Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home People Article Swipe
Fabien Buisseret
,
Louis Catinus
,
Rémi Grenard
,
Laurent Jojczyk
,
Dylan Fievez
,
Vincent Barvaux
,
Frédéric Dierick
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20113207
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20113207
Assessing the risk of fall in elderly people is a difficult challenge for clinicians. Since falls represent one of the first causes of death in such people, numerous clinical tests have been created and validated over the past 30 years to ascertain the risk of falls. More recently, the developments of low-cost motion capture sensors have facilitated observations of gait differences between fallers and nonfallers. The aim of this study is twofold. First, to design a method combining clinical tests and motion capture sensors in order to optimize the prediction of the risk of fall. Second to assess the ability of artificial intelligence to predict risk of fall from sensor raw data only. Seventy-three nursing home residents over the age of 65 underwent the Timed Up and Go (TUG) and six-minute walking tests equipped with a home-designed wearable Inertial Measurement Unit during two sets of measurements at a six-month interval. Observed falls during that interval enabled us to divide residents into two categories: fallers and nonfallers. We show that the TUG test results coupled to gait variability indicators, measured during a six-minute walking test, improve (from 68% to 76%) the accuracy of risk of fall’s prediction at six months. In addition, we show that an artificial intelligence algorithm trained on the sensor raw data of 57 participants reveals an accuracy of 75% on the remaining 16 participants.
Related Topics
Concepts
Wearable computer
Inertial measurement unit
Gait
Physical medicine and rehabilitation
Timed Up and Go test
Fall prevention
Wearable technology
Computer science
Motion capture
Test (biology)
Artificial intelligence
Poison control
Medicine
Physical therapy
Machine learning
Simulation
Injury prevention
Motion (physics)
Medical emergency
Balance (ability)
Paleontology
Embedded system
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20113207
- https://www.mdpi.com/1424-8220/20/11/3207/pdf?version=1591762801
- OA Status
- gold
- Cited By
- 84
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3033755023
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3033755023Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20113207Digital Object Identifier
- Title
-
Timed Up and Go and Six-Minute Walking Tests with Wearable Inertial Sensor: One Step Further for the Prediction of the Risk of Fall in Elderly Nursing Home PeopleWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-06-05Full publication date if available
- Authors
-
Fabien Buisseret, Louis Catinus, Rémi Grenard, Laurent Jojczyk, Dylan Fievez, Vincent Barvaux, Frédéric DierickList of authors in order
- Landing page
-
https://doi.org/10.3390/s20113207Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/11/3207/pdf?version=1591762801Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/11/3207/pdf?version=1591762801Direct OA link when available
- Concepts
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Wearable computer, Inertial measurement unit, Gait, Physical medicine and rehabilitation, Timed Up and Go test, Fall prevention, Wearable technology, Computer science, Motion capture, Test (biology), Artificial intelligence, Poison control, Medicine, Physical therapy, Machine learning, Simulation, Injury prevention, Motion (physics), Medical emergency, Balance (ability), Paleontology, Embedded system, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
84Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 15, 2024: 23, 2023: 13, 2022: 22, 2021: 9Per-year citation counts (last 5 years)
- References (count)
-
43Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W1564856129, https://openalex.org/W2047179519, https://openalex.org/W2060762518, https://openalex.org/W1974704788, https://openalex.org/W2157956359, https://openalex.org/W1550534467, https://openalex.org/W2045271086, https://openalex.org/W2167271993, https://openalex.org/W4245666729, https://openalex.org/W2115719325, https://openalex.org/W2072183979, https://openalex.org/W3004896940, https://openalex.org/W2498490541, https://openalex.org/W2914795193, https://openalex.org/W2766476110, https://openalex.org/W2606264724, https://openalex.org/W4299429830, https://openalex.org/W2129447992, https://openalex.org/W6674385629, https://openalex.org/W166349553, https://openalex.org/W2116516133, https://openalex.org/W2061808471, https://openalex.org/W1963242519, https://openalex.org/W2016178226, https://openalex.org/W2086456518, https://openalex.org/W2072863751, https://openalex.org/W2037876759, https://openalex.org/W2102568183, https://openalex.org/W2025897929, https://openalex.org/W2124705365, https://openalex.org/W2782627175, https://openalex.org/W2335125833, https://openalex.org/W6606666695, https://openalex.org/W2042805584, https://openalex.org/W2322968233, https://openalex.org/W2983704835, https://openalex.org/W2928318647, https://openalex.org/W3004787993, https://openalex.org/W2002558805, https://openalex.org/W2956733506, https://openalex.org/W2109476107, https://openalex.org/W2097998348, https://openalex.org/W3124661333 |
| referenced_works_count | 43 |
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| corresponding_author_ids | https://openalex.org/A5007379406 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 7 |
| corresponding_institution_ids | https://openalex.org/I4210144605, https://openalex.org/I4210161702, https://openalex.org/I95674353 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6700000166893005 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99543811 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |