Computer-mediated emotional regulation: Detection of emotional changes using non-parametric cumulative sum

León, Enrique Chávez; Montalban, Iraitz; Schlatter, Sarah; Dorronsoro, Inigo

doi:10.1109/iembs.2010.5627089

Cited by 4 publications

(7 citation statements)

References 17 publications

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“…Indeed, it has been demonstrated that negative emotions have adverse effects on the immune system of a person [227]. Emotions are typically communicated through three channels: audio (speech), face and body gestures (visual), and internal physiological changes such as blood pressure, heart beat rate, or respiration.…”

Section: Applicationsmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

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Ambient Intelligence (AmI) is a new paradigm in information technology aimed at empowering people’s capabilities by the means of digital environments that are sensitive, adaptive, and responsive to human needs, habits, gestures, and emotions. This futuristic vision of daily environment will enable innovative human-machine interactions characterized by pervasive, unobtrusive and anticipatory communications. Such innovative interaction paradigms make ambient intelligence technology a suitable candidate for developing various real life solutions, including in the health care domain. This survey will discuss the emergence of ambient intelligence (AmI) techniques in the health care domain, in order to provide the research community with the necessary background. We will examine the infrastructure and technology required for achieving the vision of ambient intelligence, such as smart environments and wearable medical devices. We will summarize of the state of the art artificial intelligence methodologies used for developing AmI system in the health care domain, including various learning techniques (for learning from user interaction), reasoning techniques (for reasoning about users’ goals and intensions) and planning techniques (for planning activities and interactions). We will also discuss how AmI technology might support people affected by various physical or mental disabilities or chronic disease. Finally, we will point to some of the successful case studies in the area and we will look at the current and future challenges to draw upon the possible future research paths.

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Section: Applicationsmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

et al. 2013

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“…For profile monitoring purposes, not only the output but also the model structure are relevant to monitor the sequential evolution of all variables involved in the studied process, which is not the case for the proposals under this category where the model structure is intractable. Such is the case of black box approaches related to neural networks (NN) [20], [21], [22], [23], decision trees [24], [25], and support vector machines (SVM) [26], [27].…”

Section: Data-driven Methodsmentioning

confidence: 99%

“…Instead, it means that the analyzed process is multivariate. In certain control charts, the most relevant feature after a principal component analysis (PCA) [43], or the difference between the observed value and the predicted response [20], [21], [22], [44], [45], is controlled.…”

Section: A Patient Monitoringmentioning

confidence: 99%

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

2023

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In recent years, the adoption of statistical process monitoring (SPM) techniques in healthcare has been successful. For instance, biosurveillance and biosignal monitoring have demonstrated direct benefits. As the latest reviews of the literature show, parametric SPM techniques have been implemented to evaluate the quality-of-service hospitals provide, track medical equipment, monitor safety markers, or assess the improvements made by quality projects. However, as shown in this research, world-trending topics in data science that include data-driven approaches integrated with SPM have not been reviewed. To bridge this gap and shed light on new research, a systematic review of scientific databases and a taxonomic literature analysis were performed. For the scientometric analysis, a set of bibliometric indicators were obtained to portray the performance of each subtopic, such as examining growth kinetics, identifying top authors, journals, countries and affiliations, as well as creating network maps of co-authorship and keyword co-occurrence. Additionally, the taxonomic analysis involved grouping proposals by methodological approach. Each approach was explained and discussed to identify the advantages, limitations, and challenges that researchers and practitioners may encounter. SPM researchers and practitioners require more flexibility in data-driven approaches to account for frequency unbalance, complexity, dimensionality problems, and speed. Those working in data-driven and computer-oriented areas can expand their toolbox by incorporating sequential approaches to enhance the power of their classifiers, assess risk, reduce misspecification, and adopt model-oriented mindsets.INDEX TERMS Data-driven, healthcare, scientometric, statistical process monitoring.

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“…Recent advances in neurology and psychology have demonstrated the importance of emotions in various aspects of our lives and, in particular, in the field of healthcare and wellbeing. Indeed, it has been demonstrated that negative emotions have adverse effects on the immune system of a person [227]. Emotions are typically communicated through three channels: audio (speech), face and body gestures (visual), and internal physiological changes such as blood pressure, heart beat rate, or respiration.…”

Section: E Emotional Well-beingmentioning

confidence: 99%

A Survey on Ambient Intelligence in Healthcare

Esch

2013

Proc. IEEE

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Ambient intelligence (AmI) is a computing paradigm wherein conventional input and output media no longer exist. Instead, sensors and processors are integrated into conventional objects that harmonize with people in their living situations. AmI relies on artificial intelligence (AI) to perform these duties. It obtains contextual information from embedded sensors, interprets it, and adapts the environment to interpreted needs. AmI is context-aware, personalized, anticipatory, adaptive, ubiquitous, and transparent to the user. As an intelligent paradigm, it builds upon traditional AI foundations (reasoning, activity recognition, decision making, spatiotemporal logic) and adds advances from other fields: data collection via sensor networks, robotics, and human/-computer interactive interfaces.AmI offers many enticing solutions for a healthcare domain that is beset by problems such as lack of sustainability and cost inefficiencies. Existing solutionsVweb-based platforms, electronic health records, smartphone apps for physiological monitoringVare hampered by scalability problems, security risks, and privacy issues, and none of them offer a continuous view of conditions spanning many years. Recent advances in sensor networks are spearheading revolutions in low-cost healthcare monitoring systems in homes and living environments. Common applications would include monitoring the status of patients with chronic diseases, assistive care, persuasive services for encouraging healthier lifestyles, rehabilitation, and support for healthcare professionals through improved communication and monitoring tools.The supporting infrastructure and technologies used in AmI systems for healthcare break into two main categories: body area networks (BANs) and dense/mesh sensor networks in smart homes. In addition, recent trends in sensor networks are driving new developments in epidermal electronics and microelectromechanical system (MEMS) sensors.In BANs, sensors are attached to clothing or the body itself, enabling continuous monitoring of heartbeat, temperature, physical activity, blood pressure, electrocardiogram, electroencephalography, and electromyography. The infrastructure can remotely stream data to a doctor's site, so a diagnosis can be made in real time. Data can be stored in a medical database and link to emergency alerts and intelligent management. The networks are efficient and cost effective. Sensors consume little power and their batteries are long lasting. As sensors get mass produced, costs will scale downward. BANs, moreover, can interface with a wide variety of network infrastructures. It is useful to think of BANs as being composed of three layers or tiers of communication from low level to high level: intra-BAN (2-m radio range), inter-BAN (body to access points communication), and beyond-BAN (streaming body sensor data to metropolitan areas via a gateway device).In dense/mesh sensor networks, processors and ambient sensors are embedded in everyday objects (clothes, household devices, furniture) to enable unobtrusive, ...

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Computer-mediated emotional regulation: Detection of emotional changes using non-parametric cumulative sum

Cited by 4 publications

References 17 publications

A Survey on Ambient Intelligence in Healthcare

A Survey on Ambient Intelligence in Healthcare

A Review on Statistical Process Control in Healthcare: Data-Driven Monitoring Schemes

A Survey on Ambient Intelligence in Healthcare

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