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Fall detection with body-worn sensors

A systematic review

Sturzerkennung mit am Körper getragenen Sensoren

Ein systematischer Review

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Zeitschrift für Gerontologie und Geriatrie Aims and scope Submit manuscript

Abstract

Background and aims

Falls among older people remain a major public health challenge. Body-worn sensors are needed to improve the understanding of the underlying mechanisms and kinematics of falls. The aim of this systematic review is to assemble, extract and critically discuss the information available in published studies, as well as the characteristics of these investigations (fall documentation and technical characteristics).

Methods

The searching of publically accessible electronic literature databases for articles on fall detection with body-worn sensors identified a collection of 96 records (33 journal articles, 60 conference proceedings and 3 project reports) published between 1998 and 2012. These publications were analysed by two independent expert reviewers. Information was extracted into a custom-built data form and processed using SPSS (SPSS Inc., Chicago, IL, USA).

Results

The main findings were the lack of agreement between the methodology and documentation protocols (study, fall reporting and technical characteristics) used in the studies, as well as a substantial lack of real-world fall recordings. A methodological pitfall identified in most articles was the lack of an established fall definition. The types of sensors and their technical specifications varied considerably between studies.

Conclusion

Limited methodological agreement between sensor-based fall detection studies using body-worn sensors was identified. Published evidence-based support for commercially available fall detection devices is still lacking. A worldwide research group consensus is needed to address fundamental issues such as incident verification, the establishment of guidelines for fall reporting and the development of a common fall definition.

Zusammenfassung

Einleitung

Stürze älterer Menschen stellen eine große Aufgabe für das Gesundheitswesen dar. Am Körper getragene Sensoren helfen, die Kinematik und Mechanismen von Stürzen besser zu verstehen. Ziel dieses Reviews ist es, Informationen aus publizierten Studien und deren Charakteristika (Sturzdokumentation und technische Spezifikationen) zu sammeln, zu extrahieren und kritisch zu diskutieren.

Methoden

Die systematische Suche innerhalb der öffentlich zugänglichen, elektronischen Literaturdatenbanken nach Artikeln zur Sturzerkennung mit am Körper getragenen Sensoren ergab 96 Publikationen (33 Fachzeitschriftenartikel, 60 Konferenzbeiträge und 3 Projektberichte), die von 1998 bis 2012 veröffentlicht wurden. Diese Publikationen wurden von jeweils zwei unabhängigen Gutachtern analysiert. Dabei wurden die relevanten Daten elektronisch erfasst und mit SPSS ausgewertet.

Ergebnisse

Die wichtigsten Erkenntnisse sind eine mangelnde Übereinstimmung in Methodik und Dokumentation (Studien- und technische Charakteristika sowie Sturzdokumentation) und ein substanzieller Mangel an Aufzeichnungen von realen Stürzen. In den meisten Publikationen fehlte eine etablierte Sturzdefinition. Die verwendeten Sensortypen sowie deren technische Spezifikationen variierten erheblich innerhalb der untersuchten Studien.

Schlussfolgerungen

Es wurde eine begrenzte methodische Übereinstimmung bei der sensorbasierten Sturzerkennung festgestellt. Es ist keine publizierte Evidenzbasis für kommerziell erhältliche Sturzerkennungsgeräte vorhanden. Ein Konsens von Forschergruppen weltweit wird notwendig sein, um fundamentale Fragen, z. B. zur Sturzverifikation, zu erörtern, Leitlinien für eine Sturzdokumentation zu erarbeiten und eine gemeinsame Sturzdefinition zu entwickeln.

