Automatic fall detection by a wearable embedded smart camera

About one-third of adults in the U.S. aged 65 or older fall every year, with 20% of the reported fall cases needing prompt medical attention. The methods that have been proposed for fall detection in recent years present trade-offs between level of intrusiveness, coverage area, processing power requirements and detection accuracy. We present a robust and resource-efficient method for fall detection by using a wearable embedded smart camera, which is a small, battery-operated unit. The proposed approach employs histograms of edge orientations as well as edge strength values, and analyzes their correlation. Moreover, we adaptively determine the cells that do not contribute to overall edge information, and remove them autonomously. Since the camera is worn by the subject, monitoring can continue wherever the subject may go including outdoors. The captured frames are not the images of the subject, and this alleviates the privacy concerns. The alert and an image of the surroundings can be transmitted wirelessly, only when a fall event is detected, for easier localization of the subject by emergency response teams. The experimental results obtained with over 300 trials are very promising with a 91% detection rate for falls.

[1]  R. Cumming,et al.  Interventions for preventing falls in elderly people. , 2003, The Cochrane database of systematic reviews.

[2]  Melonie P. Heron,et al.  Deaths: leading causes for 2007. , 2011, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[3]  Frank Sposaro,et al.  iFall: An android application for fall monitoring and response , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Martin Kampel,et al.  Detecting falls at homes using a network of low-resolution cameras , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[5]  L. Gonzo,et al.  A networked multisensor system for ambient assisted living application , 2009, 2009 3rd International Workshop on Advances in sensors and Interfaces.

[6]  Li Chen,et al.  A wearable real-time fall detector based on Naive Bayes classifier , 2010, CCECE 2010.

[7]  Eugenio Culurciello,et al.  An Address-Event Fall Detector for Assisted Living Applications , 2008, IEEE Transactions on Biomedical Circuits and Systems.

[8]  Israel Gannot,et al.  A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls , 2009, IEEE Transactions on Biomedical Engineering.

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

[10]  Stefano Cagnoni,et al.  Sensor Fusion-Oriented Fall Detection for Assistive Technologies Applications , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[11]  Paul Panek,et al.  Fall detection with distributed floor-mounted accelerometers: An overview of the development and evaluation of a fall detection system within the project eHome , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[12]  Maria L. Rizzo,et al.  Measuring and testing dependence by correlation of distances , 2007, 0803.4101.

[13]  N. Noury,et al.  Preliminary investigation into the use of Autonomous Fall Detectors , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[14]  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.

[15]  Heinz Jäckel,et al.  SPEEDY:a fall detector in a wrist watch , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[16]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[18]  Melonie P. Heron,et al.  Deaths: leading causes for 2004. , 2007, National vital statistics reports : from the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System.

[19]  Larry Lundy,et al.  Fall risk, vestibular schwannoma, and anticoagulation therapy. , 2008, Journal of the American Academy of Audiology.

[20]  T. Tamura.  Wearable accelerometer in clinical use , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[21]  Neil Pendleton,et al.  Predictors of incident depression after hip fracture surgery. , 2007, The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry.