Abstract
Architecture for the fall accident detection and corresponding wide area rescue system based on a smart phone and the third generation(3G) networks. To realize the fall detection algorithm, the angles acquired by the electronic compass and the waveform sequence of the triaxial accelerometer on the smart phone are used as the system inputs. The acquired signals are then used to generate an ordered feature sequence and then examined in a
sequential manner by the proposed cascade classifier for recognition purpose. With the proposed cascaded classification architecture, the computational burden and power consumption issue on the smart phone system can be alleviated.
Keywords: Fall detection; Fast Emergency Rescue; Triaxial Accelerometer; Cascade Classifier;
References
G. Acampora, D. J. Cook, P. Rashidi, and A. V. Vasilakos, “A Survey on ambient intelligence in healthcare,”
Proc. IEEE, vol. 101, no. 12, pp. 2470 – 2494, Dec. 2013.P. Rashidi and A. Mihailidis, “A survey on ambient-assisted living tools for older adults,” IEEE J. Biomed.
Health Informat., vol. 17, no. 3, pp. 579–590, May 2013.M. Mubashir, L. Shao, and L. Seed “A survey on fall detection:Principles and approaches,” Neuro computing, vol. 100, no. 16, pp. 144–152, 2013.
T. Shany, S. J. Redmond, M. R. Narayanan, and N. H. Lovell, “Sensors- Based wearable systems for
monitoring of human movement and falls,” IEEE Sensors J., vol. 12, no. 3, pp. 658–670, Mar. 2012.B.Mirmahboub, S. Samavi,N.Karimi, and S. Shirani, “Automatic monocular system for human fall detection based on variations in silhouette area,” IEEE Trans. Biomed. Eng., vol. 60, no. 2, pp. 427–436, Feb. 2013.
M. Yu, Y. Yu, A. Rhuma, S. M. R. Naqvi, L. Wang, and J. A. Chambers, “An online one class support vector machine-based person-specific fall detection system for monitoring an elderly individual in a room environment,” IEEE J. Biomed. Health Informatics, vol. 17, no. 6, pp. 1002– 1014, Nov. 2013.
M. Yu, A. Rhuma, S. M. Naqvi, L. Wang, and J. Chambers, “A posture recognition-based fall detection system
for monitoring an elderly person in a smart home environment,” IEEE Trans. Inf. Technol. Biomed., vol. 16,
no. 6, pp. 1274–1286, Nov. 2012.E.Auvinet, F. Multon, A. SaintArnaud, J. Rousseau, and J. Meunier, “Fall detection with multiple cameras:
An occlusion-resistant method based on 3-D silhouette vertical distribution,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 2, pp. 290–300, Mar. 2011.C. Rougier, J. Meunier, A. St-Arnaud, and J. Rousseau, “Robust video surveillance for fall detection based on
human shape deformation,” IEEE Trans. Circuits Syst. Video Technol., vol. 21, no. 5, pp. 611–622, May 2011.Y. Li, K. C. Ho, and M. Popescu, “A microphone array system for automatic fall detection,” IEEE Trans. Biomed
A. Ariani, S. J. Redmond, D. Chang, and N. H. Lovell, “Simulated unobtrusive falls detection with
multiple persons,” IEEE Trans. Biomed. Eng., vol. 59, no. 11, pp. 3185–3196, Nov. 2012.M. Mercuri, P. J. Soh, G. Pandey, P. Karsmakers, G. A. E. Vandenbosch, P. Leroux, andD. Schreurs, “Analysis of
an indoor biomedical radar-based system for health monitoring,” IEEE Trans.Microw. Theory Tech., vol. 61,
no. 5, pp. 2061–2068, May 2013.H. Rimminen, J. Lindstr¨om, M. Linnavuo, and R. Sepponen, “Detection of falls among the elderly by a floor sensor using the electric near field,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 6, pp. 1475– 1476, Nov. 2010.