Skip to main content
SpringerLink
Log in

Smartphone-based solutions for fall detection and prevention: the FARSEEING approach

Smartphonebasierte Lösungen zur Sturzerkennung und -prävention: das FARSEEING-Projekt

  • Beiträge zum Themenschwerpunkt
  • Published:
Zeitschrift für Gerontologie und Geriatrie Aims and scope Submit manuscript

Abstract

Falls are not an inevitable consequence of aging. The risk and rate of falls can be reduced. Recent improvements in smartphone technology enable implementation of a wide variety of services and applications, thus making the smartphone more of a digital companion than simply a communication tool. This paper presents the results obtained by the FARSEEING project where smartphones are one example of intervention in a population-based scenario. The applications developed take advantage of the smartphone-embedded inertial sensors and require that subjects wear the smartphone by means of a waist belt. The uFall Android application has been developed for monitoring the user’s motor activities at home. The application does not require any direct interaction with the user and it is also capable of running a real-time fall-detection algorithm. uTUG is a stand-alone application for instrumenting the Timed Up and Go test, which is a test often included in fall risk assessment protocols. The application acts like a pocket-sized motion laboratory, since it is capable not only of recording the trial but also of processing the data and immediately displaying the results. uTUG is designed to be self-administrable at home.

Zusammenfassung

Stürze sind keine notwendige Folge des Alterns, sie können verhindert werden. Die jüngsten Entwicklungen der Smartphonetechnologie ermöglichen eine Vielzahl von Anwendungen und Applikationen, wodurch das Gerät nicht nur als Kommunikationswerkzeug, sondern zunehmend als digitaler Alltagsbegleiter dient. In diesem Artikel werden Ergebnisse des FARSEEING-Projekts präsentiert, bei dem Smartphones ein Interventionsbeispiel in einem populationsbezogenen Szenario sind. Die hier vorgestellten Applikationen nutzen die im Gerät integrierten Inertialsensoren. Das Smartphone wird dabei mit einem Hüftgurt getragen. Die uFall-Applikation dient zur innerhäuslichen Beobachtung der körperlichen Aktivität des Nutzers und ermöglicht eine algorithmusbasierte Echtzeitsturzerkennung. Die uTUG-Applikation instrumentalisiert den Timed-up-and-go(TUG)-Test (Zeit bis zum Aufstehen und Gehen), welcher häufig zur Messung des Sturzrisikos verwendet wird. Dieses „miniaturisierte Bewegungslabor“ erlaubt nicht nur die Durchführung einzelner Messungen, sondern stellt auch prozessierte Daten zur direkten Auswertung bereit. Die Applikationen ermöglichen die Eigenanwendung und erfordern keine direkte Interaktion des Nutzers mit dem Gerät.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Heinrich S, Rapp K, Rissmann U et al (2010) Cost of falls in old age: a systematic review. Osteoporos Int 21(6):891–902

    Article  PubMed  CAS  Google Scholar 

  2. Tinetti ME, Kumar C (2010) The patient who falls: ‘‘It’s always a trade-off.’’ JAMA 303:258–266

    Google Scholar 

  3. Panel on Prevention of Falls in Older Persons, American Geriatrics Society and British Geriatrics Society (2011) Summary of the Updated American Geriatrics Society/British Geriatrics Society clinical practice guideline for prevention of falls in older persons. J Am Geriatr Soc 59(1):148–157

    Article  Google Scholar 

  4. Cesari M, Landi F, Torre S et al (2002) Prevalence and risk factors for falls in an older community-dwelling population. J Gerontol 57A(11):M722–M726

    Google Scholar 

  5. Delbaere K, Close JCT, Heim J et al (2010) A multifactorial approach to understanding fall risk in older people. J Am Geriatr Soc 58(9):1679–1685

    Article  PubMed  Google Scholar 

  6. Alwan M, Rajendran PJ, Kell S et al (2006) A smart and passive floor-vibration based fall detector for elderly. Conf Proc. of the 2nd ICTTA 1003–1007

  7. Popescu M, Li Y, Skubic M, Rantz M (2008) An acoustic fall detector system that uses sound height information to reduce the false alarm rate. Conf Proc 30th IEEE EMBS 4628–4631

  8. Lee T, Mihailidis A (2005) An intelligent emergency response system: preliminary development and testing of automated fall detection. J Telemed TeleCare 11(4):194–198

    Article  PubMed  Google Scholar 

  9. Bourke AK, Van de Ven P, 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 

  10. Bourke AK, Lyons GM (2008) A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor. Med Eng Phys 30(1):84–90

    Article  PubMed  CAS  Google Scholar 

  11. Gillespie LD, Gillespie WJ, Robertson MC et al (2009) Interventions for preventing falls in elderly people. Cochrane Database Syst Rev:CD007146

