scholarly journals From Offline to Real-Time Distributed Activity Recognition in Wireless Sensor Networks for Healthcare: A Review

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2786
Author(s):  
Rani Baghezza ◽  
Kévin Bouchard ◽  
Abdenour Bouzouane ◽  
Charles Gouin-Vallerand
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Activity Recognition ◽  
Scientific Literature ◽  
State Of The Art ◽  
Distributed Artificial Intelligence ◽  
Wireless Sensor ◽  
Time Machine ◽  
Time Activity ◽  
Starting Point

This review presents the state of the art and a global overview of research challenges of real-time distributed activity recognition in the field of healthcare. Offline activity recognition is discussed as a starting point to establish the useful concepts of the field, such as sensor types, activity labeling and feature extraction, outlier detection, and machine learning. New challenges and obstacles brought on by real-time centralized activity recognition such as communication, real-time activity labeling, cloud and local approaches, and real-time machine learning in a streaming context are then discussed. Finally, real-time distributed activity recognition is covered through existing implementations in the scientific literature, and six main angles of optimization are defined: Processing, memory, communication, energy, time, and accuracy. This survey is addressed to any reader interested in the development of distributed artificial intelligence as well activity recognition, regardless of their level of expertise.

scholarly journals ViCrypt to the Rescue: Real-Time, Machine-Learning-Driven Video-QoE Monitoring for Encrypted Streaming Traffic

2020 ◽  
Vol 17 (4) ◽  
pp. 2007-2023
Author(s):  
Sarah Wassermann ◽  
Michael Seufert ◽  
Pedro Casas ◽  
Li Gang ◽  
Kuang Li
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Time Machine ◽  
Streaming Traffic

scholarly journals IEEE Access Special Section Editorial: Real-Time Machine Learning Applications in Mobile Robotics

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 89694-89698
Author(s):  
Aysegul Ucar ◽  
Jessy W. Grizzle ◽  
Maani Ghaffari ◽  
Mattias Wahde ◽  
H. Levent Akin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Mobile Robotics ◽  
Special Section ◽  
Time Machine ◽  
Machine Learning Applications

Adaptive Monitor Placement for Near Real-time Node Failure Localisation in Wireless Sensor Networks

2022 ◽  
Vol 18 (1) ◽  
pp. 1-41
Author(s):  
Pamela Bezerra ◽  
Po-Yu Chen ◽  
Julie A. McCann ◽  
Weiren Yu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
State Of The Art ◽  
Global Analysis ◽  
Wireless Sensor ◽  
Greedy Heuristics ◽  
Network Tomography ◽  
Topology Changes ◽  
End To End

As sensor-based networks become more prevalent, scaling to unmanageable numbers or deployed in difficult to reach areas, real-time failure localisation is becoming essential for continued operation. Network tomography, a system and application-independent approach, has been successful in localising complex failures (i.e., observable by end-to-end global analysis) in traditional networks. Applying network tomography to wireless sensor networks (WSNs), however, is challenging. First, WSN topology changes due to environmental interactions (e.g., interference). Additionally, the selection of devices for running network monitoring processes (monitors) is an NP-hard problem. Monitors observe end-to-end in-network properties to identify failures, with their placement impacting the number of identifiable failures. Since monitoring consumes more in-node resources, it is essential to minimise their number while maintaining network tomography’s effectiveness. Unfortunately, state-of-the-art solutions solve this optimisation problem using time-consuming greedy heuristics. In this article, we propose two solutions for efficiently applying Network Tomography in WSNs: a graph compression scheme, enabling faster monitor placement by reducing the number of edges in the network, and an adaptive monitor placement algorithm for recovering the monitor placement given topology changes. The experiments show that our solution is at least 1,000× faster than the state-of-the-art approaches and efficiently copes with topology variations in large-scale WSNs.

scholarly journals Genetic algorithm–optimized support vector machine for real-time activity recognition in health smart home

2020 ◽  
Vol 16 (11) ◽  
pp. 155014772097151
Author(s):  
Yan Hu ◽  
Bingce Wang ◽  
Yuyan Sun ◽  
Jing An ◽  
Zhiliang Wang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Real Time ◽  
Activity Recognition ◽  
Smart Home ◽  
Feature Representation ◽  
Support Vector ◽  
Typical Application ◽  
Time Activity ◽  
Health Smart Home

Health smart home, as a typical application of Internet of things, provides a new solution for remote medical treatment. It can effectively relieve pressure from shortage of medical resources caused by aging population and help elderly people live at home more independently and safely. Activity recognition is the core of health smart home. This technology aims to recognize the activity patterns of users from a series of observations on the user’ actions and the environmental conditions, so as to avoid distress situations as much as possible. However, most of the existing researches focus on offline activity recognition, but not good at online real-time activity recognition. Besides, the feature representation techniques used for offline activity recognition are generally not suitable for online scenarios. In this article, the authors propose a real-time online activity recognition approach based on the genetic algorithm–optimized support vector machine classifier. In order to support online real-time activity recognition, a new sliding window-based feature representation technique enhanced by mutual information between sensors is devised. In addition, the genetic algorithm is used to automatically select optimal hyperparameters for the support vector machine model, thereby reducing the recognition inaccuracy caused by manual tuning of hyperparameters. Finally, a series of comprehensive experiments are conducted on freely available data sets to validate the effectiveness of the proposed approach.

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