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Depth-Based Fall Detection: Outcomes from a Real Life Pilot

  • Susanna SpinsanteEmail author
  • Marco Fagiani
  • Marco Severini
  • Stefano Squartini
  • Friedrich Ellmenreich
  • Giusy Martelli
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 544)

Abstract

With the increasing ageing population representing a challenge for society and health care systems, solutions based on ICT to prolong the independent living of older adults become critical. Among them, systems able to automatically detect falls are being investigated since several years, because many solutions that appear promising when tested in lab settings, fail when faced with the constraints and unforeseen circumstances of real deployments. In this paper, we present the outcomes resulting from the pilot installation of a fall detection system based on the use of depth sensors located on the ceiling of the monitored apartment, where a 75 years old woman lives alone. We highlight the system design process, moving from the research leading to an original algorithm working offline, preliminarily tested in a lab setting, to the real-time engineering of the software, and the physical deployment of the system. Testing the system in a real-life scenario allowed us to identify a number of tricks and conditions that should to be taken into account since the initial steps, but the lab experimentation alone can barely help to focus on.

Keyword

Fall detection Depth sensor Machine learning 

References

  1. 1.
    AAL Joint Platform: ICT for ageing well. http://www.aal-europe.eu/about/why-thisprogramme/
  2. 2.
    Hillcoat-Nallétamby S (2014) The meaning of independence for older people in different residential settings. J Gerontol Ser B 69(3):419–430.  https://doi.org/10.1093/geronb/gbu008CrossRefGoogle Scholar
  3. 3.
    Rashidi P, Mihailidis A (2013) A survey on ambient-assisted living tools for older adults. IEEE J Biomed Health Inf 17(3):579–590CrossRefGoogle Scholar
  4. 4.
    World Health Organization. Falls: fact sheet. http://www.who.int/en/newsroom/fact-sheets/detail/falls. Accessed on May 3rd, 2018
  5. 5.
    Delahoz YS, Labrador MA (2014) Survey on fall detection and fall prevention using wearable and external sensors. Sensors 14(10):19806–19842CrossRefGoogle Scholar
  6. 6.
    Turner S, Kisser R, Rogmans W (2015) Falls among older adults in the EU-28: key facts from the available statistics. EuroSafe, AmsterdamGoogle Scholar
  7. 7.
    Chaccour K, Darazi R, El Hassani AH, Andrès E (2017) From fall detection to fall prevention: a generic classification of fall-related systems. IEEE Sens J 17(3):812–822CrossRefGoogle Scholar
  8. 8.
    Droghini D, Principi E, Squartini S, Olivetti P, PiazzaF (2018) Human fall detection by using an innovative floor acoustic sensor. In: Smart innovation, systems and technologies, pp 97–107.  https://doi.org/10.1007/978-3-319-56904-8Google Scholar
  9. 9.
    Cippitelli E, Fioranelli F, Gambi E, Spinsante S (2017) Radar and RGB-depth sensors for fall detection: a review. IEEE Sens J 17(12):3585–3604CrossRefGoogle Scholar
  10. 10.
    Gasparrini S, Cippitelli E, Spinsante S, Gambi E (2014) A depth-based fall detection system using a kinect sensor. Sensors 14(2):2756–2775CrossRefGoogle Scholar
  11. 11.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Susanna Spinsante
    • 1
    Email author
  • Marco Fagiani
    • 2
  • Marco Severini
    • 2
  • Stefano Squartini
    • 1
  • Friedrich Ellmenreich
    • 3
  • Giusy Martelli
    • 3
  1. 1.Dipartimento di Ingegneria dell’InformazioneUniversità Politecnica delle MarcheAnconaItaly
  2. 2.DowSee SrlFabrianoItaly
  3. 3.CareWatch SrlBolzanoItaly

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