    Google Scholar 

  12. Cameron ID, Murray GR, Gillespie LD et al (2010) Interventions for preventing falls in older people in nursing care facilities and hospitals. Cochrane Database Syst Rev:CD005465

    Google Scholar 

  13. Consolvo S, McDonald DW, Toscos T et al (2008) Activity sensing in the wild: a field trial of Ubifit garden, Conf Proc 26th ACM SIGCHI Human Factors Comp. Sys 1797–1806

  14. Lane ND, Miluzzo E, Hong LU et al (2010) A survey of mobile phone sensing. IEEE Commun Mag 48(9):140–150

    Article  Google Scholar 

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

  16. http://mover.projects.fraunhofer.pt/index.html. Accessed June 2012

  17. Dai J, Bai X, Yang Z et al (2010) Mobile phone-based pervasive fall detection. J Personal and Ubiquitous Computing 14(7):633–643

    Article  Google Scholar 

  18. Lee RY, Carlisle AJ (2011) Detection of falls using accelerometers and mobile phone technology. Age Ageing 40(6):690–696

    Article  PubMed  Google Scholar 

  19. Kangas M, Konttila A, Lindegren P et al (2008) Comparison of low-complexity fall detection algorithms for body attached accelerometers. Gait Posture 28(2):285–291

    Article  PubMed  Google Scholar 

  20. Berg KO, Maki BE, Williams KI (1992) Clinical and laboratory measures of postural balance in an elderly population. Arch Phys Med Rehabil 73:1073–1080

    PubMed  CAS  Google Scholar 

  21. Lin M, Hwang H, Hu M et al (2004) Psychometric comparisons of the timed up and go, one-leg stand, functional reach, and tinetti balancemeasures in community-dwelling older people. J Am Geriatr Soc 52:1343–1348

    Article  PubMed  Google Scholar 

  22. Weiss A, Herman T, Plotnik M et al (2010) Can an accelerometer enhance the utility of the Timed Up & Go Test when evaluating patients with Parkinson’s disease? Med Eng Phys 32(2):119–125

    Article  PubMed  Google Scholar 

  23. Zampieri C, Salarian A, Carlson-Kuhta P et al (2010) The instrumented timed up and go test: potential outcome measure for disease modifying therapies in Parkinson’s disease. J Neurol Neurosurg Psychiatry 81(2):171–176

    Article  PubMed  Google Scholar 

  24. Marschollek M, Nemitz G, Gietzelt M et al (2009) Predicting in-patient falls in a geriatric clinic: a clinical study combining assessment data and simple sensory gait measurements. Z Gerontol Geriatr 42(4):317–321

    Article  PubMed  CAS  Google Scholar 

  25. Mellone S, Tacconi C, Chiari L (2012) Validity of a Smartphone-based instrumented Timed Up and Go. Gait Posture 1(36):163–165

    Article  Google Scholar 

  26. Tacconi C, Mellone S, Chiari L (2011) Smartphone-based applications for investigating falls and mobility, Conf Proc 5th PervasiveHealth 258–261

  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(3):368–373

    Article  PubMed  CAS  Google Scholar 

  28. Bagalà F, Becker C, Cappello A et al (2012) Evaluation of accelerometer-based fall detection algorithms on real-world falls. PLoS ONE 7(5):e37062

    Article  PubMed  Google Scholar 

  29. Tacconi C, Mellone S, Chiari L (2012) uTUG: a smartphone application for home‐based TUG testing. Conf Proc 1st Joint World Conference of ISPGR and Gait & Mental Function 329–330

Download references

Acknowledgments

This FARSEEING project is co-funded by the European Commission, Seventh Framework Programme, Cooperation—ICT, Grant Agreement no. 288940.

Conflict of interest

On behalf of all authors, the corresponding author states that there are no conflicts of interest.

Author information

Authors and Affiliations

  1. Department of Electronics, Computer Science and Systems, University of Bologna, Bologna, Italy

    S. Mellone, C. Tacconi & L. Chiari

  2. Health Sciences and Technologies - Interdepartmental Center for Industrial Research, University of Bologna, Bologna, Italy

    C. Tacconi & L. Chiari

  3. Department of Clinical Gerontology, Robert-Bosch Hospital, Stuttgart, Germany

    L. Schwickert, J. Klenk & C. Becker

  4. Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany

    J. Klenk

Authors
  1. S. Mellone
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Tacconi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. L. Schwickert
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Klenk
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. C. Becker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. L. Chiari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Mellone.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mellone, S., Tacconi, C., Schwickert, L. et al. Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Z Gerontol Geriat 45, 722–727 (2012). https://doi.org/10.1007/s00391-012-0404-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00391-012-0404-5

Keywords

Schlüsselwörter

Search

Navigation