Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
A multifrequency MAC specially designed for wireless sensor network applications
ACM Transactions on Embedded Computing Systems (TECS)
Multi-modal fall detection within the WeCare framework
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Longitudinal high-fidelity gait analysis with wireless inertial body sensors
WH '10 Wireless Health 2010
Context-aware fall detection using a Bayesian network
CASEMANS '11 Proceedings of the 5th ACM International Workshop on Context-Awareness for Self-Managing Systems
Grammar-based, posture- and context-cognitive detection for falls with different activity levels
Proceedings of the 2nd Conference on Wireless Health
An Environmental-Adaptive Fall Detection System on Mobile Device
Journal of Medical Systems
MobiCon: a mobile context-monitoring platform
Communications of the ACM
Design, implementation and experimental evaluation of a wireless fall detector
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Sensor-based e-healthcare in a next generation convergence home network
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Fuzzy inference-based reliable fall detection using kinect and accelerometer
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
A smartphone-based fall detection system
Pervasive and Mobile Computing
Fall-detection simulator for accelerometers with in-hardware preprocessing
Proceedings of the 5th International Conference on PErvasive Technologies Related to Assistive Environments
Coordinating the web of services for a smart home
ACM Transactions on the Web (TWEB)
A data analysis driven streaming framework for body sensor area networks
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
Reliable and secure body fall detection algorithm in a wireless mesh network
BodyNets '13 Proceedings of the 8th International Conference on Body Area Networks
A multi-sensor approach for fall risk prediction and prevention in elderly
ACM SIGAPP Applied Computing Review
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Falls are dangerous for the aged population as they can adversely affect health. Therefore, many fall detection systems have been developed. However, prevalent methods only use accelerometers to isolate falls from activities of daily living (ADL). This makes it difficult to distinguish real falls from certain fall-like activities such as sitting down quickly and jumping, resulting in many false positives. Body orientation is also used as a means of detecting falls, but it is not very useful when the ending position is not horizontal, e.g. falls happen on stairs. In this paper we present a novel fall detection system using both accelerometers and gyroscopes. We divide human activities into two categories: static postures and dynamic transitions. By using two tri-axial accelerometers at separate body locations, our system can recognize four kinds of static postures: standing, bending, sitting, and lying. Motions between these static postures are considered as dynamic transitions. Linear acceleration and angular velocity are measured to determine whether motion transitions are intentional. If the transition before a lying posture is not intentional, a fall event is detected. Our algorithm, coupled with accelerometers and gyroscopes, reduces both false positives and false negatives, while improving fall detection accuracy. In addition, our solution features low computational cost and real-time response.