A smart capacitive measurement system for fall detection

Abstract In this paper we present a new capacitive measurement system dedicated to fall detection of elderly people. The system is formed by a set of parallel wires integrated in the floor. To detect human presence and posture, the measurements consist of both self and mutual capacitances. The capacitive sensors are robust enough to be integrated directly in the self leveling compound or even underneath components. This fact makes easier maintenance operations on the floor, such as a change of floor covering. All the electronic devices involved in the measurement system are located out of the floor. Thus, the change of any flawed electrical components is then convenient since there is no need to remove the flooring to maintain the system. After a detailed description of the measurement system proposed, typical measurements obtained with two experimental systems are presented: an instrumented room and a transportable demonstrator. Three basic data separation methods were implemented to separate Falls and Non-Falls situations. For both systems, the results obtained show promising validation rates up to 90%.

[1]  B. Isaacs,et al.  How dangerous are falls in old people at home? , 1981, British medical journal.

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

[3]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[4]  Maurice Beck,et al.  Design of sensor electronics for electrical capacitance tomography , 1992 .

[5]  Xinguo Yu.  Approaches and principles of fall detection for elderly and patient , 2008, HealthCom 2008 - 10th International Conference on e-health Networking, Applications and Services.

[6]  Jean Meunier,et al.  Robust Video Surveillance for Fall Detection Based on Human Shape Deformation , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[7]  Jukka Vanhala,et al.  TileTrack: Capacitive human tracking using floor tiles , 2009, 2009 IEEE International Conference on Pervasive Computing and Communications.

[8]  Liang Liu,et al.  Doppler radar sensor positioning in a fall detection system , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[9]  C. Becker,et al.  Evaluation of a fall detector based on accelerometers: A pilot study , 2005, Medical and Biological Engineering and Computing.

[10]  Huan-Wen Tzeng,et al.  Design of fall detection system with floor pressure and infrared image , 2010, 2010 International Conference on System Science and Engineering.

[11]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, ICPR 2004.

[12]  M. Alwan,et al.  A Smart and Passive Floor-Vibration Based Fall Detector for Elderly , 2006, 2006 2nd International Conference on Information & Communication Technologies.

[13]  R. Henry,et al.  Human tracking using near field imaging , 2008, 2008 Second International Conference on Pervasive Computing Technologies for Healthcare.

[14]  Henry Rimminen,et al.  Detection of human movement by near field imaging / : development of a novel method and applications , 2011 .

[15]  O. Ciftja.  Coulomb self-energy and electrostatic potential of a uniformly charged square in two dimensions , 2010 .

[16]  Jukka Vanhala,et al.  Capacitive indoor positioning and contact sensing for activity recognition in smart homes , 2012, J. Ambient Intell. Smart Environ..

[17]  Joshua R. Smith,et al.  Electric field imaging , 1999 .

[18]  Mark Hawley,et al.  Fall detectors: Do they work or reduce the fear of falling? , 2004 .

[19]  Aaron D. Mazzeo,et al.  Paper‐Based, Capacitive Touch Pads , 2012, Advanced materials.

[20]  Axel Steinhage,et al.  Monitoring Movement Behavior by Means of a Large Area Proximity Sensor Array in the Floor , 2008, BMI.

[21]  Joseph A. Paradiso,et al.  Musical Applications of Electric Field Sensing , 1997 .

[22]  Gang Zhou,et al.  Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information , 2009, 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks.

[23]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[24]  Yeh-Liang Hsu,et al.  A Review of Accelerometry-Based Wearable Motion Detectors for Physical Activity Monitoring , 2010, Sensors.

[25]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[26]  Wolfgang Straßer,et al.  Smart Camera Based Monitoring System and Its Application to Assisted Living , 2008, Proceedings of the IEEE.

[27]  N. Jonassen,et al.  Human body capacitance: static or dynamic concept? [ESD] , 1998, Electrical Overstress/ Electrostatic Discharge Symposium Proceedings. 1998 (Cat. No.98TH8347).

[28]  C. Ricard,et al.  Plusieurs centaines de milliers de chutes chez les personnes âgées chaque année en France , 2008 .

[29]  Stéphane Holé,et al.  Simple and direct calculation of capacitive sensor sensitivity map , 2008 .

[30]  Neil Johnson,et al.  A smart sensor to detect the falls of the elderly , 2004, IEEE Pervasive Computing.

[31]  O. Ciftja.  Stored Coulomb Self-Energy of a Uniformly Charged Rectangular Plate , 2016 .

[32]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[33]  Israel Gannot,et al.  Fall detection of elderly through floor vibrations and sound , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Stéphane Holé,et al.  Analytical capacitive sensor sensitivity distribution and applications , 2006 .