ArticlePeerJ2022
Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution.
Article in PeerJ, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis 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
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Estimation of Kinetics Using IMUs to Monitor and Aid in Clinical Decision-Making during ACL Rehabilitation: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Peak Vertical Ground Reaction Force Modifies Gait Biomechanics Bilaterally Following ACL Reconstruction: A Randomized Experiment.Journal of orthopaedic research : official publication of the Orthopaedic Research Society · 2025Trial
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (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
- Do Training Load Metrics Agree? A Comparison of Session Rate of Perceived Exertion, Physiological and Biomechanical Load in Outdoor Running.Sports medicine - open · 2026Article
- Prediction of on-field rugby scrummaging contact forces from videos using artificial neural networks.PloS one · 2026Article
- Treadmill-based assessment of running-specific prostheses in steady and accelerated running: a case study with a Paralympic medalist.Frontiers in sports and active living · 2026Article
- Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.Biomedical engineering online · 2025Article
- Predicting Running Vertical Ground Reaction Forces Using Neural Network Models Based on an IMU Sensor.Sensors (Basel, Switzerland) · 2025Article
- Integrating personalized shape prediction, biomechanical modeling, and wearables for bone stress prediction in runners.NPJ digital medicine · 2025Article
- Biomechanical Changes and the Time Course of Recovery in Lower Extremities of Recreational Runners Following a Simulated Treadmill Half-Marathon.Sports medicine - open · 2025Article
- Prediction of Vertical Ground Reaction Forces Under Different Running Speeds: Integration of Wearable IMU with CNN-xLSTM.Sensors (Basel, Switzerland) · 2025Article
- AddBiomechanics Dataset: Capturing the Physics of Human Motion at Scale.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- A motion capture protocol for the kinematic analysis of transfemoral and transtibial sprinters.Frontiers in bioengineering and biotechnology · 2025Article
- A review of uphill and downhill running: biomechanics, physiology and modulating factors.Frontiers in bioengineering and biotechnology · 2025Review
- Estimating braking and propulsion forces during overground running in and out of the lab.PloS one · 2025Article
- Comparison of the Accuracy of Ground Reaction Force Component Estimation between Supervised Machine Learning and Deep Learning Methods Using Pressure Insoles.Sensors (Basel, Switzerland) · 2024Article
- Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation.IEEE transactions on bio-medical engineering · 2024Article
- Effect of sensor number and location on accelerometry-based vertical ground reaction force estimation during walking.PLOS digital health · 2024Article
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 at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGround reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. PURPOSE: We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data.
methodsNineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects.
resultsThe recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.
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.