ArticleSensors (Basel, Switzerland)2021
Wearable Sensor-Based Prediction Model of Timed up and Go Test in Older Adults.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
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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, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- A Systematic Review of Wearable Sensor-Based Technologies for Fall Risk Assessment in Older Adults.Sensors (Basel, Switzerland) · 2022Pooled it
- Instrumented Walkway Gait Analysis Predicts Fallers in Neurological Disorders: Identifying Digital Biomarkers for Balance Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Wearable Technologies for Gait Instability Rehabilitation: Mechanisms, Clinical Evidence, and Future Directions.Bioengineering (Basel, Switzerland) · 2026Review
- Evaluation of the Importance of Stopping Elderly Accidents, Deaths, and Injuries (STEADI)-Based Factors in Wearable Fall Risk Assessment: Secondary Data Analysis.JMIR mHealth and uHealth · 2026Article
- Classification of fallers and non-fallers in older adults using electrical IMU signal for gait analysis and explainable deep learning.Scientific reports · 2026Article
- Database for Prevalence and Determinants of Frailty in the Elderly with Quantifying Functional Mobility.Scientific data · 2026Article
- Reliability of wearable sensors single and dual-task timed up and go test among community-dwelling older women.BMC geriatrics · 2025Article
- Kinematic Biomarkers of Functional Disability in Older Adults: Analysis of the Timed Up and Go Test.Biosensors · 2025Article
- Wearable Technologies for Health Promotion and Disease Prevention in Older Adults: Systematic Scoping Review and Evidence Map.Journal of medical Internet research · 2025Article
- Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.Medical & biological engineering & computing · 2024Article
- Wearable Technology Applications and Methods to Assess Clinical Outcomes in Foot and Ankle Disorders: Achievements and Perspectives.Sensors (Basel, Switzerland) · 2024Review
- 'Watkins' & 'Watkins2.0': Smart phone applications (Apps) for gait-assessment in normal pressure hydrocephalus and decompensated long-standing overt ventriculomegaly.Acta neurochirurgica · 2024Article
- A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment.Sensors (Basel, Switzerland) · 2024Article
- Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning.Frontiers in digital health · 2024Article
- Analysis of Lower Extremity Motor Capacity and Foot Plantar Pressure in Overweight and Obese Elderly Women.International journal of environmental research and public health · 2023Article
- Evaluation of Lower-Limb Kinematics during Timed Up and Go (TUG) Test in Subjects with Locomotive Syndrome (LS) Using Wearable Gait Sensors (H-Gait System).Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor.Sensors (Basel, Switzerland) · 2022Article
- Prediction of Stability during Walking at Simulated Ship's Rolling Motion Using Accelerometers.Sensors (Basel, Switzerland) · 2022Article
- Gait as a vital sign: integrating wearables and AI into vestibular and balance medicine.Frontiers in neurologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
The Timed Up and Go (TUG) test has been frequently used to assess the risk of falls in older adults because it is an easy, fast, and simple method of examining functional mobility and balance without special equipment. The purpose of this study is to develop a model that predicts the TUG test using three-dimensional acceleration data collected from wearable sensors during normal walking. We recruited 37 older adults for an outdoor walking task, and seven inertial measurement unit (IMU)-based sensors were attached to each participant. The elastic net and ridge regression methods were used to reduce gait feature sets and build a predictive model. The proposed predictive model reliably estimated the participants' TUG scores with a small margin of prediction errors. Although the prediction accuracies with two foot-sensors were slightly better than those of other configurations (e.g., MAPE: foot (0.865 s) > foot and pelvis (0.918 s) > pelvis (0.921 s)), we recommend the use of a single IMU sensor at the pelvis since it would provide wearing comfort while avoiding the disturbance of daily activities. The proposed predictive model can enable clinicians to assess older adults' fall risks remotely through the evaluation of the TUG score during their daily walking.
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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.