Physical Actions Architecture: Context-Aware Activity Recognition in Mobile Devices

2011 ◽  
pp. 19-27 ◽  
Author(s):  
Gonzalo Blázquez Gil ◽  
Antonio Berlanga ◽  
José M. Molina
Keyword(s):  
Mobile Devices ◽  
Activity Recognition ◽  
Context Aware

scholarly journals Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT applications

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
VanDung Nguyen ◽  
Tran Trong Khanh ◽  
Tri D. T. Nguyen ◽  
Choong Seon Hong ◽  
Eui-Nam Huh
Keyword(s):  
Cloud Computing ◽  
Augmented Reality ◽  
Mobile Devices ◽  
Computing System ◽  
Mobile User ◽  
Computation Offloading ◽  
Context Aware ◽  
Computing Systems ◽  
Iot Applications ◽  
Service Times

AbstractIn the Internet of Things (IoT) era, the capacity-limited Internet and uncontrollable service delays for various new applications, such as video streaming analysis and augmented reality, are challenges. Cloud computing systems, also known as a solution that offloads energy-consuming computation of IoT applications to a cloud server, cannot meet the delay-sensitive and context-aware service requirements. To address this issue, an edge computing system provides timely and context-aware services by bringing the computations and storage closer to the user. The dynamic flow of requests that can be efficiently processed is a significant challenge for edge and cloud computing systems. To improve the performance of IoT systems, the mobile edge orchestrator (MEO), which is an application placement controller, was designed by integrating end mobile devices with edge and cloud computing systems. In this paper, we propose a flexible computation offloading method in a fuzzy-based MEO for IoT applications in order to improve the efficiency in computational resource management. Considering the network, computation resources, and task requirements, a fuzzy-based MEO allows edge workload orchestration actions to decide whether to offload a mobile user to local edge, neighboring edge, or cloud servers. Additionally, increasing packet sizes will affect the failed-task ratio when the number of mobile devices increases. To reduce failed tasks because of transmission collisions and to improve service times for time-critical tasks, we define a new input crisp value, and a new output decision for a fuzzy-based MEO. Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark algorithms in augmented reality, healthcare, compute-intensive, and infotainment applications. Simulation results show that our proposal provides better results in terms of WLAN delay, service times, the number of failed tasks, and VM utilization.

Context-Aware Photography Learning for Smart Mobile Devices

2015 ◽  
Vol 12 (1s) ◽  
pp. 1-24 ◽  
Author(s):  
Yogesh Singh Rawat ◽  
Mohan S. Kankanhalli
Keyword(s):  
Mobile Devices ◽  
Context Aware ◽  
Smart Mobile ◽  
Smart Mobile Devices

scholarly journals Context-Aware Community Construction in Proximity-Based Mobile Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Na Yu ◽  
Qi Han
Keyword(s):  
Social Networking ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Context Aware ◽  
Two Phase ◽  
Space And Time ◽  
Shared Information ◽  
Influence Graph ◽  
Social Applications ◽  
Share Information

Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

scholarly journals Context-Aware Human Activity Recognition in Industrial Processes

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 134
Author(s):  
Friedrich Niemann ◽  
Stefan Lüdtke ◽  
Christian Bartelt ◽  
Michael ten Hompel
Keyword(s):  
Activity Recognition ◽  
Time Management ◽  
Recognition Performance ◽  
Context Information ◽  
Context Aware ◽  
Recognition Model ◽  
Movement Data ◽  
Information Activity ◽  
The Subject ◽  
Motion Capturing

The automatic, sensor-based assessment of human activities is highly relevant for production and logistics, to optimise the economics and ergonomics of these processes. One challenge for accurate activity recognition in these domains is the context-dependence of activities: Similar movements can correspond to different activities, depending on, e.g., the object handled or the location of the subject. In this paper, we propose to explicitly make use of such context information in an activity recognition model. Our first contribution is a publicly available, semantically annotated motion capturing dataset of subjects performing order picking and packaging activities, where context information is recorded explicitly. The second contribution is an activity recognition model that integrates movement data and context information. We empirically show that by using context information, activity recognition performance increases substantially. Additionally, we analyse which of the pieces of context information is most relevant for activity recognition. The insights provided by this paper can help others to design appropriate sensor set-ups in real warehouses for time management.