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References

  1. Peel NM (2011) Epidemiology of falls in older age. Can J Aging 1–13

  2. Deandrea S, Lucenteforte E, Bravi F et al (2010) Risk factors for falls in community-dwelling older people: a systematic review and meta-analysis. Epidemiology 21:658–668

    Article  PubMed  Google Scholar 

  3. Zecevic AA, Salmoni AW, Lewko JH et al (2009) Utilization of the Seniors Falls Investigation Methodology to identify system-wide causes of falls in community-dwelling seniors. Gerontologist 49:685–696

    Article  PubMed  Google Scholar 

  4. Gillespie LD, Robertson MC, Gillespie WJ et al (2012) Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev 9:CD007146

    PubMed  Google Scholar 

  5. Robinovitch SN, Feldman F, Yang Y et al (2013) Video capture of the circumstances of falls in elderly people residing in long-term care: an observational study. Lancet 381:47–54

    Article  PubMed  Google Scholar 

  6. Mellone S, Tacconi C, Schwickert L et al (2012) Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Z Gerontol Geriatr 45:722–727

    Article  PubMed  CAS  Google Scholar 

  7. Becker C, Chiari L (2013) What videos can tell us about falling. Lancet 381:8–9

    Article  PubMed  Google Scholar 

  8. Kangas M (2011) Development of accelerometry-based fall detection

  9. Xinguo Y (2008) Approaches and principles of fall detection for elderly and patient. CORD Conf Proc 2008:42–47

    Google Scholar 

  10. Bagalà F, Becker C, Cappello A et al (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7:e37062

    Article  PubMed  Google Scholar 

  11. Lamb SE, Jørstad-Stein EC, Hauer K, Becker C (2005) Development of a common outcome data set for fall injury prevention trials: the Prevention of Falls Network Europe consensus. J Am Geriatr Soc 53:1618–1622

    Article  PubMed  Google Scholar 

  12. Noury N, Rumeau P, Bourke AK et al (2008) A proposal for the classification and evaluation of fall detectors. IRBM 29:340–349

    Article  Google Scholar 

  13. Becker C, Schwickert L, Mellone S et al (2012) Proposal for a multiphase fall model based on real-world fall recordings with body-fixed sensors. Z Gerontol Geriatr 45:707–715

    Article  PubMed  CAS  Google Scholar 

  14. Hayes WC, Myers ER, Robinovitch SN (1996) Etiology and prevention of age-related hip fractures. Bone 18:S77–S86

    Article  Google Scholar 

  15. Dinh A, Teng D, Chen L et al (2008) Data acquisition system using six degree-of-freedom inertia sensor and Zigbee wireless link for fall detection and prevention. Conf Proc IEEE Eng Med Biol Soc 2008:2353–2356

    PubMed  CAS  Google Scholar 

  16. Dinh A, Shi Y, Teng D et al (2009) A fall and near-fall assessment and evaluation system. Open Biomed Eng J 3:1–7

    Article  PubMed  Google Scholar 

  17. Bourke AK, O’Donovan KJ, Nelson J, O’Laighin GM (2008) Fall-detection through vertical velocity thresholding using a tri-axial accelerometer characterized using an optical motion-capture system. Conf Proc IEEE Eng Med Biol Soc 2008:2832–2835

    PubMed  Google Scholar 

  18. Bourke AK, O’Donovan KJ, O’Laighin G (2008) The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls. Med Eng Phys 30:937–946

    Article  PubMed  CAS  Google Scholar 

  19. Bourke AK, O’Brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26:194–199

    Article  PubMed  CAS  Google Scholar 

  20. Bourke AK, Lyons GM (2008) A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 30:84–90

    Article  PubMed  CAS  Google Scholar 

  21. Brownsell S, Hawley MS (2004) Automatic fall detectors and the fear of falling. J Telemed Telecare 10:262–266

    Article  PubMed  Google Scholar 

  22. Bloch F, Gautier V, Noury N et al (2011) Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects. Ann Phys Rehabil Med

  23. Boyle J, Karunanithi M (2008) Simulated fall detection via accelerometers. Conf Proc IEEE Eng Med Biol Soc 2008:1274–1277

    PubMed  Google Scholar 

  24. Kangas M, Vikman I, Nyberg L et al (2012) Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects. Gait Posture 35:500–505

