ReviewSensors (Basel, Switzerland)2018
Indirect Measurement of Ground Reaction Forces and Moments by Means of Wearable Inertial Sensors: A Systematic Review.
Review in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 7 of them syntheses that pooled it.
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
96 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Digital Technologies and Biomarkers for Locomotor Capacity Assessment in Older Adults: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Concurrent validity of wearable IMUs for sagittal plane lower-limb range of motion during walking and estimated ground reaction forces: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2025Pooled it
- Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Recent Machine Learning Progress in Lower Limb Running Biomechanics With Wearable Technology: A Systematic Review.Frontiers in neurorobotics · 2022Pooled it
- Continuous home monitoring of Parkinson's disease using inertial sensors: A systematic review.PloS one · 2021Pooled it
- Estimating Biomechanical Time-Series with Wearable Sensors: A Systematic Review of Machine Learning Techniques.Sensors (Basel, Switzerland) · 2019Pooled it
- Accuracy of consumer-level and research-grade activity trackers in ambulatory settings in older adults.PloS one · 2019Trial
- Development and Validation of Accelerometer-Based Machine Learning Models for Classifying Walking, Running, and Jumping Activities.Sensors (Basel, Switzerland) · 2026Article
- Development and Validation of a Kinetics Prediction Model for Football Cutting Using a Single Trunk-Mounted IMU.Sensors (Basel, Switzerland) · 2026Article
- The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.Sensors (Basel, Switzerland) · 2026Article
- Prediction of Three-Dimensional Ground Reaction Forces in the Golf Swing Using Wearable Inertial Measurement Units and Biomimetic Deep Learning Models.Biomimetics (Basel, Switzerland) · 2026Article
- A conceptual field-based framework for lower-upper body kinetic chain assessment in the tennis serve using IMUs and portable force plates: measurement coverage, new performance indices, and applied implications.Frontiers in sports and active living · 2026Review
- Spatiotemporal gait parameters and ground reaction force analysis in female non-gymnastsFrontiers in bioengineering and biotechnology · 2026Article
- Biomechanical Monitoring of Exercise Fatigue Using Wearable Devices: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Ergonomic Risk Assessment of Professional Dance Using Motion Capture with Ergonomic Evaluation by the Rapid Entire Body Assessment (REBA).Sensors (Basel, Switzerland) · 2025Article
- A multi-pseudo-sensor fusion approach to estimating the lower limb joint moments based on deep neural network.Medical & biological engineering & computing · 2025Article
- Estimation of the External Knee Adduction Moment Using Inertial Measurement Unit Sensors on the Shank and Lower Back: A Pilot Study.Journal of functional morphology and kinesiology · 2025Article
- Verification of Footwear Effects on a Foot Deformation Approach for Estimating Ground Reaction Forces and Moments.Sensors (Basel, Switzerland) · 2025Article
- Estimation of Three-Dimensional Ground Reaction Force and Center of Pressure During Walking Using a Machine-Learning-Based Markerless Motion Capture System.Bioengineering (Basel, Switzerland) · 2025Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last few years, estimating ground reaction forces by means of wearable sensors has come to be a challenging research topic paving the way to kinetic analysis and sport performance testing outside of labs. One possible approach involves estimating the ground reaction forces from kinematic data obtained by inertial measurement units (IMUs) worn by the subject. As estimating kinetic quantities from kinematic data is not an easy task, several models and protocols have been developed over the years. Non-wearable sensors, such as optoelectronic systems along with force platforms, remain the most accurate systems to record motion. In this review, we identified, selected and categorized the methodologies for estimating the ground reaction forces from IMUs as proposed across the years. Scopus, Google Scholar, IEEE Xplore, and PubMed databases were interrogated on the topic of Ground Reaction Forces estimation based on kinematic data obtained by IMUs. The identified papers were classified according to the methodology proposed: (i) methods based on direct modelling; (ii) methods based on machine learning. The methods based on direct modelling were further classified according to the task studied (walking, running, jumping, etc.). Finally, we comparatively examined the methods in order to identify the most reliable approaches for the implementation of a ground reaction force estimator based on IMU data.
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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.