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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Published in:	International journal of precision engineering and manufacturing Vol. 21; no. 4; pp. 725 - 737
Main Authors:	Lee, Chang Min, Park, Jisu, Park, Shinsuk, Kim, Choong Hyun
Format:	Journal Article
Language:	English
Published:	Seoul Korean Society for Precision Engineering 01.04.2020 Springer Nature B.V
Subjects:	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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References_xml	– reference: PappasIPIA reliable gyroscope-based gait-phase detection sensor embedded in a shoe insoleIEEE Sensors Journal20044226827410.1109/JSEN.2004.823671 – reference: LeeJKRobinovitchSNParkEJInertial sensing-based pre-impact detection of falls involving near-fall scenariosIEEE Transactions on Neural Systems and Rehabilitation Engineering201523225826610.1109/TNSRE.2014.2357806 – reference: KangasMComparison of low-complexity fall detection algorithms for body attached accelerometersGait & Posture200828228529110.1016/j.gaitpost.2008.01.003 – reference: BourkeAKO’brienJVLyonsGMEvaluation of a threshold-based tri-axial accelerometer fall detection algorithmGait & posture200726219419910.1016/j.gaitpost.2006.09.012 – reference: Shan, S., & Yuan, T. (2010). A wearable pre-impact fall detector using feature selection and support vector machine. In 2010 IEEE 10th international conference on signal processing (ICSP). 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). New Jersey: Wiley. – reference: WHOWHO global report on falls: prevention in older age2007GenevaWorld Health Organization – volume: 16 start-page: 178 issue: 2 year: 2008 ident: 268_CR9 publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2007.916282 – volume: 31 start-page: 1 issue: 1 year: 1997 ident: 268_CR3 publication-title: Journal of Biomechanics doi: 10.1016/S0021-9290(97)00114-0 – volume: 41 start-page: 2297 issue: 10 year: 2008 ident: 268_CR4 publication-title: Journal of Biomechanics doi: 10.1016/j.jbiomech.2008.03.042 – volume: 30 start-page: 84 issue: 1 year: 2008 ident: 268_CR6 publication-title: Medical Engineering & Physics doi: 10.1016/j.medengphy.2006.12.001 – volume: 55 start-page: 45 issue: 1 year: 2017 ident: 268_CR14 publication-title: Medical & Biological Engineering & Computing doi: 10.1007/s11517-016-1504-y – volume: 28 start-page: 285 issue: 2 year: 2008 ident: 268_CR8 publication-title: Gait & Posture doi: 10.1016/j.gaitpost.2008.01.003 – volume: 4 start-page: 268 issue: 2 year: 2004 ident: 268_CR16 publication-title: IEEE Sensors Journal doi: 10.1109/JSEN.2004.823671 – volume: 58 start-page: 99 year: 2016 ident: 268_CR2 publication-title: Journal of safety Research doi: 10.1016/j.jsr.2016.05.001 – volume: 23 start-page: 258 issue: 2 year: 2015 ident: 268_CR12 publication-title: IEEE Transactions on Neural Systems and Rehabilitation Engineering doi: 10.1109/TNSRE.2014.2357806 – volume: 13 start-page: 910 issue: 6 year: 2009 ident: 268_CR10 publication-title: IEEE Transactions on Information Technology in Biomedicine doi: 10.1109/TITB.2009.2033673 – ident: 268_CR20 – volume-title: WHO global report on falls: prevention in older age year: 2007 ident: 268_CR1 – ident: 268_CR11 doi: 10.1109/ICOSP.2010.5656840 – ident: 268_CR13 doi: 10.1109/EMBC.2016.7591534 – volume: 39 start-page: 94 year: 2016 ident: 268_CR15 publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.10.062 – volume: 14 start-page: 767 issue: 3 year: 2010 ident: 268_CR17 publication-title: IEEE Transactions on Information Technology in Biomedicine doi: 10.1109/TITB.2009.2038904 – volume: 34 start-page: 309 issue: 3 year: 2001 ident: 268_CR21 publication-title: Journal of Biomechanics doi: 10.1016/S0021-9290(00)00200-1 – volume: 26 start-page: 194 issue: 2 year: 2007 ident: 268_CR5 publication-title: Gait & posture doi: 10.1016/j.gaitpost.2006.09.012 – volume: 18 start-page: 681 issue: 5 year: 2017 ident: 268_CR19 publication-title: International Journal of Precision Engineering and Manufacturing doi: 10.1007/s12541-017-0081-9 – volume: 30 start-page: 937 issue: 7 year: 2008 ident: 268_CR7 publication-title: Medical Engineering & Physics doi: 10.1016/j.medengphy.2007.12.003 – volume: 19 start-page: 309 issue: 2 year: 2018 ident: 268_CR18 publication-title: International Journal of Precision Engineering and Manufacturing doi: 10.1007/s12541-018-0038-7
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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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