    Article  PubMed  CAS  Google Scholar 

  25. Tamura T, Yoshimura T, Horiuchi F et al (2000) Fujimoto. An ambulatory fall monitor for the elderly. 4:2608–2604

  26. Van de Ven P, Bourke AK, Nelson J, O’Laighin G (2008) A wireless platform for fall and mobility monitoring. 2008:319–324

  27. Klenk J, Becker C, Lieken F et al (2011) Comparison of acceleration signals of simulated and real-world backward falls. Med Eng Phys 33:368–373

    Article  PubMed  CAS  Google Scholar 

  28. Knezovic T, Najafi B, Lindemann U et al (2005) A smart device for fall detection using triaxial accelerometers

  29. Hauer K, Lamb SE, Jorstad EC et al (2006) Systematic review of definitions and methods of measuring falls in randomised controlled fall prevention trials. Age Ageing 35:5–10

    Article  PubMed  Google Scholar 

  30. Abbate S, Avvenuti M, Cola G et al (2011) Recognition of false alarms in fall detection systems. CORD Conf Proc 2011:23–28

    Google Scholar 

  31. Anania G, Tognetti A, Carbonaro N et al (2008) Development of a novel algorithm for human fall detection using wearable sensors. IEEE Sensors Proc 2008:1336–1339

    Google Scholar 

  32. Aziz O, Robinovitch SN (2011) An analysis of the accuracy of wearable sensors for classifying the causes of falls in humans. IEEE Trans Neural Syst Rehabil Eng 19:670–676

    Article  PubMed  Google Scholar 

  33. Benocci M, Carlo T, Elisabetta F et al (2010) Accelerometer-based fall detection using optimized ZigBee data streaming. Microelectronics J 41:703–710

    Article  CAS  Google Scholar 

  34. Bianchi F, Redmond SJ, Narayanan MR et al (2009) Falls event detection using triaxial accelerometry and barometric pressure measurement. Conf Proc IEEE Eng Med Biol Soc 2009:6111–6114

    PubMed  Google Scholar 

  35. Boissy P, Choquette S, Hamel M, Noury N (2007) User-based motion sensing and fuzzy logic for automated fall detection in older adults. Telemed J E Health 13:683–693

    Article  PubMed  Google Scholar 

  36. Bourke AK, Ven P van de, Gamble M et al (2010) Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities. J Biomech 43:3051–3057

    Article  PubMed  CAS  Google Scholar 

  37. Bourke AK, Torrent M, Parra X et al (2011) Fall algorithm development using kinematic parameters measured from simulated falls performed in a quasi-realistic environment using accelerometry. Conf Proc IEEE Eng Med Biol Soc 2011:4449–4452

    PubMed  Google Scholar 

  38. Brown G (2005) An accelerometer based fall detector: development, experimentation, and analysis

  39. Campo E, Grangereau E (2008) Wireless fall sensor with GPS location for monitoring the elderly. Conf Proc IEEE Eng Med Biol Soc 2008:498–501

    PubMed  Google Scholar 

  40. Chang S-Y, Lai C-F, Chao H-CJ et al (2011) An environmental-adaptive fall detection system on mobile device. J Med Syst 35:1299–1312

    Article  PubMed  Google Scholar 

  41. Chao P-K, Chan H-L, Tang F-T et al (2009) A comparison of automatic fall detection by the cross-product and magnitude of tri-axial acceleration. Physiol Meas 30:1027–1037

    Article  PubMed  Google Scholar 

  42. Chen J, Kwong K, Chang D et al (2005) Wearable sensors for reliable fall detection. Conf Proc IEEE Eng Med Biol Soc 4:3551–3554

    PubMed  Google Scholar 

  43. Chen G-C, Huang C-N, Chiang C-Y et al (2010) A reliable fall detection system based on wearable sensor and signal magnitude area for elderly residents. In: Lee Y, Bien ZZ, Mokhtari M, Kim JT, Park M, Kim J et al (eds) ICOST, vol 6159. Springer, pp 267–270

  44. Choi Y, Ralhan AS, Ko S (2011) A study on machine learning algorithms for fall detection and movement classification. CORD Conf Proc 2011:1–8

