다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

European Resuscitation Council Guidelines for Resuscitation.

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

Sensor-Based Activity Recognition

This paper introduces a knowledge-driven approach to real-time, continuous activity recognition based on multisensor data streams in smart homes The approach goes beyond the traditional data-centric methods for activity recognition in three ways First, it makes extensive use of domain knowledge in the life cycle of activity recognition Second, it uses ontologies for explicit context and activity modeling and representation Third and finally, it exploits semantic reasoning and classification for activity inferencing, thus enabling both coarse-grained and fine-grained activity recognition In this paper, we analyze the characteristics of smart homes and Activities of Daily Living (ADL) upon which we built both context and ADL ontologies We present a generic system architecture for the proposed knowledge-driven approach and describe the underlying ontology-based recognition process Special emphasis is placed on semantic subsumption reasoning algorithms for activity recognition The proposed approach has been implemented in a function-rich software system, which was deployed in a smart home research laboratory We evaluated the proposed approach and the developed system through extensive experiments involving a number of various ADL use scenarios An average activity recognition rate of 9444 percent was achieved and the average recognition runtime per recognition operation was measured as 25 seconds

A Knowledge-Driven Approach to Activity Recognition in Smart Homes

This article describes an investigation to determine the optimal placement of accelerometers for the purpose of detecting a range of everyday activities. The paper investigates the effect of combining data from accelerometers placed at various bodily locations on the accuracy of activity detection. Eight healthy males participated within the study. Data were collected from six wireless tri-axial accelerometers placed at the chest, wrist, lower back, hip, thigh and foot. Activities included walking, running on a motorized treadmill, sitting, lying, standing and walking up and down stairs. The Support Vector Machine provided the most accurate detection of activities of all the machine learning algorithms investigated. Although data from all locations provided similar levels of accuracy, the hip was the best single location to record data for activity detection using a Support Vector Machine, providing small but significantly better accuracy than the other investigated locations. Increasing the number of sensing locations from one to two or more statistically increased the accuracy of classification. There was no significant difference in accuracy when using two or more sensors. It was noted, however, that the difference in activity detection using single or multiple accelerometers may be more pronounced when trying to detect finer grain activities. Future work shall therefore investigate the effects of accelerometer placement on a larger range of these activities.

https://www.mdpi.com/1424-8220/13/7/9183/pdf

Optimal Placement of Accelerometers for the Detection of Everyday Activities

https://pure.ulster.ac.uk/ws/files/11226001/MULVENNA-Review_of_ICT-Based_Services_for_Identified_Unmet_Needs_in_People_with_Dementia.pdf

Review of ICT-Based Services for Identified Unmet Needs in People with Dementia

For future network scenarios to exhibit autonomic behaviour, both networks and application components and services need to be aware of their computational and environmental context, and must tune their activities accordingly. In this position paper, we propose an abstract architecture for knowledge networks that addresses the key issues of how both physical contextual knowledge and social knowledge from the users of communication networks can be used to form a knowledge space in support of autonomic agents dealing with network elements and applications. We discuss that the availability of raw contextual data is not enough to achieve meaningful autonomic behaviours. Rather, contextual information should be properly organised into ‘networks of knowledge', to be exploited by both network and application components as the basic ‘nervous system' in which situational stimuli reify into digital knowledge, and by means of which components can properly orchestrate their activities in a globally meaningful way. Here we firstly discuss the fundamental role of knowledge networks, and try to sketch what actual form and position such knowledge networks could assume. Then, we analyse some simple scenarios of use, showing how it is possible for the components of an autonomic communication system to build such knowledge networks autonomously; and, at the same time, to exploit them for orchestrating their activities in a type of stigmergy-based knowledge-rich system. Eventually, we sketch a rough research agenda and discuss the relations with other research areas.

Chris D. Nugent

Papers

Sensor-Based Activity Recognition

A Knowledge-Driven Approach to Activity Recognition in Smart Homes

Optimal Placement of Accelerometers for the Detection of Everyday Activities

Review of ICT-Based Services for Identified Unmet Needs in People with Dementia

Knowledge networks