Use of Wireless Smart Sensors for Detecting Human Falls through Structural Vibrations

  • Benjamin T. Davis
  • Juan M. Caicedo
  • Scott Langevin
  • Victor Hirth
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)


Falls are the leading cause of accidental deaths for people over the age of 65 because of the fall, fall related injury itself or related complications including hypothermia, dehydration, pressure sores and pneumonia. Several fall detection systems are commercially available including Life Alert, Life Link, and Alert One where the person wears a pendant that can be pressed in the case of an emergency or with newer models activates automatically when there is no motion. Pendant-based emergency systems become ineffective if the person is not wearing the pendant (refuses or forgets) or cannot press the pendant’s button, for example when falling in a prone position on top of the device. In addition, the elderly are hesitant to use emergency systems for several reasons such as the concern of bothering others and personal pride. This paper proposes the use of structural vibrations to determine if a person has fallen. An Imote2 and an ITS400CA sensor board are used for the collection of structural vibrations induced by human activity, including falls. These sensors are discrete, and have shown potential for the data collection and diagnostic processing needed to detect human falls. The use of wireless smart sensors in the structure provides a non-intrusive method for human fall detection that does not require the use of any device by the person. A preliminary study of the classification of human induced vibration in a typical structure using traditional wired sensors is also discussed as well as a sensing framework used to study structural vibrations induced by human falls.


Sensor Node Structural Health Monitoring Master Node Structural Vibration Fall Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Benjamin T. Davis
    • 1
  • Juan M. Caicedo
    • 1
  • Scott Langevin
    • 2
  • Victor Hirth
    • 3
  1. 1.University of South CarolinaColumbiaUSA
  2. 2.University of South CarolinaColumbiaUSA
  3. 3.University of South Carolina, School of MedicineColumbiaUSA

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