Pervasive Alert System for fall detection based on Mobile Phones
Falls are an everyday potential health hazards that all of us are exposed to. A fall can cause injuries or hurt people especially the elderly. Critical injuries provoked by falls are among the major causes of hospitalization in elderly persons, diminishing their quality of life and often resulting in a rapid decline in functionality or death. Rapid response can improve the patients outcome, but this is often lacking when the injured person lives alone and the nature of the injury complicates calling for help. This paper presents pervasive alert system for fall detection using common commercially available Android-based smart phone with an integrated tri-axial accelerometer. The focus of this research was developing the most successful algorithm for detecting falls and distinguishing them from non-falls. Hybrid algorithm concentrating on acceleration magnitude and angle change was developed for fall detection. We implement a prototype system on the Android phone and conduct experiments to evaluate its performances on real-world falls. Experimental results show that the system achieves strong detection performance and power efﬁciency.
Annekenny R, O'Shea D. Falls and syncope in elderly patients. Clin Geriatric Med 2002;18:xiii-xiv.
Scuffam P, Chaplin S, Legood R. Incidence and costs of unintentional falls in older people in the United Kingdom. J Epidemiol Community Health 2003;57:740-4.
Tinetti ME, Doucette J, Claus E, Marottoli R (1995) Risk factors for serious injury during falls by older persons in the community. J Am Geriatr Soc 43: 1214–1221.
Sadigh S, Reimers A, Andersson R, Laflamme L (2004) Falls and Fall-Related Injuries Among the Elderly: A Survey of Residential-Care Facilities in a Swedish Municipality. Journal of Community Health 29: 129–140.
Ryynanen OP, Kivela SL, Honkanen R, Laippala P (1992) Falls and Lying Helpless in the Elderly. Z Gerontology 25: 278–282.
Spice CL, Morotti W, George S, Dent THS, Rose J, et al. (2009) The Winchester falls project: a randomised controlled trial of secondary prevention of falls in older people. Age and Ageing 38: 33–40.
Vellas BJ, Wayne SJ, Romero LJ, Baumgartner RN, Garry PJ (1997) Fear of falling and restriction of mobility in elderly fallers. Age and Ageing 26: 189–193.
Mann R, Birks Y, Hall J, Torgerson D, Watt I (2006) Exploring the relationship between fear of falling and neuroticism: a cross-sectional study in community-dwelling women over 70. Age and Ageing 35: 143–147.
Delbaere K, Crombez G, Vanderstraeten G, Willems T, Cambier D (2004) Fear-related avoidance of activities, falls and physical frailty. A prospective community-based cohort study. Age and Ageing 33: 368–373.
American Academy of Orthopaedic Surgeons, “Don’t let a fall be your last trip: Who is at risk?,” Your Orthopaedic Connection, AAOS, July 2007.
Lin, C.-W., et al., Compressed-Domain Fall Incident Detection for Intelligent Home Surveillance. Proceedings of IEEE International Symposium on Circuits and Systems, ISCAS 2005, 2005: p. 2781-3784.
Nait-Charif, H. and S.J. McKenna, Activity Summarisation and Fall Detection in a Supportive Home Environment. Proceedings of the 17th International Conference on Pattern Recognition (ICPR04), 2004.
M.Prado, J. Reina-Tosina, and L.Roa, Distributed intelligent architecture for falling detection and physical activity analysis in the elderly. Proceedings of the Second Joint EMBS/BMES Conference, 2002: p. 1910-1911.
Zhang, T., et al., Fall Detection by Wearable Sensor and One-Class SVM Algorithm. Lecture Notes in Control and Information Science, 2006. 345: p. 858-863.
Majd Alwan, Prabhu Jude Rajendran, Steve Kell, David Mack, Siddharth Dalal, Matt Wolfe, and Robin Felder. A smart and passive ﬂoor-vibration based fall detector for elderly.
Mihail Popescu, Yun Li, Marjorie Skubic, and Marilyn Rantz. Anacoustic fall detector system that uses sound height information to reduce the false alarm rate. 30th Annual International IEEE EMBS Conference, August 2008.
Tracy Lee and Alex Mihailidis. An intelligent emergency response system: preliminary development and testing of automated fall detection. Journal of Telemedicine and Telecare, 11(4):194–198, 2005.
Shaou-Gang Miaou, Pei-Hsu Sung, and Chia-Yuan Huang. A customized human fall detection system using omni-camera images and personal information p.39–41. Proceedings of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference, April 2006.
Caroline Rougier and Jean Meunier. Fall detection using 3d head trajectory extracted from a single camera video sequence. The First International Workshop on Video Processing for Security June 7-9, 2006 Quebec City, Canada
Hammadi Nait-Charif and Stephen J. McKenna. Activity summarisation and fall detection in a supportive home environment. 2004.
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(1):150–154, 2000.
Thomas Riisgaard Hansen, J. Mikael Eklund, Jonthan Sprinkle, Ruzena Bajcsy, and Shankar Sastry. Using smart sensors and a camera phone to detect and verify the fall of elderly persons. European Medicine, Biology and Engineering Conference (EMBEC 2005), November 2005.
G Williams, K Doughty, K Cameron, and D.A. Bradley. A smart fall and activity monitor for telecare applications, volume 30, pages 1151–1154. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 1998.