Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.

[1]  T. Tamura,et al.  A preliminary study to demonstrate the use of an air bag device to prevent fall-related injuries , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Yagiz Onat Yazir,et al.  Tradeoffs in cross platform solutions for mobile assistive technology , 2013, 2013 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PACRIM).

[3]  Jong Hyuk Park,et al.  An Environmental-Adaptive Fall Detection System on Mobile Device , 2011, Journal of Medical Systems.

[4]  Sheikh Iqbal Ahamed,et al.  iPrevention: towards a novel real-time smartphone-based fall prevention system , 2013, SAC '13.

[5]  Dong Xuan,et al.  Mobile phone-based pervasive fall detection , 2010, Personal and Ubiquitous Computing.

[6]  Lale Akarun,et al.  A Smartphone Based Fall Detector with Online Location Support , 2010 .

[7]  Yingli Tian,et al.  Privacy Preserving Automatic Fall Detection for Elderly Using RGBD Cameras , 2012, ICCHP.

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

[9]  Ali-young Jeon,et al.  Emergency Detection System Using PDA Based on Self-Response Algorithm , 2007, 2007 International Conference on Convergence Information Technology (ICCIT 2007).

[10]  Tong Zhang,et al.  Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm , 2006 .

[11]  Yibin Hou,et al.  Triaxial accelerometer-based real time fall event detection , 2012, International Conference on Information Society (i-Society 2012).

[12]  Hanseok Ko,et al.  Acoustic and visual signal based context awareness system for mobile application , 2011, IEEE Transactions on Consumer Electronics.

[13]  Shuai Tao,et al.  Privacy-Preserved Behavior Analysis and Fall Detection by an Infrared Ceiling Sensor Network , 2012, Sensors.

[14]  Juan Manuel Moreno,et al.  FATE: One step towards an automatic aging people fall detection service , 2013, Proceedings of the 20th International Conference Mixed Design of Integrated Circuits and Systems - MIXDES 2013.

[15]  M. Kaenampornpan,et al.  Fall detection prototype for Thai elderly in mobile computing era , 2011, The 8th Electrical Engineering/ Electronics, Computer, Telecommunications and Information Technology (ECTI) Association of Thailand - Conference 2011.

[16]  Ying-Wen Bai,et al.  Recognition of direction of fall by smartphone , 2013, 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE).

[17]  Felix Büsching,et al.  DroidCluster: Towards Smartphone Cluster Computing -- The Streets are Paved with Potential Computer Clusters , 2012, 2012 32nd International Conference on Distributed Computing Systems Workshops.

[18]  Shuwan Xue,et al.  Portable Preimpact Fall Detector With Inertial Sensors , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Younghoon Kim,et al.  A Simple Falling Recognition Scheme for a Human Body by Using Mobile Devices , 2013 .

[20]  Yasuhisa Hirata,et al.  Analysis of the slip-related falls and fall prevention with an intelligent shoe system , 2010, 2010 3rd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

[21]  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.

[22]  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.

[23]  A McIntosh,et al.  The design of a practical and reliable fall detector for community and institutional telecare , 2000, Journal of telemedicine and telecare.

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

[25]  Ye Li,et al.  Physical Activity Recognition Utilizing the Built-In Kinematic Sensors of a Smartphone , 2013, Int. J. Distributed Sens. Networks.

[26]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[27]  Binod Vaidya,et al.  SensorFall - An Accelerometer Based Mobile Application , 2009, 2009 2nd International Conference on Computer Science and its Applications.

[28]  Begoña García Zapirain,et al.  Mobile communication for intellectually challenged people: a proposed set of requirements for interface design on touch screen devices , 2012, Communications in Mobile Computing.

[29]  C. Todd,et al.  World Health Organisation Global Report on Falls Prevention in Older Age , 2007 .

[30]  M. Tinetti,et al.  Risk factors for falls among elderly persons living in the community. , 1988, The New England journal of medicine.

[31]  Young-Koo Lee,et al.  Comprehensive Context Recognizer Based on Multimodal Sensors in a Smartphone , 2012, Sensors.

[32]  Hamit Erdem,et al.  A multi-channel remote controller for home and office appliances , 2009, IEEE Transactions on Consumer Electronics.

[33]  B. Hjorth EEG analysis based on time domain properties. , 1970, Electroencephalography and clinical neurophysiology.

[34]  Cem Ersoy,et al.  A Review and Taxonomy of Activity Recognition on Mobile Phones , 2013 .

[35]  Juan-Luis Gorricho,et al.  Surveillance with Alert Management System Using Conventional Cell Phones , 2010, 2010 Fifth International Multi-conference on Computing in the Global Information Technology.

[36]  G. Miller,et al.  Science Perspectives on Psychological the Smartphone Psychology Manifesto on Behalf Of: Association for Psychological Science the Smartphone Psychology Manifesto Previous Research Using Mobile Electronic Devices What Smartphones Can Do Now and Will Be Able to Do in the near Future , 2022 .

[37]  Hartmut König,et al.  Location-independent fall detection with smartphone , 2013, PETRA '13.

[38]  Andrew Boehner A Smartphone Application for a Portable Fall Detection System , 2013 .

[39]  Raymond Y. W. Lee,et al.  Detection of falls using accelerometers and mobile phone technology. , 2011, Age and ageing.

[40]  E. Thammasat,et al.  A simply fall-detection algorithm using accelerometers on a smartphone , 2012, The 5th 2012 Biomedical Engineering International Conference.

[41]  A. K. Singh,et al.  Using Android platform to detect free fall , 2013, 2013 International Conference on Information Systems and Computer Networks.

[42]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[43]  Davide Carneiro,et al.  A multi-modal approach for activity classification and fall detection , 2014, Int. J. Syst. Sci..

[44]  S. Cerutti,et al.  Falls event detection using triaxial accelerometry and barometric pressure measurement , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[45]  Deborah Estrin,et al.  Diversity in smartphone usage , 2010, MobiSys '10.

[46]  P. Laippala,et al.  Falls and lying helpless in the elderly. , 1992, Zeitschrift fur Gerontologie.

[47]  Mihail Popescu,et al.  Acoustic fall detection using a circular microphone array , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[48]  Maria Virvou,et al.  Intelligent Mobile Multimedia Application for the Support of the Elderly , 2012, 2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing.

[49]  Raveendra Hegde,et al.  Technical Advances in Fall Detection System – A Review , 2013 .

[50]  R. K. Megalingam,et al.  HOPE: An electronic gadget for home-bound patients and elders , 2012, 2012 Annual IEEE India Conference (INDICON).

[51]  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.

[52]  J.R. Casar,et al.  Context-aware services for ambient assisted living: A case-study , 2008, 2008 First International Symposium on Applied Sciences on Biomedical and Communication Technologies.

[53]  Dong Xuan,et al.  PerFallD: A pervasive fall detection system using mobile phones , 2010, 2010 8th IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[54]  Sheikh Iqbal Ahamed,et al.  smartPrediction: a real-time smartphone-based fall risk prediction and prevention system , 2013, RACS.

[55]  Wan-Young Chung,et al.  Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications , 2012, Sensors.

[56]  Gary M. Weiss,et al.  Applications of mobile activity recognition , 2012, UbiComp.

[57]  Keith Hill,et al.  Design-related bias in hospital fall risk screening tool predictive accuracy evaluations: systematic review and meta-analysis. , 2007, The journals of gerontology. Series A, Biological sciences and medical sciences.

[58]  Joaquim Gabriel,et al.  Active assistance for senior healthcare: A wearable system for fall detection , 2013, 2013 8th Iberian Conference on Information Systems and Technologies (CISTI).

[59]  Matthias Gietzelt,et al.  Fall detection on the road , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[60]  A. Oguz KANSIZ,et al.  Selection of Time-Domain Features for Fall Detection Based on Supervised Learning , .

[61]  Gueesang Lee,et al.  Fall Detection Based on Movement and Smart Phone Technology , 2012, 2012 IEEE RIVF International Conference on Computing & Communication Technologies, Research, Innovation, and Vision for the Future.

[62]  Amy Loutfi,et al.  Evaluation of the android-based fall detection system with physiological data monitoring , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[63]  Joel J. P. C. Rodrigues,et al.  Towards an autonomous fall detection and alerting system on a mobile and pervasive environment , 2011, Telecommunication Systems.

[64]  Siv Sadigh,et al.  Falls and Fall-Related Injuries Among the Elderly: A Survey of Residential-Care Facilities in a Swedish Municipality , 2004, Journal of Community Health.

[65]  Dimitrios Makris,et al.  Fall detection system using Kinect’s infrared sensor , 2014, Journal of Real-Time Image Processing.

[66]  S. Brownsell,et al.  Do community alarm users want telecare? , 2000, Journal of telemedicine and telecare.

[67]  Ying-Wen Bai,et al.  Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone , 2012, 2012 IEEE 16th International Symposium on Consumer Electronics.

[68]  Martin J.-D. Otis,et al.  Toward an augmented shoe for preventing falls related to physical conditions of the soil , 2012, 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[69]  A. Bourke,et al.  Fall detection - Principles and Methods , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[70]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[71]  Bingchuan Yuan Non-intrusive Movement Detection in CARA Pervasive Healthcare Application , 2011 .

[72]  Oh-young Kwon,et al.  Design of U-Health System with the Use of Smart Phone and Sensor Network , 2010, 2010 Proceedings of the 5th International Conference on Ubiquitous Information Technologies and Applications.

[73]  Jeffrey M. Hausdorff,et al.  Comparison of acceleration signals of simulated and real-world backward falls. , 2011, Medical engineering & physics.

[74]  Stefan Madansingh,et al.  Smartphone based fall detection system , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[75]  Philip Heng Wai Leong,et al.  Development of a Human Airbag System for Fall Protection Using MEMS Motion Sensing Technology , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[76]  Konrad Paul Kording,et al.  Fall Classification by Machine Learning Using Mobile Phones , 2012, PloS one.

