Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults
Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and ana...
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| Published in | Sensors (Basel, Switzerland) Vol. 19; no. 6; p. 1357 |
|---|---|
| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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Switzerland
MDPI
18.03.2019
MDPI AG |
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| Online Access | Get full text |
| ISSN | 1424-8220 1424-8220 |
| DOI | 10.3390/s19061357 |
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| Abstract | Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively. |
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| AbstractList | Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor “AIDE-MOI” was developed. “AIDE-MOI” senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as “fall” or “non-fall”. The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively. Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor "AIDE-MOI" was developed. "AIDE-MOI" senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as "fall" or "non-fall". The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively.Falls are the primary contributors of accidents in elderly people. An important factor of fall severity is the amount of time that people lie on the ground. To minimize consequences through a short reaction time, the motion sensor "AIDE-MOI" was developed. "AIDE-MOI" senses acceleration data and analyzes if an event is a fall. The threshold-based fall detection algorithm was developed using motion data of young subjects collected in a lab setup. The aim of this study was to improve and validate the existing fall detection algorithm. In the two-phase study, twenty subjects (age 86.25 ± 6.66 years) with a high risk of fall (Morse > 65 points) were recruited to record motion data in real-time using the AIDE-MOI sensor. The data collected in the first phase (59 days) was used to optimize the existing algorithm. The optimized second-generation algorithm was evaluated in a second phase (66 days). The data collected in the two phases, which recorded 31 real falls, was split-up into one-minute chunks for labelling as "fall" or "non-fall". The sensitivity and specificity of the threshold-based algorithm improved significantly from 27.3% to 80.0% and 99.9957% (0.43) to 99.9978% (0.17 false alarms per week and subject), respectively. |
| Author | Scheurer, Simon Kucera, Martin Urwyler, Prabitha Bryn, Hȧkon Koch, Janina Meerstetter, Tobias Bärtschi, Marcel Nef, Tobias |
| AuthorAffiliation | 2 Oxomed AG, 3097 Liebefeld, Switzerland 4 ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland 3 Gerontechnology and Rehabilitation Group, University of Bern, 3008 Bern, Switzerland; tobias.nef@artorg.unibe.ch 5 University Neurorehabilitation Unit, Department of Neurology, University Hospital Inselspital, 3010 Bern, Switzerland 1 Department of Engineering and Information Technology, Bern University of Applied Sciences, 3401 Burgdorf, Switzerland; simon.scheurer@oxomed.ch (S.S.); janina.koch@oxomed.ch (J.K.); martin.kucera@bfh.ch (M.K.); haakon.bryn@gmail.com (H.B.); marcel.baertschi@gmail.com (M.B.); tm@oxon.ch (T.M.) |
| AuthorAffiliation_xml | – name: 2 Oxomed AG, 3097 Liebefeld, Switzerland – name: 4 ARTORG Center for Biomedical Engineering Research, University of Bern, 3008 Bern, Switzerland – name: 1 Department of Engineering and Information Technology, Bern University of Applied Sciences, 3401 Burgdorf, Switzerland; simon.scheurer@oxomed.ch (S.S.); janina.koch@oxomed.ch (J.K.); martin.kucera@bfh.ch (M.K.); haakon.bryn@gmail.com (H.B.); marcel.baertschi@gmail.com (M.B.); tm@oxon.ch (T.M.) – name: 3 Gerontechnology and Rehabilitation Group, University of Bern, 3008 Bern, Switzerland; tobias.nef@artorg.unibe.ch – name: 5 University Neurorehabilitation Unit, Department of Neurology, University Hospital Inselspital, 3010 Bern, Switzerland |
