ArticleSensors (Basel, Switzerland)2022
Estimating Running Ground Reaction Forces from Plantar Pressure during Graded Running.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- An Accelerometer-Equipped Running Watch Provides a Valid Estimate of Peak Vertical Ground Reaction Force and Cumulative Weighted Damage: A Cross-Sectional Laboratory Study.European journal of sport science · 2026Article
- Emerging sports footwear technologies and their effects on running economy, biomechanics, and performance: a systematic review.BMC sports science, medicine & rehabilitation · 2026Article
- The Use of Machine Learning to Estimate Ground Reaction Forces During Running: A Scoping Review of the Current Practices.Sensors (Basel, Switzerland) · 2026Article
- Data driven shoe design improves running economy beyond state-of-the-art Advanced Footwear Technology running shoes.Journal of sport and health science · 2026Article
- Foot Strike Pattern Detection Using a LoadsolSensors (Basel, Switzerland) · 2025Article
- Estimation of 3D Ground Reaction Force and 2D Center of Pressure Using Deep Learning and Load Cells Across Various Gait Conditions.Sensors (Basel, Switzerland) · 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
- Novel Cost-Effective and Portable Three-Dimensional Force Measurement System for Biomechanical Analysis: A Reliability and Validity Study.Sensors (Basel, Switzerland) · 2024Article
- 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
- Impact of PCA Pre-Normalization Methods on Ground Reaction Force Estimation Accuracy.Sensors (Basel, Switzerland) · 2024Article
- Article
- Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning Model for 3D Ground Reaction Force Estimation Using Shoes with Three Uniaxial Load Cells.Sensors (Basel, Switzerland) · 2023Article
- Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds.Sensors (Basel, Switzerland) · 2023Article
- Characterization of the Kinetyx SI Wireless Pressure-Measuring Insole during Benchtop Testing and Running Gait.Sensors (Basel, Switzerland) · 2023Article
- Estimating 3D ground reaction forces in running using three inertial measurement units.Frontiers in sports and active living · 2023Article
- Predicting Vertical Ground Reaction Forces in Running from the Sound of Footsteps.Sensors (Basel, Switzerland) · 2022Article
- Evaluation of the Foot Center of Pressure Estimation from Pressure Insoles during Sidestep Cuts, Runs and Walks.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the basis for computing inverse dynamics. Wearable technology can predict time-continuous GRFs during walking and running; however, the majority of GRF predictions examine level ground locomotion. The purpose of this manuscript was to predict vertical and anterior-posterior GRFs across different speeds and slopes. Eighteen recreationally active subjects ran on an instrumented treadmill while we collected GRFs and plantar pressure. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. We estimated GRFs using a set of linear models and a recurrent neural network, which used speed, slope, and plantar pressure as inputs. We also tested eliminating speed and slope as inputs. The recurrent neural network outperformed the linear model across all conditions, especially with the prediction of anterior-posterior GRFs. Eliminating speed and slope as model inputs had little effect on performance. We also demonstrate that subject-specific model training can reduce errors from 8% to 3%. With such low errors, researchers can use these wearable-based GRFs to understand running performance or injuries in real-world settings.
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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.