ArticleIEEE transactions on bio-medical engineering2022
Estimating Ground Reaction Force and Center of Pressure Using Low-Cost Wearable Devices.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- 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 feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors.Wearable technologies · 2025Article
- Myoelectric-Based Estimation of Vertical Ground Reaction Force During Unconstrained Walking by a Stacked One-Dimensional Convolutional Long Short-Term Memory Model.Sensors (Basel, Switzerland) · 2024Article
- Design of a low-cost force insoles to estimate ground reaction forces during human gait.HardwareX · 2024Article
- Calibrating Low-Cost Smart Insole Sensors with Recurrent Neural Networks for Accurate Prediction of Center of Pressure.Sensors (Basel, Switzerland) · 2024Article
- Integration of force and IMU sensors for developing low-cost portable gait measurement system in lower extremities.Scientific reports · 2023Article
- Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis.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
- A Wearable-Sensor System with AI Technology for Real-Time Biomechanical Feedback Training in Hammer Throw.Sensors (Basel, Switzerland) · 2022Article
- Biomechanical Effects of Manipulating Preferred Cadence During Treadmill Walking in Patients With ACL Reconstruction.Sports healthArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveAmbulatory monitoring of ground reaction force (GRF) and center of pressure (CoP) could improve management of health conditions that impair mobility. Insoles instrumented with force-sensitive resistors (FSRs) are an unobtrusive, low-cost, and low-power technology for sampling GRF and CoP in real-world environments. However, FSRs have variable response characteristics that complicate estimation of GRF and CoP. This study introduces a unique data analytic pipeline that enables accurate estimation of GRF and CoP despite relatively inaccurate FSR responses. This paper also investigates whether inclusion of a complementary knee angle sensor improves estimation accuracy.
methodsSeventeen healthy subjects were equipped with an insole instrumented with six FSRs and a string-based knee angle sensor. Subjects walked in a straight line at self-selected slow, preferred, and fast speeds over an in-ground force platform. Twenty repetitions were performed for each speed. Supervised machine learning models estimated weight-normalized GRF and shoe size-normalized CoP, which were re-scaled to obtain GRF and CoP.
resultsAnteroposterior GRF, Vertical GRF, and Anteroposterior CoP were estimated with a normalized root mean square error (NRMSE) of less than 5%. Mediolateral GRF and CoP were estimated with an NRMSE of 8.1% and 6.4%, respectively. Knee angle-related features slightly improved GRF estimates.
conclusionNormalized models accurately estimated GRF and CoP despite deficiencies in FSR data. SIGNIFICANCE: Ambulatory use of the proposed system could enable objective, longitudinal monitoring of severity and progression for a variety of health conditions.
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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.