibta ◽  
Ahmad Taha ◽  
Muhammad Aurangzeb Khan ◽  
William Taylor ◽  
Ahsen Tahir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Well Being ◽  
Home Health ◽  
Activity Monitoring ◽  
Single Subject ◽  
Channel State ◽  
Average Accuracy ◽  
Wireless Signal ◽  
Simultaneous Identification

AbstractWireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.

scholarly journals Evaluation of Activity Monitoring Algorithm based on Smart Approaches

2020 ◽  
Vol 2 (3) ◽  
pp. 175-181
Author(s):  
Vivekanadam B
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Web Applications ◽  
Activity Monitoring ◽  
Online Classes ◽  
Learning Techniques ◽  
Monitoring Algorithm ◽  
Online Group ◽  
Online Web

Activity monitoring in online group meetings has become a needed application in the COVID-19 situation. During the lockdown period, most of the teaching classes were conducted through online web applications. The number of attendees in such classes are very higher and it is not to be manageable by a single tutor of the class. The applications are also designed to show only several number of person’s faces in a particular window. To improve the quality of such online classes, it is mandatory to verify the listener’s activity. The paper evaluates certain artificial intelligence based deep learning techniques for finding a suitable approach for monitoring the listener’s activity in real time.

Deep Learning

2009 ◽  
Author(s):  
Stellan Ohlsson
Keyword(s):  
Deep Learning
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