A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Activity and Location Recognition Using Wearable Sensors
IEEE Pervasive Computing
OGC® Sensor Web Enablement: Overview and High Level Architecture
GeoSensor Networks
Recognizing Affective Dimensions from Body Posture
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
Recognising Human Emotions from Body Movement and Gesture Dynamics
ACII '07 Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
A Single Camera Motion Capture System for Human-Computer Interaction
IEICE - Transactions on Information and Systems
Mobile health monitoring and smart sensors: a product line approach
Proceedings of the 2009 Euro American Conference on Telematics and Information Systems: New Opportunities to increase Digital Citizenship
Software Engineering for Self-Adaptive Systems
Software Engineering for Self-Adaptive Systems
Wearable Services in Risk Management
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Distributed recognition of human actions using wearable motion sensor networks
Journal of Ambient Intelligence and Smart Environments
Towards an activity-aware wearable computing platform based on an egocentric interaction model
UCS'07 Proceedings of the 4th international conference on Ubiquitous computing systems
A Logical Approach to Home Healthcare with Intelligent Sensor-Network Support
The Computer Journal
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The high number of accidents in living areas, work environments, and ambient, in general, can benefit of prevention mechanisms able to identify the causes and the indications which precede accidents, and to put in place strategies to avoid risks whenever this is possible. To this aim, this paper presents a risk management architecture for monitoring movements within a smart ambient and managing possible risks. In particular, we present a methodology for movement analysis aimed at detecting and preventing risks. Results from experimentations are discussed.