    Google Scholar 

  45. Dai J, Xiaole B, Zhimin Yang ZY et al (2010) PerFallD: a pervasive fall detection system using mobile phones. CORD Conf Proc 2010:292–297

    Google Scholar 

  46. Degen T, Jaeckel H, Rufer M, Wyss S (2003) SPEEDY:a fall detector in a wrist watch 2003:184–187

  47. Diaz A, Prado M, Roa LM et al (2004) Preliminary evaluation of a full-time falling monitor for the elderly. 1:2180–2183

  48. Dinh A, Teng D, Li Chen et al (2009) Implementation of a physical activity monitoring system for the elderly people with built-in vital sign and fall detection. 2009:1226–1231

  49. Dinh C, Struck M (2009) A new real-time fall detection approach using fuzzy logic and a neural network. 2009:57–60

  50. Erdogan SZ, Bilgin TT, Cho J (2010) Fall detection by using K-nearest neighbor algorithm on WSN data. CORD Conf Proc 2010:2054–2058

    Google Scholar 

  51. Estudillo-Valderrama MA, Roa LM, Reina-Tosina J, Naranjo-Hernandez D (2009) Design and implementation of a distributed fall detection system—personal server. IEEE Trans Inf Technol Biomed 13:874–881

    Article  PubMed  Google Scholar 

  52. Fukaya K (2002) Fall detection sensor for fall protection airbag. CORD Conf Proc 1:419–411

    Google Scholar 

  53. Gjoreski H, Lustrek M, Gams M (2011) Accelerometer placement for posture recognition and fall detection. CORD Conf Proc 2011:47–54

    Google Scholar 

  54. Grassi M, Lombardi A, Rescio G et al (2008) A hardware-software framework for high-reliability people fall detection. IEEE Sensors Proc 2008:1328–1331

    Google Scholar 

  55. Hansen TR (2005) Using smart sensors and a camera phone to detect and verify the falls of elderly

  56. Huang C-N, Chiang C-Y (n.d.) Fall detection system for healthcare quality improvement in residential care facilities. J Med Biol Eng 30:247–252

  57. Hwang JY, Kang JM, Jang YW, Kim HC (2004) Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly. CORD Conf Proc 1:2204–2207

    Google Scholar 

  58. Jacob J, Nguyen T, Lie DYC et al (2011) A fall detection study on the sensors placement location and a rule-based multi-thresholds algorithm using both accelerometer and gyroscopes. IEEE Int Conf Fuzzy Syst Proc 2011:666–671

    Google Scholar 

  59. Jantaraprim P, Phukpattaranont P, Limsakul C, Wongkittisuksa B (2010) Improving the accuracy of a fall detection algorithm using free fall characteristics. CORD Conf Proc 2010:501–504

    Google Scholar 

  60. Jeon A, Park G, Ro J-H, Geon G (2012) Development of the algorithm for detecting falls during daily activity using 2 tri-axial accelerometers

  61. Kaenampornpan M, Anuchad T, Supaluck P (2011) Fall detection prototype for Thai elderly in mobile computing era. CORD Conf Proc 2011:446–449

    Google Scholar 

  62. Kang JM, Yoo T, Kim HC (2006) A wrist-worn integrated health monitoring instrument with a tele-reporting device for telemedicine and telecare. IEEE Trans Instrum Meas 55:1655–1661

    Article  Google Scholar 

  63. Kangas M, Konttila A, Winblad I, Jamsa T (2007) Determination of simple thresholds for accelerometry-based parameters for fall detection. Conf Proc IEEE Eng Med Biol Soc 2007:1367–1370

    PubMed  Google Scholar 

  64. Kangas M, Konttila A, Lindgren P et al (2008) Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28:285–291

    Article  PubMed  Google Scholar 

  65. Kangas M, Vikman I, Wiklander J et al (2009) Sensitivity and specificity of fall detection in people aged 40 years and over. Gait Posture 29:571–574