| Author_xml | – sequence: 1 givenname: Simon surname: Scheurer fullname: Scheurer, Simon – sequence: 2 givenname: Janina orcidid: 0000-0001-5064-5243 surname: Koch fullname: Koch, Janina – sequence: 3 givenname: Martin surname: Kucera fullname: Kucera, Martin – sequence: 4 givenname: Hȧkon surname: Bryn fullname: Bryn, Hȧkon – sequence: 5 givenname: Marcel surname: Bärtschi fullname: Bärtschi, Marcel – sequence: 6 givenname: Tobias surname: Meerstetter fullname: Meerstetter, Tobias – sequence: 7 givenname: Tobias surname: Nef fullname: Nef, Tobias – sequence: 8 givenname: Prabitha orcidid: 0000-0002-7641-8898 surname: Urwyler fullname: Urwyler, Prabitha |
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| Cites_doi | 10.1007/s00391-013-0559-8 10.1093/geront/10.1_Part_1.20 10.1016/j.gaitpost.2006.09.012 10.1111/j.1532-5415.2005.53455.x 10.1017/CBO9780511722233 10.1109/EMBC.2016.7590763 10.1093/ageing/afj066 10.1016/j.eswa.2014.06.045 10.1111/jgs.13708 10.1007/s11071-015-2571-6 10.1016/j.medengphy.2016.10.014 10.3390/s141018543 10.1016/j.asoc.2015.10.062 10.1159/000362720 10.1007/978-3-319-47075-7_54 10.1109/TITB.2012.2185851 10.1519/JPT.0b013e3182abe779 10.1109/TIM.2014.2385144 10.1109/EMBC.2016.7591534 10.1007/s12652-017-0592-3 10.1371/journal.pone.0037062 10.1097/00007611-199509000-00006 10.1016/j.medengphy.2010.11.003 10.1371/journal.pone.0036556 10.1016/j.medengphy.2006.12.001 10.1111/j.1532-5415.2005.53221.x 10.1136/bmj.a2227 10.1001/jama.1993.03500010075035 10.1093/ageing/14.3.174 10.1001/jama.1963.03060120024016 10.1016/j.rehab.2011.07.962 10.1111/j.1532-5415.1995.tb07017.x 10.3390/s18072060 10.1046/j.1365-2702.2002.00578.x 10.1109/IEMBS.2008.4649396 10.3390/s140610691 10.1109/TNSRE.2014.2357806 10.3390/s18051613 10.1016/j.gaitpost.2011.11.016 10.4108/eai.28-9-2015.2261462 10.1109/IEMBS.2007.4352627 10.3109/10903127.2013.856504 10.3390/s17020307 10.18517/ijaseit.7.6.4467 10.2147/PPA.S119177 10.1016/j.dcan.2015.12.001 |
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| References | ref_50 ref_14 ref_12 Kangas (ref_29) 2012; 35 Fleming (ref_8) 2008; 337 King (ref_5) 1995; 43 Chaudhuri (ref_10) 2014; 37 ref_52 Roa (ref_51) 2012; 16 Bloch (ref_18) 2011; 54 Mallinson (ref_6) 1985; 14 Bourke (ref_19) 2007; 26 Vallabh (ref_9) 2017; 9 Valera (ref_15) 2014; 41 Fuller (ref_2) 2000; 61 ref_21 Jin (ref_25) 2016; 84 Katz (ref_41) 1970; 10 ref_28 Yang (ref_44) 2016; 2 Lee (ref_47) 2015; 23 ref_26 Ozdemir (ref_27) 2014; 14 Bourke (ref_46) 2008; 30 Schwendimann (ref_40) 2006; 35 Lipsitz (ref_17) 2016; 64 Simpson (ref_4) 2014; 18 Thilo (ref_33) 2017; 11 Lamb (ref_43) 2005; 53 Schwickert (ref_11) 2013; 46 ref_36 ref_35 ref_32 Khan (ref_13) 2017; 39 ref_31 Baglio (ref_22) 2015; 64 Roush (ref_7) 1995; 88 Kangas (ref_20) 2007; 2007 Klenk (ref_30) 2011; 33 ref_37 Myers (ref_39) 2002; 11 Gibson (ref_24) 2016; 39 Luque (ref_23) 2014; 14 ref_45 ref_1 Kangas (ref_16) 2015; 61 ref_49 ref_48 Katz (ref_42) 1963; 185 Scarpato (ref_34) 2017; 7 Tinetti (ref_3) 1993; 269 Nasreddine (ref_38) 2005; 53 |
| References_xml | – volume: 46 start-page: 706 year: 2013 ident: ref_11 article-title: Sturzerkennung mit am Körper getragenen Sensoren: Ein systematischer Review publication-title: Z. Gerontol. Geriatr. doi: 10.1007/s00391-013-0559-8 – volume: 10 start-page: 20 year: 1970 ident: ref_41 article-title: Progress in development of the index of ADL publication-title: Gerontologist doi: 10.1093/geront/10.1_Part_1.20 – volume: 26 start-page: 194 year: 2007 ident: ref_19 article-title: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm publication-title: Gait Posture doi: 10.1016/j.gaitpost.2006.09.012 – volume: 53 start-page: 1618 year: 2005 ident: ref_43 article-title: Development of a common outcome data set for fall injury prevention trials: The Prevention of Falls Network Europe consensus publication-title: J. Am. Geriatr. Soc. doi: 10.1111/j.1532-5415.2005.53455.x – ident: ref_1 doi: 10.1017/CBO9780511722233 – ident: ref_14 doi: 10.1109/EMBC.2016.7590763 – volume: 35 start-page: 311 year: 2006 ident: ref_40 article-title: Evaluation of the Morse Fall Scale in hospitalised patients publication-title: Age Ageing doi: 10.1093/ageing/afj066 – volume: 41 start-page: 7980 year: 2014 ident: ref_15 article-title: Fall detection based on the gravity vector using a wide-angle camera publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2014.06.045 – volume: 64 start-page: 365 year: 2016 ident: ref_17 article-title: Evaluation of an Automated Falls Detection Device in Nursing Home Residents publication-title: J. Am. Geriatr. Soc. doi: 10.1111/jgs.13708 – volume: 84 start-page: 1327 year: 2016 ident: ref_25 article-title: Modeling of nonlinear system based on deep learning framework publication-title: Nonlinear Dyn. doi: 10.1007/s11071-015-2571-6 – ident: ref_35 – volume: 39 start-page: 12 year: 2017 ident: ref_13 article-title: Review of fall detection techniques: A data availability perspective publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2016.10.014 – volume: 14 start-page: 18543 year: 2014 ident: ref_23 article-title: Comparison and characterization of android-based fall detection systems publication-title: Sensors doi: 10.3390/s141018543 – volume: 39 start-page: 94 year: 2016 ident: ref_24 article-title: Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2015.10.062 – volume: 61 start-page: 61 year: 2015 ident: ref_16 article-title: Sensitivity and false alarm rate of a fall sensor in long-term fall detection in the elderly publication-title: Gerontology doi: 10.1159/000362720 – ident: ref_28 doi: 10.1007/978-3-319-47075-7_54 – volume: 16 start-page: 264 year: 2012 ident: ref_51 article-title: Personalization and adaptation to the medium and context in a fall detection system publication-title: IEEE Trans. Inf. Technol. Biomed. doi: 10.1109/TITB.2012.2185851 – volume: 37 start-page: 178 year: 2014 ident: ref_10 article-title: Fall Detection Devices and their Use with Older Adults: A Systematic Review publication-title: J. Geriatr. Phys. Tehr. doi: 10.1519/JPT.0b013e3182abe779 – volume: 64 start-page: 1814 year: 2015 ident: ref_22 article-title: An Event Polarized Paradigm for ADL Detection in AAL Context publication-title: IEEE Trans. Instrum. Meas. doi: 10.1109/TIM.2014.2385144 – ident: ref_49 doi: 10.1109/EMBC.2016.7591534 – ident: ref_52 – volume: 9 start-page: 1809 year: 2017 ident: ref_9 article-title: Fall detection monitoring systems: A comprehensive review publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-017-0592-3 – ident: ref_12 doi: 10.1371/journal.pone.0037062 – volume: 88 start-page: 917 year: 1995 ident: ref_7 article-title: Impact of a personal emergency response system on hospital utilization by community-residing elders publication-title: South Med. J. doi: 10.1097/00007611-199509000-00006 – volume: 2007 start-page: 1367 year: 2007 ident: ref_20 article-title: Determination of simple thresholds for accelerometry-based parameters for fall detection publication-title: Conf. Proc. IEEE Eng. Med. Biol. Soc. – volume: 61 start-page: 2159 year: 2000 ident: ref_2 article-title: Falls in the elderly publication-title: Am. Fam. Phys. – volume: 33 start-page: 368 year: 2011 ident: ref_30 article-title: Comparison of acceleration signals of simulated and real-world backward falls publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2010.11.003 – ident: ref_26 doi: 10.1371/journal.pone.0036556 – volume: 30 start-page: 84 year: 2008 ident: ref_46 article-title: A threshold-based fall-detection algorithm using a bi-axial gyroscope sensor publication-title: Med. Eng. Phys. doi: 10.1016/j.medengphy.2006.12.001 – volume: 53 start-page: 695 year: 2005 ident: ref_38 article-title: The Montreal Cognitive Assessment, MoCA: A Brief Screening Tool For Mild Cognitive Impairment publication-title: J. Am. Geriatr. Soc. doi: 10.1111/j.1532-5415.2005.53221.x – volume: 337 start-page: 