Machine Learning-Based Predicted Age of the Elderly on the Instrumented Timed Up and Go Test and Six-Minute Walk Test.

Szczegóły
Abstrakt

Tytuł:: Machine Learning-Based Predicted Age of the Elderly on the Instrumented Timed Up and Go Test and Six-Minute Walk Test.
Autorzy:: Ko JB; Digital Healthcare R&D Department, Korea Institute of Industrial Technology, Cheonan 31056, Korea.
Hong JS; Digital Healthcare R&D Department, Korea Institute of Industrial Technology, Cheonan 31056, Korea.
Shin YS; Digital Healthcare R&D Department, Korea Institute of Industrial Technology, Cheonan 31056, Korea.
Kim KB; Digital Healthcare R&D Department, Korea Institute of Industrial Technology, Cheonan 31056, Korea.
Źródło:: Sensors (Basel, Switzerland) [Sensors (Basel)] 2022 Aug 09; Vol. 22 (16). Date of Electronic Publication: 2022 Aug 09.
Typ publikacji:: Journal Article
Język:: English
Imprint Name(s):: Original Publication: Basel, Switzerland : MDPI, c2000-
MeSH Terms:: Gait*
Postural Balance*
Aged ; Humans ; Machine Learning ; Time and Motion Studies ; Walk Test
References:: Am J Phys Med Rehabil. 2002 Nov;81(11):857-66; quiz 867-8. (PMID: 12394998)
Physiother Theory Pract. 2017 Oct;33(10):766-771. (PMID: 28771062)
J Am Med Dir Assoc. 2016 Feb;17(2):162-7. (PMID: 26577625)
Phys Ther. 2006 May;86(5):646-55. (PMID: 16649889)
Sensors (Basel). 2021 Mar 29;21(7):. (PMID: 33805368)
J Biomech Eng. 2018 Dec 5;:. (PMID: 30516241)
Sensors (Basel). 2019 Jan 22;19(3):. (PMID: 30678268)
Br Med J (Clin Res Ed). 1982 May 29;284(6329):1607-8. (PMID: 6805625)
J Am Geriatr Soc. 2014 Apr;62(4):673-8. (PMID: 24635699)
Clin Biomech (Bristol, Avon). 2019 Oct;69:109-114. (PMID: 31330459)
IEEE J Biomed Health Inform. 2018 Jan;22(1):147-153. (PMID: 28333650)
J Gerontol. 1990 Nov;45(6):S229-37. (PMID: 2146312)
J Clin Med. 2020 May 21;9(5):. (PMID: 32455757)
Comput Methods Biomech Biomed Engin. 2015;18(4):426-37. (PMID: 23964848)
J Phys Ther Sci. 2015 Nov;27(11):3571-8. (PMID: 26696740)
JAMA. 1963 Sep 21;185:914-9. (PMID: 14044222)
Gait Posture. 2003 Aug;18(1):35-46. (PMID: 12855299)
PLoS One. 2016 Jun 06;11(6):e0155984. (PMID: 27271994)
J Am Geriatr Soc. 1991 Feb;39(2):142-8. (PMID: 1991946)
Sensors (Basel). 2017 Apr 23;17(4):. (PMID: 28441748)
Med Eng Phys. 2014 Jun;36(6):779-85. (PMID: 24636448)
J Biomech. 2013 Feb 22;46(4):828-31. (PMID: 23317758)
Phys Ther. 2002 Feb;82(2):128-37. (PMID: 11856064)
J Gerontol A Biol Sci Med Sci. 2013 Apr;68(4):441-6. (PMID: 22987796)
Sensors (Basel). 2021 Dec 27;22(1):. (PMID: 35009706)
J Biomech. 2018 Jun 25;75:102-107. (PMID: 29779829)
Med Sci Sports Exerc. 2003 Jan;35(1):169-74. (PMID: 12544651)
J Gerontol A Biol Sci Med Sci. 2011 Jan;66(1):89-96. (PMID: 20861145)
Am J Respir Crit Care Med. 2002 Jul 1;166(1):111-7. (PMID: 12091180)
J Gerontol A Biol Sci Med Sci. 2002 Nov;57(11):M751-6. (PMID: 12403805)
Appl Ergon. 2016 Jan;52:275-84. (PMID: 26360219)
Sensors (Basel). 2015 Sep 04;15(9):22451-72. (PMID: 26404302)
J Neuroeng Rehabil. 2014 Jul 04;11:109. (PMID: 24993146)
Gait Posture. 2005 Jan;21(1):48-58. (PMID: 15536033)
Gait Posture. 2014;39(1):553-7. (PMID: 24139685)
Age Ageing. 2014 May;43(3):406-11. (PMID: 24212918)
Exp Aging Res. 2018 May-Jun;44(3):258-269. (PMID: 29558320)
Sensors (Basel). 2015 Dec 11;15(12):31314-38. (PMID: 26690450)
J Biomech. 2004 Jan;37(1):121-6. (PMID: 14672575)
Proc Natl Acad Sci U S A. 1991 Mar 15;88(6):2297-301. (PMID: 11607165)
J Gerontol A Biol Sci Med Sci. 2011 Jan;66(1):136-41. (PMID: 20923910)
J Neuroeng Rehabil. 2015 May 24;12:48. (PMID: 26003560)
Appl Ergon. 2021 Oct;96:103414. (PMID: 34087702)
Sensors (Basel). 2020 Mar 29;20(7):. (PMID: 32235310)
BMC Musculoskelet Disord. 2013 Apr 24;14:145. (PMID: 23617377)
PLoS One. 2015 Jul 23;10(7):e0130851. (PMID: 26203909)
Congest Heart Fail. 2010 Sep-Oct;16(5):208-13. (PMID: 20887617)
J Am Geriatr Soc. 1989 Mar;37(3):267-71. (PMID: 2645355)
Grant Information:: KITECH EO-22-0001 Korea Institute of Industrial Technology
Contributed Indexing:: Keywords: XAI; dynamic balance ability; inertial measurement unit (IMU); six-minute walk test; timed up and go test (TUG)
Entry Date(s):: Date Created: 20220826 Date Completed: 20220829 Latest Revision: 20220830
Update Code:: 20240104
PubMed Central ID:: PMC9413258
DOI:: 10.3390/s22165957
PMID:: 36015714
: Czasopismo naukowe

