ReviewSensors (Basel, Switzerland)2025
A Review of Gait Analysis Using Gyroscopes and Inertial Measurement Units.
Review in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- A Sustainable Natural-Rubber IoT Smart Insole for Remote Body-Load Monitoring: An Observational Gait Comparison in Flexible Flatfoot.Sensors (Basel, Switzerland) · 2026Article
- MediaPipe-Based Activity Analysis by Healthcare Professionals: Method Development and Technology Acceptance-A Pilot Study.Sensors (Basel, Switzerland) · 2026Article
- Deep Learning-Based Temporal Gait Analysis Using a Smartphone IMU in Older Adults with and Without Non-Specific Low Back Pain.Bioengineering (Basel, Switzerland) · 2026Article
- Development and Prospective Validation of Wearable Sensor-Based Gait Metric for Individuals with Lower-Limb Amputation.Sensors (Basel, Switzerland) · 2026Article
- Innovative strategies for diabetic peripheral neuropathy: From clinical management to emerging bioengineering solutions.Bioactive materials · 2026Review
- Walking as a Window to the Brain: Redefining Gait in Neurology.Medical sciences (Basel, Switzerland) · 2026Review
- IMU-Based Wearable Insoles in Clinical Settings: Key Parameters Differentiating Clinical and Non-Clinical Populations.Sensors (Basel, Switzerland) · 2026Article
- EEG and IMU Gait Signal Processing: A Comparative Assessment of the "Reza" Exponential Filter and Classical Filters.Sensors (Basel, Switzerland) · 2026Article
- Spatiotemporal Gait Parameters in Fixed Versus Rotating Bearing Total Knee Arthroplasty: A Prospective 24-Month Longitudinal Study.Journal of personalized medicine · 2026Article
- Technologies to investigate the mechanisms of force-based manipulations: a narrative methods review.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- Wearable AI for on-device frailty assessment.Nature communications · 2025Article
- Feasibility of IMU-Based Wearable Sonification: Toward Personalized, Real-Time Gait Monitoring and Rehabilitation.Biosensors · 2025Article
- Improved running gait parameter estimation from single foot-mounted IMU data based on refined event detection.Frontiers in bioengineering and biotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Wearable sensors are used in gait analysis to obtain spatiotemporal parameters, with gait events serving as critical markers for foot and lower limb movement. Summarizing detection methods is essential, as accurately identifying gait events and phases are key to deriving precise spatiotemporal parameters through wearable technology. However, a clear understanding of how these sensors, particularly angular velocity and acceleration signals within inertial measurement units, individually or collectively, contribute to the detection of gait events and gait phases is lacking. This review aims to summarize the current state of knowledge on the application for both gyroscopes, with particular emphasis on the role of angular velocity signals, and inertial measurement units with both angular velocity and acceleration signals in identifying gait events, gait phases, and calculating gait spatiotemporal parameters. Gyroscopes remain the primary tool for gait events detection, while inertia measurement units enhance reliability and enable spatiotemporal parameter estimation. Rule-based methods are suitable for controlled environments, whereas machine learning offers flexibility to analyze complex gait conditions. In addition, there is a lack of consensus on optimal sensor configurations for clinical applications. Future research should focus on standardizing sensor configurations and developing robust, adaptable detection methodologies suitable for different gait conditions.
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.