1279 year: 2008 ident: ref_8 article-title: Inability to get up after falling, subsequent time on floor, and summoning help: Prospective cohort study in people over 90 publication-title: BMJ doi: 10.1136/bmj.a2227 – volume: 269 start-page: 65 year: 1993 ident: ref_3 article-title: Predictors and prognosis of inability to get up after falls among elderly persons publication-title: JAMA doi: 10.1001/jama.1993.03500010075035 – volume: 14 start-page: 174 year: 1985 ident: ref_6 article-title: Covert muscle injury in aged patients admitted to hospital following falls publication-title: Age Ageing doi: 10.1093/ageing/14.3.174 – volume: 185 start-page: 914 year: 1963 ident: ref_42 article-title: Studies of Illness in the Aged. The Index of Adl: A Standardized Measure of Biological and Psychosocial Function publication-title: JAMA doi: 10.1001/jama.1963.03060120024016 – volume: 54 start-page: 391 year: 2011 ident: ref_18 article-title: Evaluation under real-life conditions of a stand-alone fall detector for the elderly subjects publication-title: Ann. Phys. Rehabil. Med. doi: 10.1016/j.rehab.2011.07.962 – volume: 43 start-page: 1146 year: 1995 ident: ref_5 article-title: Falls in community-dwelling older persons publication-title: J. Am. Geriatr. Soc. doi: 10.1111/j.1532-5415.1995.tb07017.x – ident: ref_31 doi: 10.3390/s18072060 – ident: ref_37 – volume: 11 start-page: 134 year: 2002 ident: ref_39 article-title: The sensitivity and specificity of the Morse Fall Scale in an acute care setting publication-title: J. Clin. Nurs. doi: 10.1046/j.1365-2702.2002.00578.x – ident: ref_50 doi: 10.1109/IEMBS.2008.4649396 – volume: 14 start-page: 10691 year: 2014 ident: ref_27 article-title: Detecting falls with wearable sensors using machine learning techniques publication-title: Sensors doi: 10.3390/s140610691 – volume: 23 start-page: 258 year: 2015 ident: ref_47 article-title: Inertial sensing-based pre-impact detection of falls involving near-fall scenarios publication-title: IEEE Trans. Neural Syst. Rehabil. Eng. doi: 10.1109/TNSRE.2014.2357806 – ident: ref_32 doi: 10.3390/s18051613 – volume: 35 start-page: 500 year: 2012 ident: ref_29 article-title: Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects publication-title: Gait Posture doi: 10.1016/j.gaitpost.2011.11.016 – ident: ref_48 doi: 10.4108/eai.28-9-2015.2261462 – ident: ref_21 doi: 10.1109/IEMBS.2007.4352627 – volume: 18 start-page: 185 year: 2014 ident: ref_4 article-title: Epidemiology of emergency medical service responses to older people who have fallen: A prospective cohort study publication-title: Prehosp. Emerg. Care doi: 10.3109/10903127.2013.856504 – ident: ref_36 – ident: ref_45 doi: 10.3390/s17020307 – volume: 7 start-page: 2328 year: 2017 ident: ref_34 article-title: E-health-IoT universe: A review publication-title: Int. J. Adv. Sci. Eng. Inf. Technol. doi: 10.18517/ijaseit.7.6.4467 – volume: 11 start-page: 11 year: 2017 ident: ref_33 article-title: Involvement of the end user: Exploration of older people’s needs and preferences for a wearable fall detection device–A qualitative descriptive study publication-title: Patient Prefer. Adherence doi: 10.2147/PPA.S119177 – volume: 2 start-page: 24 year: 2016 ident: ref_44 article-title: 3D depth image analysis for indoor fall detection of elderly people publication-title: Digit. Commun. Netw. doi: 10.1016/j.dcan.2015.12.001 |
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| SubjectTerms | fall detection healthcare sensors threshold algorithm wearable |
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| Title | Optimization and Technical Validation of the AIDE-MOI Fall Detection Algorithm in a Real-Life Setting with Older Adults |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/30889925 https://www.proquest.com/docview/2194585539 https://pubmed.ncbi.nlm.nih.gov/PMC6470846 https://doaj.org/article/ad1fa823b04942cea850202e0a5691fc |
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