Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data

In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquire...

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Vydáno v:	International journal of precision engineering and manufacturing Ročník 21; číslo 4; s. 725 - 737
Hlavní autoři:	Lee, Chang Min, Park, Jisu, Park, Shinsuk, Kim, Choong Hyun
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Seoul Korean Society for Precision Engineering 01.04.2020 Springer Nature B.V
Témata:	Algorithms Data acquisition Data points Decision analysis Decision trees Engineering Fall detection Industrial and Production Engineering Inertial platforms Lead time Materials Science Performance evaluation Plantar pressure Pressure measurement Regular Paper Center of pressure Force sensing resistor Activities of daily living Inertial measurement unit Decision tree Fall detection
ISSN:	2234-7593, 2005-4602
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Abstract	In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquired data with the threshold method and the decision-tree method. In general, the decision-tree method shows better performance than the threshold method, and the fall-detection accuracy increases when the acceleration and center-of-pressure (COP) data are used together, rather than when each data point is used separately. The results show that the fall-detection algorithm that applies both acceleration and COP data to the decision-tree method has a fall-detection accuracy of 95% or higher and a sufficient lead time of 317 ms on average.
AbstractList	In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquired data with the threshold method and the decision-tree method. In general, the decision-tree method shows better performance than the threshold method, and the fall-detection accuracy increases when the acceleration and center-of-pressure (COP) data are used together, rather than when each data point is used separately. The results show that the fall-detection algorithm that applies both acceleration and COP data to the decision-tree method has a fall-detection accuracy of 95% or higher and a sufficient lead time of 317 ms on average. In this study, experiments are conducted for four types of falls and eight types of activities of daily living with an integrated sensor system that uses both an inertial measurement unit and a plantar-pressure measurement unit and the fall-detection performance is evaluated by analyzing the acquired data with the threshold method and the decision-tree method. In general, the decision-tree method shows better performance than the threshold method, and the fall-detection accuracy increases when the acceleration and center-of-pressure (COP) data are used together, rather than when each data point is used separately. The results show that the fall-detection algorithm that applies both acceleration and COP data to the decision-tree method has a fall-detection accuracy of 95% or higher and a sufficient lead time of 317 ms on average.
Author	Kim, Choong Hyun Park, Jisu Lee, Chang Min Park, Shinsuk
Author_xml	– sequence: 1 givenname: Chang Min surname: Lee fullname: Lee, Chang Min organization: Center for Bionics, Korea Institute of Science and Technology, Mechanical Engineering Department, Korea University – sequence: 2 givenname: Jisu surname: Park fullname: Park, Jisu organization: Center for Bionics, Korea Institute of Science and Technology – sequence: 3 givenname: Shinsuk surname: Park fullname: Park, Shinsuk organization: Mechanical Engineering Department, Korea University – sequence: 4 givenname: Choong Hyun orcidid: 0000-0002-0162-6342 surname: Kim fullname: Kim, Choong Hyun email: nems.kim@gmail.com organization: Center for Bionics, Korea Institute of Science and Technology
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Cites_doi	10.1109/TNSRE.2007.916282 10.1016/S0021-9290(97)00114-0 10.1016/j.jbiomech.2008.03.042 10.1016/j.medengphy.2006.12.001 10.1007/s11517-016-1504-y 10.1016/j.gaitpost.2008.01.003 10.1109/JSEN.2004.823671 10.1016/j.jsr.2016.05.001 10.1109/TNSRE.2014.2357806 10.1109/TITB.2009.2033673 10.1109/ICOSP.2010.5656840 10.1109/EMBC.2016.7591534 10.1016/j.asoc.2015.10.062 10.1109/TITB.2009.2038904 10.1016/S0021-9290(00)00200-1 10.1016/j.gaitpost.2006.09.012 10.1007/s12541-017-0081-9 10.1016/j.medengphy.2007.12.003 10.1007/s12541-018-0038-7
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IEEE. – reference: BurnsERStevensJALeeRThe direct costs of fatal and non-fatal falls among older adults—United StatesJournal of safety Research2016589910310.1016/j.jsr.2016.05.001 – reference: TamuraTA wearable airbag to prevent fall injuriesIEEE Transactions on Information Technology in Biomedicine200913691091410.1109/TITB.2009.2033673 – reference: GibsonRMMultiple comparator classifier framework for accelerometer-based fall detection and diagnosticApplied Soft Computing2016399410310.1016/j.asoc.2015.10.062 – reference: Bourke, A. K., et al. (2016). Fall detection algorithms for real-world falls harvested from lumbar sensors in the elderly population: A machine learning approach. In 2016 IEEE 38th annual international conference of the engineering in medicine and biology society (EMBC). 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A. (2009). Anthropometry. In Biomechanics and motor control of human movement (pp. 82–83). 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SubjectTerms	Algorithms Data acquisition Data points Decision analysis Decision trees Engineering Fall detection Industrial and Production Engineering Inertial platforms Lead time Materials Science Performance evaluation Plantar pressure Pressure measurement Regular Paper
Title	Fall-Detection Algorithm Using Plantar Pressure and Acceleration Data
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