Chameleon: personalised and adaptive fall detection of elderly people in home-based environments

Threshold-based fall detection has been widely adopted in conventional fall detection systems. In this paper, we argue that a fixed threshold is not flexible enough for different people. By exploiting the personalised and adaptive threshold, we propose a novel threshold extraction model, which meets being adaptive to detect a fall, while only taking consideration of data from activity of daily living ADL. We believe this is a solid step toward improving the performance of the threshold-based fall detection solution. Furthermore, we incorporate the proposed idea into Chameleon. To evaluate the performance of this threshold extraction model, we compared Chameleon with advanced magnitude detection AMD and fixed and tracking fall detection FTFD. The results show Chameleon has an accuracy of 96.83% when detecting falls, which is 1.67% higher than FTFD and 2.67% higher than AMD. Meanwhile, the sensitivity and the specificity of Chameleon are also higher than the other two algorithms.

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

[2]  Franck Multon,et al.  Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution , 2011, IEEE Transactions on Information Technology in Biomedicine.

[3]  Chin-Feng Lai,et al.  Detection of Cognitive Injured Body Region Using Multiple Triaxial Accelerometers for Elderly Falling , 2011, IEEE Sensors Journal.

[4]  E. Alasaarela,et al.  A two-threshold fall detection algorithm for reducing false alarms , 2012, 2012 6th International Symposium on Medical Information and Communication Technology (ISMICT).

[5]  Marjorie Skubic,et al.  An acoustic fall detector system that uses sound height information to reduce the false alarm rate , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Allen R. Hanson,et al.  Aging in place: fall detection and localization in a distributed smart camera network , 2007, ACM Multimedia.

[7]  Edouard Auvinet,et al.  Head detection using Kinect camera and its application to fall detection , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[8]  Weisong Shi,et al.  Low-power fall detection in home-based environments , 2012, MobileHealth '12.

[9]  A M Dellinger,et al.  Motor vehicle and fall related deaths among older Americans 1990–98: sex, race, and ethnic disparities , 2002, Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention.

[10]  Ding Liang,et al.  Pre-impact & impact detection of falls using wireless Body Sensor Network , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[11]  Jiewen Zheng,et al.  Design of Automatic Fall Detector for Elderly Based on Triaxial Accelerometer , 2009, 2009 3rd International Conference on Bioinformatics and Biomedical Engineering.

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

[13]  Jong-Hoon Youn,et al.  Survey and evaluation of real-time fall detection approaches , 2009, 2009 6th International Symposium on High Capacity Optical Networks and Enabling Technologies (HONET).

[14]  Mohan Karunanithi,et al.  Simulated fall detection via accelerometers , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Nigel H. Lovell,et al.  Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  Wan Young Chung,et al.  Activity monitoring from real-time triaxial accelerometer data using sensor network , 2007, 2007 International Conference on Control, Automation and Systems.

[17]  Mitja Lustrek,et al.  Fall Detection and Activity Recognition with Machine Learning , 2009, Informatica.

[18]  K. Samsudin,et al.  Evaluation of fall detection classification approaches , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

[19]  Marjorie Skubic,et al.  VAMPIR- an automatic fall detection system using a vertical PIR sensor array , 2012, 2012 6th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[20]  Martin Däumer,et al.  A new method to estimate the real upper limit of the false alarm rate in a 3 accelerometry-based fall detector for the elderly , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[21]  Garrett R. Brown,et al.  An Accelerometer Based Fall Detector : Development , Experimentation , and Analysis , 2005 .

[22]  Suhuai Luo,et al.  A dynamic motion pattern analysis approach to fall detection , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

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

[24]  M. Kangas,et al.  Determination of simple thresholds for accelerometry-based parameters for fall detection , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[25]  P. Phukpattaranont,et al.  Improving the accuracy of a fall detection algorithm using free fall characteristics , 2010, ECTI-CON2010: The 2010 ECTI International Confernce on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[26]  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.

[27]  Thomas M. Gatton,et al.  Fuzzy Logic Decision Making for an Intelligent Home Healthcare System , 2010, 2010 5th International Conference on Future Information Technology.

[28]  Jong Hyuk Park,et al.  Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling Detection , 2010 .

[29]  Marcia A Ciol,et al.  Falls in the Medicare Population: Incidence, Associated Factors, and Impact on Health Care , 2009, Physical Therapy.

[30]  Alessio Vecchio,et al.  Recognition of false alarms in fall detection systems , 2011, 2011 IEEE Consumer Communications and Networking Conference (CCNC).

[31]  Yeh-Liang Hsu,et al.  Algorithm Design forReal-time Physical Activity Identification withAccel erometry Measurement , 2007 .

[32]  Xia Wang,et al.  Fall Detection on Mobile Phones Using Features from a Five-Phase Model , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[33]  Ruzena Bajcsy,et al.  USING SMART SENSORS AND A CAMERA PHONE TO DETECT AND VERIFY THE FALL OF ELDERLY PERSONS , 2005 .

[34]  Jong Hyuk Park,et al.  Adaptive Body Posture Analysis for Elderly-Falling Detection with Multisensors , 2010, IEEE Intelligent Systems.

[35]  Yunjian Ge,et al.  HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer , 2013, IEEE Sensors Journal.

[36]  Maarit Kangas,et al.  Comparison of low-complexity fall detection algorithms for body attached accelerometers. , 2008, Gait & posture.

[37]  H.C. Kim,et al.  Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[38]  Jae-Young Pyun,et al.  Real life applicable fall detection system based on wireless body area network , 2013, 2013 IEEE 10th Consumer Communications and Networking Conference (CCNC).

[39]  Jeffrey M. Hausdorff,et al.  Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls , 2012, PloS one.

[40]  Bin Huang,et al.  A method for fast fall detection , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[41]  R. Bajcsy,et al.  Wearable Sensors for Reliable Fall Detection , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[42]  Alan K. Bourke,et al.  An optimum accelerometer configuration and simple algorithm for accurately detecting falls , 2006 .

[43]  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.