MetaQ: A knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns

2016 ◽  
Vol 25 ◽  
pp. 104-124 ◽  
Author(s):  
Georgios Meditskos ◽  
Stamatia Dasiopoulou ◽  
Ioannis Kompatsiaris
Keyword(s):  
Activity Recognition ◽  
Activity Patterns ◽  
Context Aware

A User Context-Aware Advertising Framework for the Mobile Web

2010 ◽  
pp. 1-15
Author(s):  
Nan Jing ◽  
Yong Yao ◽  
Yanbo Ru
Keyword(s):  
Mobile Devices ◽  
Online Advertising ◽  
User Interaction ◽  
Context Aware ◽  
Mobile Platforms ◽  
Social Community ◽  
Mobile Web ◽  
Web Technologies ◽  
Critical Components ◽  
Advertising Content

Context-aware advertising is one of the most critical components in the Internet ecosystem today because most WWW publisher’s revenue highly depends on the relevance of the displayed advertisement to the context of the user interaction. Existing research works in context-aware advertising mainly focus on analyzing either the content of the web page (in which it is also called contextual advertising), or the keywords of the user search. However, we have identified the limitations of these works when being extended into mobile web, which has become a major platform for users to access Internet with thanks to the new lightweight web technologies and the development of mobile devices. These mobile devices are equipped with networking capabilities and sensors that provide versatile contexts including physical environment, user internal and social community. These contexts, which are far beyond just page content and search keywords, should be well organized and utilized for online advertising to gain better user experience and reaction. In this chapter, we point out the aforementioned limitations of the existing works in context-aware advertising when being applied for mobile platforms. We also discuss the characteristics of the contexts that are available on mobile devices and clearly describe the challenges of utilizing these contexts to optimize the advertisement on mobile platforms. We then present a context-aware advertising framework that collects and integrates the user contexts to select, generate, and present advertising content. The purpose of this framework is to provide the mobile users with targeted and purposeful advertisement. Finally, we discuss the implementation aspects and one specific application of this framework and outline our future plans.

Mobile Spatial Interaction and Mediated Social Navigation

2011 ◽  
pp. 2604-2608
Author(s):  
Mark Bilandzic ◽  
Marcus Foth
Keyword(s):  
Web 2.0 ◽  
Mobile Devices ◽  
Mobile Networks ◽  
Spatial Interaction ◽  
Value Added ◽  
Handheld Devices ◽  
Context Aware ◽  
Social Navigation ◽  
Global Positioning ◽  
Community Platforms

The increasing ubiquity of location and context-aware mobile devices and applications, geographic information systems (GIS) and sophisticated 3D representations of the physical world accessible by lay users is enabling more people to use and manipulate information relevant to their current surroundings (Scharl & Tochtermann, 2007). The relationship between users, their current geographic location and their devices are summarised by the term “mobile spatial interaction” (MSI), and stands for the emerging opportunities and affordances that location sensitive and Internet capable devices provide to its users. The first major academic event which coined the term in its current usage was a workshop on MSI (see http://msi.ftw.at/) at the CHI 2007 (Fröhlich et al., 2007). Mobile spatial interaction is grounded in a number of technologies that recently started to converge. First, the development of mobile networks and mobile Internet technologies enables people to request and exchange specific information from anywhere at anytime. Using their handheld devices people can, for example, check the latest news, request recent stock exchange values or communicate via mobile instant messaging. The second enabler is global positioning technology. Mobile devices with integrated Global Positioning System (GPS) receivers—soon to be joined by the Russian Global Navigation Satellite System (GLONASS) and the European Galileo system—are aware of their current latitude and longitude coordinates and can use this data as value added information for context-aware services, that is, mobile applications that refer to information relevant to the current location of the user. A possible use scenario for such an information request would be, for example, “find all clubs and pubs in a radius of 500 meters from my current position.” The focus of this work is to enrich the opportunities given by such location aware services with selected Web 2.0 design paradigms (Beer & Burrows, 2007; Kolbitsch & Maurer, 2006) toward mobile social networking services that are bound to specific physical places. User participation, folksonomy and geotagging are three design methods that have become popular in Web 2.0 community-platforms and proven to be effective information management tools for various domains (Casey & Savastinuk, 2007; Courtney, 2007; Macgregor & McCulloch, 2006). Applying such a design approach for a mobile information system creates a new experience of collaboration between mobile users, a step toward what Jaokar refers to as the Mobile Web 2.0 (Jaokar & Fish, 2006), that is, a chance for mediated social navigation in physical spaces.

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