ArticleSensors (Basel, Switzerland)2019
Use of a Single Wireless IMU for the Segmentation and Automatic Analysis of Activities Performed in the 3-m Timed Up & Go Test.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Exploring the repeatability of curved gait during an instrumented figure of 8 walk test, a new methodology.Scientific reports · 2026Article
- Step-Length Estimation in Asymmetric Gait Using a Single Lower-Back IMU Data and a Biomechanical Model Inspired by a Double Inverted Pendulum.Bioengineering (Basel, Switzerland) · 2025Article
- L Test Subtask Segmentation for Lower-Limb Amputees Using a Random Forest Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Investigating the Effects of Virtual Reality-Based Training on Balance Ability and Balance Confidence in Older Individuals.Applied sciences (Basel, Switzerland) · 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
- Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.Sensors (Basel, Switzerland) · 2022Article
- A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis Systems.Sensors (Basel, Switzerland) · 2022Article
- Machine learning classification of multiple sclerosis patients based on raw data from an instrumented walkway.Biomedical engineering online · 2022Article
- Between-site equivalence of turning speed assessments using inertial measurement units.Gait & posture · 2021Article
- Inertial Sensor Algorithms to Characterize Turning in Neurological Patients With Turn Hesitations.IEEE transactions on bio-medical engineering · 2021Article
- Automatic Functional Shoulder Task Identification and Sub-task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment.Sensors (Basel, Switzerland) · 2020Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Falls represent a major public health problem in the elderly population. The Timed Up & Go test (TU & Go) is the most used tool to measure this risk of falling, which offers a unique parameter in seconds that represents the dynamic balance. However, it is not determined in which activity the subject presents greater difficulties. For this, a feature-based segmentation method using a single wireless Inertial Measurement Unit (IMU) is proposed in order to analyze data of the inertial sensors to provide a complete report on risks of falls. Twenty-five young subjects and 12 older adults were measured to validate the method proposed with an IMU in the back and with video recording. The measurement system showed similar data compared to the conventional test video recorded, with a Pearson correlation coefficient of 0.9884 and a mean error of 0.17 ± 0.13 s for young subjects, as well as a correlation coefficient of 0.9878 and a mean error of 0.2 ± 0.22 s for older adults. Our methodology allows for identifying all the TU & Go sub–tasks with a single IMU automatically providing information about variables such as: duration of sub⁻tasks, standing and sitting accelerations, rotation velocity of turning, number of steps during walking and turns, and the inclination degrees of the trunk during standing and sitting.
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.