    Article  PubMed  Google Scholar 

  66. Lai C-F, Huang Y-M, Park JH, Chao H-C (2010) Adaptive body posture analysis for elderly-falling detection with multisensors. IEEE Intell Syst 25:20–30

    Article  Google Scholar 

  67. Le TM, Pan R (2009) Accelerometer-based sensor network for fall detection. CORD Conf Proc 2009:265–268

    Google Scholar 

  68. Lee RY, Carlisle AJ (2011) Detection of falls using accelerometers and mobile phone technology. Age Ageing 40:690–696

    Article  PubMed  Google Scholar 

  69. Lee Y, Lee M (2008) Accelerometer sensor module and fall detection monitoring system based on wireless sensor network for e-health applications. Telemed J E Health 14:587–592

    Article  PubMed  Google Scholar 

  70. Li Q, Stankovic JA, Hanson MA et al (2009) Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. 2009:138–143

  71. Li Q, Stankovic JA (2011) Grammar-based, posture- and context-cognitive detection for falls with different activity levels. In: Jacobs IM, Soon-Shiong P, Topol E, Toumazou C (eds) Wireless health. ACM, p 6

  72. Lin CS, Hsu HC, Lay YL et al (2007) Wearable device for real-time monitoring of human falls. Meas 40:10–10

    Article  Google Scholar 

  73. Lindemann U, Hock A, Stuber M et al (2005) Evaluation of a fall detector based on accelerometers: a pilot study. Med Biol Eng Comput 43:548–551

    Article  PubMed  CAS  Google Scholar 

  74. Luo S, Qingmao Hu QH (2004) A dynamic motion pattern analysis approach to fall detection. CORD Conf Proc 2004:1–8a

    Google Scholar 

  75. Lustrek M (2011) Detecting falls with location sensors and accelerometers

  76. Marinkovic S, Puppo R, Pan RLC, Popovici E (2010) Implementation and testing of a secure fall detection system for body area networks. CORD Conf Proc 2010:315–318

    Google Scholar 

  77. Mathie M, Celler BG, Lovell NH, Coster AC (2004) Classification of basic daily movements using a triaxial accelerometer. Med Biol Eng Comput 42:679–687

    Article  PubMed  CAS  Google Scholar 

  78. Mostarac P, Roman Malaric, Jurcevic M et al (2011) System for monitoring and fall detection of patients using mobile 3-axis accelerometers sensors. 2011:456–459

  79. Naranjo-Hernandez D, Roa LM, Reina-Tosina J, Estudillo-Valderrama MA (2012) Personalization and adaptation to the medium and context in a fall detection system. IEEE Trans Inf Technol Biomed 16:264–271

    Article  PubMed  Google Scholar 

  80. Nguyen TT, Cho MC, Lee TS (2009) Automatic fall detection using wearable biomedical signal measurement terminal. Conf Proc IEEE Eng Med Biol Soc 2009:5203–5206

    PubMed  Google Scholar 

  81. Niazmand K, Jehle C, D’A LT, Lueth TC (2010) A new washable low-cost garment for everyday fall detection. Conf Proc IEEE Eng Med Biol Soc 2010:6377–6380

    PubMed  Google Scholar 

  82. Noury N, Barralon P, Virone G et al (2003) A smart sensor based on rules and its evaluation in daily routines. Proc 25th Annual Int Conf IEEE Eng Med Biol Soc 4:3286–3289

    Google Scholar 

  83. Nyan MN, Tay FEH, Tan AWY, Seah KHW (2006) Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization. Med Eng Phys 28:842–849

    Article  PubMed  CAS  Google Scholar 

  84. Nyan M, Tay F, Manimaran M, Seah K (2006) Garment-based detection of falls and activities of daily living using 3-axis {MEMS} accelerometer. J Phys 34:1059–1067

    Google Scholar 

  85. Nyan MN, Tay FEH, Murugasu E (2008) A wearable system for pre-impact fall detection. J Biomech 41:3475–3481

    Article  PubMed  CAS  Google Scholar 

  86. Ojetola O, Gaura EI, Brusey J (2011) Fall detection with wearable sensors—safe (smart fall detection). CORD Conf Proc 2011:318–321

