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Wearable Computers
Published in Julie A. Jacko, The Human–Computer Interaction Handbook, 2012
Daniel Siewiorek, Asim Smailagic, Thad Starner
The next step in the evolution of wearable computers is context awareness. Context-aware computing is aware of a user’s state and surroundings and the mobile computer modifies its behavior based on this information. A user’s context can be quite rich, consisting of attributes such as physical location, physiological state (such as body temperature, heart rate, and skin resistance), emotional state (such as angry, distraught, or calm), personal history, daily behavioral patterns, and so on. If a human assistant were given such context, he or she would make decisions in a proactive fashion, anticipating user needs. In making these decisions, the assistant would typically not disturb the user at inopportune moments except in an emergency. The goal is to enable mobile computers to play an analogous role, exploiting context information to significantly reduce demands on human attention. Context-aware intelligent agents can deliver relevant information when a user needs that information. These data make possible many exciting new applications, such as augmented reality, context-aware collaboration, wearable assisted living, augmented manufacturing, and maintenance.
Internet of Things: A Context-Awareness Perspective
Published in Lu Yan, Yan Zhang, Laurence T. Yang, Huansheng Ning, The Internet of Things, 2008
Davy Preuveneers, Yolande Berbers
The notion of context is widely understood in the pervasive and ubiquitous computing domain as relevant information referring to the situation and circumstances in which a computational artifact is embedded. As such, context awareness is the ability to detect and respond to contextual changes. The goal of context-aware computing is to gather and utilize information to positively affect the provisioning of services that are considered appropriate for a particular person or device. Therefore, context information can only be considered useful if it can be interpreted. As context is a rather vague concept, we first mention how context has been defined by leading experts in the field before continuing to describe how context can be modeled, acquired, and used to achieve autonomous and nonintrusive behavior in a service-oriented architecture.
User Activity Recognition through Software Sensors
Published in Qurban A. Memon, Distributed Networks, 2017
Stephan Reiff-Marganiec, Kamran Taj Pathan, Yi Hong
In this chapter, we have introduced software sensors as an inexpensive alternative to hardware sensors. They are particularly well suited to detecting user activities as they can exploit the multitude of data exchanged between users and the web-enabled services they use. Hardware sensors will still have their place and are orthogonal to software sensors in that sense: they are very well suited to obtain environment data such as temperature or physical location measurements. The exchanged data enrich the information available about user activities and hence will allow for more effective context-aware systems.
Context Awareness in Recognition of Affective States: A Systematic Mapping of the Literature
Published in International Journal of Human–Computer Interaction, 2023
Sandro Oliveira Dorneles, Rosemary Francisco, Débora Nice Ferrari Barbosa, Jorge Luis Victória Barbosa
Context-aware systems have improved user interaction and personalized services provision in recent years. The evolution of personal mobile devices, such as smartphones, tablets, smartwatches, and other types of wearable devices has allowed the development of systems that are part of everyday life in different areas, such as health (Abdellatif et al., 2019; Bavaresco, Barbosa, et al., 2020; Pittoli et al., 2018; Vianna & Barbosa, 2019), entertainment (Renjith et al., 2020) and education (Abech et al., 2016; Barbosa et al., 2013; Fernández-Caramés & Fraga-Lamas, 2019; Ferreira et al., 2020; Wagner et al., 2014). We used Dey’s (2001) definition of “context” in this article, which describes it as any information that can be used to characterize the situation of an entity, and this entity can be any person, place, or object that could be considered relevant to the interaction between a user and an application, which includes the user and the applications themselves.
Context-aware system for cardiac condition monitoring and management: a survey
Published in Behaviour & Information Technology, 2022
Godwin Okechukwu Ogbuabor, Juan Carlos Augusto, Ralph Moseley, Aléchia van Wyk
The main difference between Dey (2001) and our definition is that Dey (2001) emphasised more about the system and data, while we look at context and context-awareness from the stakeholder point of view. Context can be categorised into primary and secondary context (Perera et al. 2014). Primary context is any information relevant to the stakeholder retrieved without using any existing context, while secondary context is relevant information derived from the primary context (Temdee and Prasad 2018). For instance, location data from GPS sensor is primary context while calculating distance covered by fusion of location data is secondary context. Context-aware applications are enhancing interaction between human and systems in areas such as industry (Imtiaz et al. 2014), transportation (Binjammaz, Al-Bayatti, and Al-Hargan 2016) security (Park, Han, and Chung 2007), business (Zhao and Mafuz 2015) and healthcare (Bricon-Souf and Newman 2007), as well as smart home (Li, Suna, and Hua 2012). These systems provide a platforms for timely and better decision-making process for the user. There are different application domain of context-awareness (Yürür et al. 2016). However, the focus of this survey is in healthcare targeting cardiac condition monitoring and management.
A personalized Human Factors Analysis and Classification System (HFACS) for construction safety managementbased on context-aware technology
Published in Enterprise Information Systems, 2022
Ning Tang, Hao Hu, Feng Xu, J. K. W. Yeoh, David Kim Huat Chua, Zhe Hu
Cyber-physical systems are an important application of the Internet of Things, and the potential to greatly improve the autonomous collection of personalised data. The mature subsystem technology includes location sensor systems, temperature sensor systems, humidity sensor systems, light intensity sensor systems, image sensor systems. Redundancy and resilience are the two main problems related to cyber-physical systems, and system context is one reasonable method to address the problems (Lezoche and Panetto 2018).These sensor systems have different levels of accuracy, and engineers can choose different kinds of sensors to suit different situations. Events are more important than functions and objectives when building cyber-physical systems (Jiang, Chen, and Duan 2014), and the multi-criteria decision-making (MCDM) method is widely used in cyber-physical assessment (Silva and Jardim-Goncalves 2019). Errors and data processing are also the focus of cyber-physical research (Wang and Zhang 2020; Xu and Duan 2018). Context-aware technology is a branch of cyber-physical systems, which can detect, infer, and evaluate contextual information to provide personalised services for users. This kind of technology learns about the state of objectives and to obtain contextual information using different kinds of sensors with limited explicit intervention (Hoyos, García-Molina, and Botía 2010). The context-aware technology offers major benefits for smart-living (Forkan, Khalil, and Tari 2014; Niu and Wang 2016), traffic monitoring (Wan et al. 2014), and e-medical management (Serral et al. 2015). Moreover, previous research has shown its potential for personalised management (Abbas, Zhang, and Khan 2015; Alfonso-Cendón et al. 2016; Forkan, Khalil, and Tari 2014; Serral et al. 2015) .Context-aware technology has also begun to be used in construction lean production management (Dave et al. 2016) and safety management (Zhu et al. 2016). Possible collisions can be prevented by using GPS to locate on-site workers and mobile equipment (Zhu et al. 2016). Wearable devices, radio transceivers, wake-up sensors and alarm actuators are efficient for monitoring, localising, and warning on-site labourers of potential dangers (Kanan, Elhassan, and Bensalem 2018).