ArticlePloS one2019
Walking-speed estimation using a single inertial measurement unit for the older adults.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 48 citations in OpenAlex.
- Development of a Wearable Sensor Platform for Fall Risk Assessment and Fall Detection.Sensors (Basel, Switzerland) · 2026Article
- Estimation of walking energy expenditure using a single sacrum-mounted IMU based on biomechanically-informed machine learning.Scientific reports · 2025Article
- Validity and reliability of inertial measurement units on gait, static balance and functional mobility performance among community-dwelling older adults: a systematic review and meta-analysis.EFORT open reviews · 2025Article
- Neural control of anticipatory braking and lateral balance predicts fall risk in older adults: BMI-dependent mechanisms.Frontiers in aging neuroscience · 2025Article
- A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.NPJ digital medicine · 2024Article
- Beyond gait speed: exploring the added value of Inertial Measurement Unit-based measurements of gait in the estimation of the walking ability in daily life.BMC neurology · 2024Article
- Vitamin D and myopia: a review.International ophthalmology · 2024Review
- Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device.Scientific reports · 2024Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- A Comparison of Walking Behavior during the Instrumented TUG and Habitual Gait.Sensors (Basel, Switzerland) · 2023Article
- Development of a Gait Feature-Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor.Neurology · 2023Article
- Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium.Journal of neuroengineering and rehabilitation · 2023Article
- Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults.Sensors (Basel, Switzerland) · 2022Article
- Development of a gait speed estimation model for healthy older adults using a single inertial measurement unit.PloS one · 2022Article
- A Pilot Study to Validate a Wearable Inertial Sensor for Gait Assessment in Older Adults with Falls.Sensors (Basel, Switzerland) · 2021Article
- Orientation-Invariant Spatio-Temporal Gait Analysis Using Foot-Worn Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
- Validation of Walking Speed Estimation from Trunk Mounted Accelerometers for a Range of Walking Speeds.Sensors (Basel, Switzerland) · 2021Article
- A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data.Healthcare (Basel, Switzerland) · 2021Article
- Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough walking speed is associated with important clinical outcomes and designated as the sixth vital sign of the elderly, few walking-speed estimation algorithms using an inertial measurement unit (IMU) have been derived and tested in the older adults, especially in the elderly with slow speed. We aimed to develop a walking-speed estimation algorithm for older adults based on an IMU.
methodsWe used data from 659 of 785 elderly enrolled from the cohort study. We measured gait using an IMU attached on the lower back while participants walked around a 28 m long round walkway thrice at comfortable paces. Best-fit linear regression models were developed using selected demographic, anthropometric, and IMU features to estimate the walking speed. The accuracy of the algorithm was verified using mean absolute error (MAE) and root mean square error (RMSE) in an independent validation set. Additionally, we verified concurrent validity with GAITRite using intraclass correlation coefficients (ICCs).
resultsThe proposed algorithm incorporates the age, sex, foot length, vertical displacement, cadence, and step-time variability obtained from an IMU sensor. It exhibited high estimation accuracy for the walking speed of the elderly and remarkable concurrent validity compared to the GAITRite (MAE = 4.70%, RMSE = 6.81 𝑐𝑚/𝑠, concurrent validity (ICC (3,1)) = 0.937). Moreover, it achieved high estimation accuracy even for slow walking by applying a slow-speed-specific regression model sequentially after estimation by a general regression model. The accuracy was higher than those obtained with models based on the human gait model with or without calibration to fit the population.
conclusionsThe developed inertial-sensor-based walking-speed estimation algorithm can accurately estimate the walking speed of older adults.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.