SATIRE: a software architecture for smart AtTIRE
Proceedings of the 4th international conference on Mobile systems, applications and services
Smart home care network using sensor fusion and distributed vision-based reasoning
Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks
Smart Homecare System for Health Tele-monitoring
ICDS '07 Proceedings of the First International Conference on the Digital Society
Classification of Posture and Movement Using a 3-axis Accelerometer
ICCIT '07 Proceedings of the 2007 International Conference on Convergence Information Technology
Robust Dynamic Human Activity Recognition Based on Relative Energy Allocation
DCOSS '08 Proceedings of the 4th IEEE international conference on Distributed Computing in Sensor Systems
Body posture identification using hidden Markov model with a wearable sensor network
BodyNets '08 Proceedings of the ICST 3rd international conference on Body area networks
TEMPO 3.1: A Body Area Sensor Network Platform for Continuous Movement Assessment
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
MobiSense: Mobile body sensor network for ambulatory monitoring
ACM Transactions on Embedded Computing Systems (TECS)
Mobile phone-based pervasive fall detection
Personal and Ubiquitous Computing
Context-aware wireless sensor networks for assisted living and residential monitoring
IEEE Network: The Magazine of Global Internetworking
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Falls are dangerous for the aged population as they result in serious detrimental consequences. Therefore, many fall detection methods have been proposed. Most of these methods characterize falls by large accelerations and fast body orientation changes. However, certain activities like sitting down quickly, vigorous gaits, and jumping, also show these characteristics, and thus are hard to distinguish from real falls. Moreover, many falls in the elderly are slow falls which show lower activity levels. Existing work fails to detect slow falls effectively because they only identify falls with high activity levels. In this paper, we present a grammar-based fall detection framework which not only better distinguishes fall-like activities from real falls, but also emphasizes the detection of slow falls. We utilize posture information extracted from on-body sensors and context information collected from sensors deployed in the house to reduce false positives. A fall in our framework is detected as a sequence of sensor events. We provide a context-free grammar to define these sequences so that the framework can be easily extended to detect more kinds of falls. Our case study shows that our method can distinguish various fall-like activities from real falls and can also effectively detect both fast falls and slow falls. The integration evaluation shows that our method achieves both high sensitivity and high specificity.