Development of a gait speed estimation model for healthy older adults using a single inertial measurement unit Article Swipe
Hyang Jun Lee
,
Ji Sun Park
,
Jong Bin Bae
,
Ji Won Han
,
Ki Woong Kim
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0275612
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0275612
Although gait speed changes are associated with various geriatric conditions, standard gait analysis systems, such as laboratory-based motion capture systems or instrumented walkways, are too expensive, spatially limited, and difficult to access. A wearable inertia sensor is cheap and easy to access; however, its accuracy in estimating gait speed is limited. In this study, we developed a model for accurately estimating the gait speed of healthy older adults using the data captured by an inertia sensor placed at their center of body mass (CoM). We enrolled 759 healthy older adults from two population-based cohort studies and asked them to walk on a 14 m long walkway thrice at comfortable paces with an inertia sensor attached to their CoM. In the middle of the walkway, we placed GAITRite ™ to obtain the gold standard of gait speed. We then divided the participants into three subgroups using the normalized step length and developed a linear regression model for estimating the gold standard gait speed using age, foot length, and the features obtained from an inertia sensor, including cadence, vertical height displacement, yaw angle, and role angle of CoM. Our model exhibited excellent accuracy in estimating the gold standard gait speed (mean absolute error = 3.74%; root mean square error = 5.30 cm/s ; intraclass correlation coefficient = 0.954). Our model may contribute to the early detection and monitoring of gait disorders and other geriatric conditions by making gait assessment easier, cheaper, and more ambulatory while remaining as accurate as other standard gait analysis systems.
Related Topics
Concepts
Gait
Gold standard (test)
Cadence
Gait analysis
Standard error
Inertial measurement unit
Preferred walking speed
Effect of gait parameters on energetic cost
Intraclass correlation
Physical medicine and rehabilitation
Accelerometer
Population
Simulation
Medicine
Computer science
Statistics
Mathematics
Artificial intelligence
Reproducibility
Operating system
Environmental health
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1371/journal.pone.0275612
- OA Status
- gold
- Cited By
- 4
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4302363781
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4302363781Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1371/journal.pone.0275612Digital Object Identifier
- Title
-
Development of a gait speed estimation model for healthy older adults using a single inertial measurement unitWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-06Full publication date if available
- Authors
-
Hyang Jun Lee, Ji Sun Park, Jong Bin Bae, Ji Won Han, Ki Woong KimList of authors in order
- Landing page
-
https://doi.org/10.1371/journal.pone.0275612Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1371/journal.pone.0275612Direct OA link when available
- Concepts
-
Gait, Gold standard (test), Cadence, Gait analysis, Standard error, Inertial measurement unit, Preferred walking speed, Effect of gait parameters on energetic cost, Intraclass correlation, Physical medicine and rehabilitation, Accelerometer, Population, Simulation, Medicine, Computer science, Statistics, Mathematics, Artificial intelligence, Reproducibility, Operating system, Environmental healthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2023: 2Per-year citation counts (last 5 years)
- References (count)
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42Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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