[77]  Kazuhiro Kosuge,et al.  Motion control of intelligent passive-type Walker for fall-prevention function based on estimation of user state , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[78]  AGEinG And LifE CoursE , fAmiLy And Community HEALtH WHo Global report on falls Prevention in older Age , .

[79]  Arthur D. Fisk,et al.  Designing for Older Adults: Principles and Creative Human Factors Approaches , 2004 .

[80]  Jer-Vui Lee,et al.  Smart Elderly Home Monitoring System with an Android Phone , 2013 .

[81]  J. Painter,et al.  Living Alone and Fall Risk Factors in Community-Dwelling Middle Age and Older Adults , 2009, Journal of community health.

[82]  Ye Li,et al.  Falling-Incident Detection and Alarm by Smartphone with Multimedia Messaging Service (MMS) , 2012 .

[83]  Yujiu Yang,et al.  E-FallD: A fall detection system using android-based smartphone , 2012, 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery.

[84]  Alessio Vecchio,et al.  A smartphone-based fall detection system , 2012, Pervasive Mob. Comput..

[85]  John G. Cleary,et al.  K*: An Instance-based Learner Using and Entropic Distance Measure , 1995, ICML.

[86]  Shih-Hau Fang,et al.  Developing a mobile phone-based fall detection system on Android platform , 2012, 2012 Computing, Communications and Applications Conference.

[87]  Sergios Theodoridis,et al.  Introduction to Pattern Recognition: A Matlab Approach , 2010 .

[88]  Shuangquan Wang,et al.  FallAlarm: Smart Phone Based Fall Detecting and Positioning System , 2012, ANT/MobiWIS.

[89]  Deokjai Choi,et al.  Semi-supervised fall detection algorithm using fall indicators in smartphone , 2012, ICUIMC '12.

[90]  Toshiyo Tamura,et al.  A Wearable Airbag to Prevent Fall Injuries , 2009, IEEE Transactions on Information Technology in Biomedicine.

[91]  Andrew Charlesworth The ascent of smartphone , 2009 .

[92]  Bernd Schulze,et al.  Concept and Design of a Video Monitoring System for Activity Recognition and Fall Detection , 2009, ICOST.

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

[94]  Kazuhiro Kosuge,et al.  Fall prevention control of passive intelligent walker based on human model , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[95]  Ye Li,et al.  Fall detection by built-in tri-accelerometer of smartphone , 2012, Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics.

[96]  Yiqiang Chen,et al.  Fall Detecting and Alarming Based on Mobile Phone , 2010, 2010 7th International Conference on Ubiquitous Intelligence & Computing and 7th International Conference on Autonomic & Trusted Computing.

[97]  Jian Huang,et al.  A novel fall prevention scheme for intelligent cane robot by using a motor driven universal joint , 2011, 2011 International Symposium on Micro-NanoMechatronics and Human Science.

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

[99]  C. Becker,et al.  Smartphone-based solutions for fall detection and prevention: the FARSEEING approach , 2012, Zeitschrift für Gerontologie und Geriatrie.

[100]  Lei Wang,et al.  Exploration and Implementation of a Pre-Impact Fall Recognition Method Based on an Inertial Body Sensor Network , 2012, Sensors.

[101]  Shang-Lin Hsieh,et al.  A Finite State Machine-Based Fall Detection Mechanism on Smartphones , 2012, 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing.

[102]  Norbert Noury,et al.  A feasibility study of using a smartphone to monitor mobility in elderly , 2012, 2012 IEEE 14th International Conference on e-Health Networking, Applications and Services (Healthcom).

[103]  Lorenzo Chiari,et al.  Smartphone-based applications for investigating falls and mobility , 2011, 2011 5th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth) and Workshops.

[104]  Majid Sarrafzadeh,et al.  A Remote Patient Monitoring System for Congestive Heart Failure , 2011, Journal of Medical Systems.

[105]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[106]  Yun Li,et al.  A Microphone Array System for Automatic Fall Detection , 2012, IEEE Transactions on Biomedical Engineering.

[107]  Jian Huang,et al.  Real-time fall and overturn prevention control for human-cane robotic system , 2013, IEEE ISR 2013.

[108]  Jesús Fontecha,et al.  Elderly frailty detection by using accelerometer-enabled smartphones and clinical information records , 2012, Personal and Ubiquitous Computing.

[109]  Wen-Chang Cheng,et al.  Fall Detection with the Support Vector Machine during Scripted and Continuous Unscripted Activities , 2012, Sensors.

[110]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[111]  Inmaculada Plaza,et al.  Guidelines to Design Smartphone Applications for People with Intellectual Disability: A Practical Experience , 2013, ISAmI.

[112]  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.

[113]  Nancy Fell,et al.  Telemedicine assessment of fall risk using wireless sensors , 2013, 2013 IEEE International Conference on Sensing, Communications and Networking (SECON).

[114]  Peter P. K. Chiu,et al.  Health Guard system with emergency call based on smartphone , 2011 .