Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 369-372

  Tolkiehn, Marie ^a   Atallah, Louis ^a   Lo, Benny ^a   Yang, Guang Zhong ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

BAROMETRIC PRESSURE; DIRECTION SENSITIVE; EFFICIENT ANALYSIS; FALL DETECTION; LABORATORY ANALYSIS; LIVING ALONE; LOW POWER; RECOVERY TIME; TRIAXIAL ACCELEROMETER; WEARABLE SENSORS;

ATMOSPHERIC PRESSURE; BAROMETERS; FEATURE EXTRACTION; PRESSURE SENSORS;

ACCELEROMETERS;

ACCELERATION; ACTIMETRY; AMBULATORY MONITORING; ARTICLE; EQUIPMENT; EQUIPMENT DESIGN; FALLING; HUMAN; INSTRUMENTATION; MOVEMENT (PHYSIOLOGY); PHYSIOLOGY; PRESSURE TRANSDUCER; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; EQUIPMENT FAILURE;

ACCELERATION; ACCIDENTAL FALLS; ACTIGRAPHY; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; HUMANS; MONITORING, AMBULATORY; MOVEMENT; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TRANSDUCERS, PRESSURE;

EID: 84861949867     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6090120     Document Type: Conference Paper

References (23)

1. H. Sjögren and U. Björnstig. Unintentional injuries among elderly people: Incidence, causes, severity, and costs. Accident Analysis & Prevention, 21(3):233-242, 6 1989.
2. World Health. Organization and Ebooks Corporation. The injury chart book : A graphical overview of the global burden of injuries., 2002.
3. K. Doughty, R. Lewis, and A. McIntosh. The design of a practical and reliable fall detector for community and institutional telecare. Journal of Telemedicine and Telecare, 6(Supplement 1):150, 2000.
4. D.A. Sterling, J.A. O'Connor, and J. Bonadies. Geriatric falls: injury severity is high and disproportionate to mechanism. 50(1):116-119, 2001.
5. B.J. Vellas, S.J. Wayne, L.J. Romero, R.N. Baumgartner, and P.J. Garry. Fear of falling and restriction of mobility in elderly fallers. 26(3):189-193, 1997.
6. D Wild, U S Nayak, and B Isaacs. How dangerous are falls in old people at home?
7. C. Wong R. C. King, L. Atallah, F. Miskelly, and G.-Z. Yang. Elderly risk assessment of falls with bsn. Wearable and Implantable Body Sensor Networks, International Workshop on, 0:30-35, 2010.
8. S. Flores E. Mitty. Fall prevention in assisted living: Assessment and strategies. Geriatric Nursing, 28(6):349-357, 2007.
9. R. Jan Gurley, Nancy Lum, Merle Sande, Bernard Lo, and Mitchell H. Katz. Persons found in their homes helpless or dead. New England Journal of Medicine, 334(26):1710-1716, 1996.
10. A. Mihailidis T. Lee. An intelligent emergency response system: preliminary development and testing of automated fall detection. J Telemed Telecare, 11(4):194-198, 2005.
11. M. Skubic M. Popescu, Y. Li and M. Rantz. An acoustic fall detector system that uses sound height information to reduce the false alarm rate. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE, pages 4628-4631, 2008.
12. A.K. Bourke, J.V. O'Brien, and G.M. Lyons. Evaluation of a thresholdbased tri-axial accelerometer fall detection algorithm. Gait & Posture, 26(2):194-199, 2007.
13. A.K. Bourke and G.M. Lyons. A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Medical Engineering & Physics, 30(1):84-90, 2008.
14. Eelis Cederström and Ilkka Korhonen. A device for indicating a fall. Patent EP1642248, IST International Security Technology OY (Melkonkatu 16 A, 00210 Helsinki, FI), 2007.
15. M. J. Mathie, J. Basilakis, and B. G. Celler. A system for monitoring posture and physical activity using accelerometers. Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, 4:3654-3657 vol.4, 2001.
16. U. Lindemann, A. Hock, M. Stuber, W. Keck, and C. Becker. Evaluation of a fall detector based on accelerometers: A pilot study. Medical and Biological Engineering and Computing, 43:548-551, 2005. 10.1007/BF02351026.
17. M. Kangas, A. Konttila, I. Winblad, and T. Jamsa. Determination of simple thresholds for accelerometry-based parameters for fall detection. Engineering in Medicine and Biology Society, 2007. EMBS 2007. 29th Annual International Conference of the IEEE, pages 1367-1370, 22-26 Aug. 2007.
18. M. A. Hanson A. T. Barth J. Lach Q. Li, J. A. Stankovic and G. Zhou. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. Wearable and Implantable Body Sensor Networks, International Workshop on, 0:138-143, 2009.
19. Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, and Dong Xuan. Perfalld: A pervasive fall detection system using mobile phones. Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference on, pages 292-297, March 29 2010-April 2 2010.
20. F. Bianchi, S. J. Redmond, M. R. Narayanan, S. Cerutti, B. G. Celler, and N. H. Lovell. Falls event detection using triaxial accelerometry and barometric pressure measurement. Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, pages 6111-6114, 3-6 Sept. 2009.
21. R. Salomon, M. Luder, and G. Bieber. iFall-a new embedded system for the detection of unexpected falls. In Pervasive Computing and Communications Workshops (PERCOM Workshops), 2010 8th IEEE International Conference on, pages 286-291. IEEE, 2010.
22. G.-Z. Yang. Body Sensor Networks. Springer-Verlag, 2006.
23. S. Thiemjarus. A device-orientation independent method for activity recognition. Body Sensor Networks (BSN), 2010 International Conference on, pages 19-23, 7-9 June 2010.