Monitoring of Human Movements for Fall Detection and Activities Recognition in Elderly Care Using Wireless Sensor Network: a Survey

The problem with accidental falls among elderly people has massive social and economic impacts. Falls in elderly people are the main cause of admission and extended period of stay in a hospital. It is the sixth cause of death for people over the age of 65, the second for people between 65 and 75, and the first for people over 75. Among people affected by Alzheimer’s Disease, the probability of a fall increases by a factor of three. Elderly care can be improved by using sensors that monitor the vital signs and activities of patients, and remotely communicate this information to their doctors and caregivers. For example, sensors installed in homes can alert caregivers when a patient falls. Research teams in universities and industries are developing monitoring technologies for in-home elderly care. They make use of a network of sensors including pressure sensors on chairs, cameras, and RFID tags embedded throughout the home of the elderly people as well as in furniture and clothing, which communicate with tag readers in floor mats, shelves, and walls. A fall can occur not only when a person is standing, but also while sitting on a chair or lying on a bed during sleep. The consequences of a fall can vary from scrapes to fractures and in some cases lead to death. Even if there are no immediate consequences, the long-wait on the floor for help increases the probability of death from the accident. This underlines the importance of real-time monitoring and detection of a fall to enable first-aid by relatives, paramedics or caregivers as soon as possible. Monitoring the activities of daily living (ADL) is often related to the fall problem and requires a non-intrusive technology such as a wireless sensor network. An elderly with risk of fall can be instrumented with (preferably) one wireless sensing device to capture and analyze the 1

[1]  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.

[2]  Chia-Chi Wang,et al.  Development of a Fall Detecting System for the Elderly Residents , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[3]  T. Masud,et al.  Epidemiology of falls. , 2001, Age and ageing.

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

[5]  D. Oliver,et al.  Falls risk-prediction tools for hospital inpatients. Time to put them to bed? , 2008, Age and ageing.

[6]  Kent Larson,et al.  Real-Time Recognition of Physical Activities and Their Intensities Using Wireless Accelerometers and a Heart Rate Monitor , 2007, 2007 11th IEEE International Symposium on Wearable Computers.

[7]  M. Kangas,et al.  Sensitivity and specificity of fall detection in people aged 40 years and over. , 2009, Gait & Posture.

[8]  S L Luther,et al.  Fall risk assessment measures: an analytic review. , 2001, The journals of gerontology. Series A, Biological sciences and medical sciences.

[9]  L. Lipsitz,et al.  Causes and correlates of recurrent falls in ambulatory frail elderly. , 1991, Journal of gerontology.

[10]  N. Noury,et al.  Monitoring behavior in home using a smart fall sensor and position sensors , 2000, 1st Annual International IEEE-EMBS Special Topic Conference on Microtechnologies in Medicine and Biology. Proceedings (Cat. No.00EX451).

[11]  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).

[12]  Yeh-Liang Hsu,et al.  Algorithm Design for Real-time Physical Activity Identification with Accelerometry Measurement , 2007, IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society.

[13]  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.

[14]  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.

[15]  M. Gibson,et al.  The prevention of falls in late life. A report of the Kellogg International Work Group on the prevention of falls by the elderly , 1987 .

[16]  M. Mathie,et al.  of the 23 rd Annual EMBS International Conference , October 25-28 , Istanbul , Turkey A SYSTEM FOR MONITORING POSTURE AND PHYSICAL ACTIVITY USING ACCELEROMETERS , 2004 .

[17]  Michael Vassallo,et al.  Fall risk-assessment tools compared with clinical judgment: an evaluation in a rehabilitation ward. , 2008, Age and ageing.

[18]  David E. Culler,et al.  Telos: enabling ultra-low power wireless research , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[19]  Patrick Boissy,et al.  A smart sensor based on rules and its evaluation in daily routines , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[20]  F Englander,et al.  Economic dimensions of slip and fall injuries. , 1996, Journal of forensic sciences.

[21]  H. Menz,et al.  Falls in Older People: Risk Factors and Strategies for Prevention , 2000 .

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

[23]  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.

[24]  J A Ward,et al.  An epidemiological study of falls in older community-dwelling women: the Randwick falls and fractures study. , 2010, Australian journal of public health.

[25]  G. ÓLaighin,et al.  A proposal for the classification and evaluation of fall detectors Une proposition pour la classification et l'évaluation des détecteurs de chutes , 2008 .

[26]  Youngbum Lee,et al.  Implementation of Accelerometer Sensor Module and Fall Detection Monitoring System based on Wireless Sensor Network , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[27]  M. Borrie,et al.  Circumstances and consequences of falls experienced by a community population 70 years and over during a prospective study. , 1990, Age and ageing.

[28]  Clare Eileen Bradley,et al.  Hospitalisations due to falls by older people, Australia 2005-06 , 2013 .

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

[30]  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.

[31]  T. Kennedy.  The prevention of falls in later life. A report of the Kellogg International Work Group on the Prevention of Falls by the Elderly. , 1987, Danish medical bulletin.

[32]  A. E. Chapman,et al.  Biomechanical Analysis of Fundamental Human Movements , 2008 .

[33]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[34]  Manuel Prado-Velasco,et al.  The inescapable smart impact detection system (ISIS): An ubiquitous and personalized fall detector based on a distributed “divide and conquer strategy” , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.