Voltage compensation based calibration measurement of 3D-acceleration transducer in fall detection system for the elderly

The fall detection algorithm, which can recognize the fall of human body by collecting the acceleration signals in different directions of the body, is an important part of fall detection system for the elderly. The system, however, may have errors during analyzing the acceleration signal, due to that the coordinate system of the transducer does not coincide with the one of human motion. Furthermore, voltage variation of the battery also influences the accuracy of the acceleration signal. Therefore, in this paper, a fall detection system based on the 3D-acceleration transducer MMA7260 is designed, which can calibrate the acceleration data through compensation of voltage and transformation of coordinates. Experiments illustrated that the proposed method can accurately transform the collected data from the coordinate system of the transducer to that of the human motion, and can recognize various postural changes in the course of the motion of human body.

[1]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.

[2]  F. Shaw Falls in Older People With Dementia , 2003 .

[3]  Guang-Zhong Yang,et al.  Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  A K Bourke,et al.  Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. , 2007, Gait & posture.

[5]  Reinhold Haux,et al.  Automatic self-calibration of body worn triaxial-accelerometers for application in healthcare , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[6]  Ilias Maglogiannis,et al.  Patient Fall Detection using Support Vector Machines , 2007, AIAI.

[7]  M. Vela,et al.  Automatic Detection of Health Emergency States at Home , 2014 .

[8]  Alex Mihailidis,et al.  An intelligent emergency response system: preliminary development and testing of automated fall detection , 2005, Journal of telemedicine and telecare.

[9]  Paul Lukowicz,et al.  Automatic Calibration of Body Worn Acceleration Sensors , 2004, Pervasive.

[10]  Tong Zhang,et al.  Fall Detection by Wearable Sensor and One-Class SVM Algorithm , 2006 .

[11]  Paulo Salgado,et al.  Fall body detection algorithm based on tri-accelerometer sensors , 2013, 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics (CINTI).

[12]  Reinhold Haux,et al.  Automatic self-calibration of body worn triaxial-accelerometers for application in healthcare , 2008, Pervasive 2008.

[13]  Jiandong Wang,et al.  Design and Calibration for a Smart Inertial Measurement Unit for Autonomous Helicopters Using MEMS Sensors , 2006, 2006 International Conference on Mechatronics and Automation.

[14]  G. Demiris,et al.  Fall Detection Devices and Their Use With Older Adults: A Systematic Review , 2014, Journal of geriatric physical therapy.