Pełny tekst

A decrease in dynamic balance ability (DBA) in the elderly is closely associated with aging. Various studies have investigated different methods to quantify the DBA in the elderly through DBA evaluation methods such as the timed up and go test (TUG) and the six-minute walk test (6MWT), applying the G-Walk wearable system. However, these methods have generally been difficult for the elderly to intuitively understand. The goal of this study was thus to generate a regression model based on machine learning (ML) to predict the age of the elderly as a familiar indicator. The model was based on inertial measurement unit (IMU) data as part of the DBA evaluation, and the performance of the model was comparatively analyzed with respect to age prediction based on the IMU data of the TUG test and the 6MWT. The DBA evaluation used the TUG test and the 6MWT performed by 136 elderly participants. When performing the TUG test and the 6MWT, a single IMU was attached to the second lumbar spine of the participant, and the three-dimensional linear acceleration and gyroscope data were collected. The features used in the ML-based regression model included the gait symmetry parameters and the harmonic ratio applied in quantifying the DBA, in addition to the features of description statistics for IMU signals. The feature set was differentiated between the TUG test and the 6MWT, and the performance of the regression model was comparatively analyzed based on the feature sets. The XGBoost algorithm was used to train the regression model. Comparison of the regression model performance according to the TUG test and 6MWT feature sets showed that the performance was best for the model using all features of the TUG test and the 6MWT. This indicated that the evaluation of DBA in the elderly should apply the TUG test and the 6MWT concomitantly for more accurate predictions. The findings in this study provide basic data for the development of a DBA monitoring system for the elderly.
Competing Interests: The authors declare no conflict of interest.

Informacja