    Google Scholar 

  87. Paiyarom S, Tangamchit P, Keinprasit R, Kayasith P (2009) Fall detection and activity monitoring system using dynamic time warping for elderly and disabled people. Proceedings of the 3rd International Convention on Rehabilitation Engineering & Assistive Technology. ACM, New York, p 9:1–9:4

  88. Perolle G, Fraisse P, Mavros M et al (2006) Automatic fall detection and activity monitoring for elderly. Proc MEDETEL 41:33–41

    Google Scholar 

  89. Perry JT, Kellog S, Vaidya SM et al (2009) Survey and evaluation of real-time fall detection approaches. CORD Conf Proc 2009:158–164

    Google Scholar 

  90. Quagliarella L, Sasanelli N, Belgiovine B (2008) A fall and loss of consciousness wearable detector. Gerontechnology 7

  91. Quagliarella L, Sasanelli N, Belgiovine G (2008) An interactive fall and loss of consciousness detector system. Gait Posture 28:699–702

    Article  PubMed  CAS  Google Scholar 

  92. Salomon R, Lüder M, Bieber G (2010) iFall—case studies in unexpected falls. 2010:1645–1650

  93. Sim SY, Jeon HS, Chung GS et al (2011) Fall detection algorithm for the elderly using acceleration sensors on the shoes. Conf Proc IEEE Eng Med Biol Soc 2011:4935–4938

    PubMed  CAS  Google Scholar 

  94. Sposaro F, Tyson G (2009) iFall: an Android application for fall monitoring and response. Conf Proc IEEE Eng Med Biol Soc 2009:6119–6122

    PubMed  Google Scholar 

  95. Srinivasan S, Han J, Lal D, Gacic A (2007) Towards automatic detection of falls using wireless sensors. Conf Proc IEEE Eng Med Biol Soc 2007:1379–1382

    PubMed  Google Scholar 

  96. Tamura T, Yoshimura T, Sekine M et al (2009) A wearable airbag to prevent fall injuries. IEEE Trans Inf Technol Biomed 13:910–914

    Article  PubMed  Google Scholar 

  97. Tolkiehn M, Louis A, Benny L, Guang-Zhong Y (2011) Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor. Conf Proc IEEE Eng Med Biol Soc 2011:369–372

    PubMed  Google Scholar 

  98. Tomkun J, Nguyen B (2010) Design of a fall detection and prevention system for the elderly. EE 4BI6 Electrical Engineering Biomedical Capstones

  99. Lina Tong LT, Wei Chen WC, Quanjun Song QS, Yunjian Ge YG (2009) A research on automatic human fall detection method based on wearable inertial force information acquisition system. CORD Conf Proc 2009:949–953

    Google Scholar 

  100. Van Wieringen M, Eklund J (2008) Real-time signal processing of accelerometer data for wearable medical patient monitoring devices. Conf Proc IEEE Eng Med Biol Soc 2008:2397–2400

    Google Scholar 

  101. Wang C-C, Chiang C-Y, Lin P-Y et al (2008) Development of a fall detecting system for the elderly residents. CORD Conf Proc 2008:1359–1362

    Google Scholar 

  102. Williams G, Doughty K, Cameron K, Bradley DA (1998) A smart fall and activity monitor for telecare applications. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 3:1151–1154

    Google Scholar 

  103. Wolf H, Lohse A (n.d.) Development of a fall detector and classifier based on a triaxial accelerometer demo board

  104. Wu G, Xue S (2008) Portable preimpact fall detector with inertial sensors. IEEE Trans Neural Syst Rehabil Eng 16:178–183

    Article  PubMed  Google Scholar 

  105. Yang Y, Xingqun Z (2011) Development of a fall detection algorithm based on a tri-axial accelerometer. CORD Conf Proc 1:1371–1374

