Accurate, fast fall detection using posture and context information

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Accurate, fast fall detection using posture and context information

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Conference On Embedded Networked Sensor Systems archive
Proceedings of the 6th ACM conference on Embedded network sensor systems table of contents

Raleigh, NC, USA

POSTER SESSION: Posters table of contents

Pages 443-444

Year of Publication: 2008

ISBN:978-1-59593-990-6

Authors

Qiang Li	University of Virginia, Charlottesville, USA
Gang Zhou	College of William and Mary, Williamsburg, USA
John A. Stankovic	University of Virginia, Charlottesville, USA

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGOPS: ACM Special Interest Group on Operating Systems
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems

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ACM New York, NY, USA

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ABSTRACT

Traditional fall detection is only based on acceleration analysis. In this work we present a novel fall detection method that also utilizes posture and context information. This information can help reduce both false positives and negatives. Our solution also strives for low computational cost and fast response.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	http://cricket.csail.mit.edu/.
	2	J. Chen, K. Kwong, D. Chang, J. Luk, and R. Bajcsy. Wearable sensors for reliable fall detection. In Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pages 3551--3554, Shanghai, China, Sept 2005. IEEE.
	3	Raghu K. Ganti , Praveen Jayachandran , Tarek F. Abdelzaher , John A. Stankovic, SATIRE: a software architecture for smart AtTIRE, Proceedings of the 4th international conference on Mobile systems, applications and services, June 19-22, 2006, Uppsala, Sweden [doi>10.1145/1134680.1134693]
	4	N. Noury. A smart sensor for the remote follow up of activity and falldetection of the elderly. In Proceedings of 2nd Annual International IEEE-EMB Special Topic Conference on Microtechnologies in Medicine and Biology, pages 314--317, 2002.
	5	A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, and J. Stankovic. ALARM-NET: Wireless sensor networks for assisted-living and residential monitoring. Technical report, Department of Computer Science, University of Virginia, 2007.