    Google Scholar 

  106. Yavuz GR, Hulya Y, Lale A, Cem E (2011) Wavelet transform based fall detection. CORD Conf Proc 2011:142–145

    Google Scholar 

  107. Yoshida T, Mizuno F, Hayasaka T et al (2005) A wearable computer system for the detection and prevention of elderly users from falling

  108. Yuwono M, Moulton BD, Su SW et al (2012) Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems. Biomed Eng Online 11:9

    Article  PubMed  Google Scholar 

  109. Zhang T (2006) Fall detection by embedding an accelerometer in cellphone and using KFD algorithm. Int J Comp Sci Netw Security 6:277–284

    Google Scholar 

  110. Zhang T, Wang J, Xu L, Liu P (2006) Fall detection by wearable sensor and one-class SVM algorithm. In: Huang D-S, Li K, Irwin GW (eds) Intelligent computing in signal processing and pattern recognition. Springer, Berlin, pp 858–863

  111. Zheng J, Guang Zhang GZ, Taihu Wu TW (2009) Design of automatic fall detector for elderly based on triaxial accelerometer. CORD Conf Proc 2009:1–4

    Google Scholar 

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Acknowledgements

The authors wish to thank Stefanie Schneider, librarian of the Robert-Bosch-Hospital medical library, for contributing to the systematic literature search.

The following experts are part of the FARSEEING consensus group and contributed to the discussion: Christophe Büla, Centre Hospitalier Universitaire Vaudois Lausanne, Switzerland; Michele Carenini, NoemaLife spa, Italy; Kim Delbaere, Neuroscience Research Australia, Australia; Matthias Gietzelt, University of Braunschweig and Hannover Medical School, Germany; Klaus Hauer, Agaplesion Bethanien Hospital Heidelberg, Germany; Jeffrey M. Hausdorff, Beth Israel Deaconess Medical Center and Harvard Medical School, USA and Israel; Helen Hawley, University of Manchester, United Kingdom; Anisoara Ionescu, EPFL Lausanne, Switzerland; Maarit Kangas, University of Oulu, Finland; Fabio La Porta, Azienda USL di Modena, Italy; Stephen Lord, Neuroscience Research Australia, Australia; Walter Mätzler, University of Tübingen, Germany; Michael Marschollek, University of Braunschweig and Hannover Medical School, Germany; Sabato Mellone, University of Bologna, Italy; Fabio Bagalà, University of Bologna, Italy; Norbert Noury, University of Lyon, France; Rachel Potter, University of Warwick, United Kingdom; Kilian Rapp, Robert-Bosch Hospital Stuttgart and University of Ulm, Germany; Stephen Redmond, University of New South Wales, Australia; Stephen Robinovitch, Simon Fraser University, Canada; Stephane Rochat, Centre Hospitalier Universitaire Vaudois Lausanne, Switzerland; Johannes Salb, University of Erlangen, Germany; Michael Schwenk, Agaplesion Bethanien Hospital Heidelberg, Germany; Olav Sletvold, Norwegian University of Science and Technology, Norway; Stuart Smith, Neuroscience Research Australia, Australia; Matthis Synofzik, University of Tübingen, Germany; Enrico Valtolina, Bticino spa, Italy; Aleksandra Zecevic, Western University London Ontario, Canada; Tania Zieschang, Agaplesion Bethanien Hospital Heidelberg, Germany.

Compliance with ethical guidelines

Conflict of interest. L. Schwickert, C. Becker, U. Lindemann, C. Maréchal, A. Bourke, L. Chiari, JL. Helbostad, W. Zijlstra, K. Aminian, C. Todd, S. Bandinelli and J. Klenk state that there are no conflicts of interest.

The accompanying manuscript does not include studies on humans or animals.

The companies participating in the FARSEEING project had no influence on the writing of this manuscript and the presented results.

Funding

The FARSEEING project is being funded by the European Commission (Grant agreement no.: 288940).

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Schwickert, L., Becker, C., Lindemann, U. et al. Fall detection with body-worn sensors. Z Gerontol Geriat 46, 706–719 (2013). https://doi.org/10.1007/s00391-013-0559-8

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