ArticleFrontiers in bioengineering and biotechnology2023
Estimating vertical ground reaction forces during gait from lower limb kinematics and vertical acceleration using wearable inertial sensors.
Article in Frontiers in bioengineering and biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Multibody dynamics-based musculoskeletal modeling for gait analysis: a systematic review.Journal of neuroengineering and rehabilitation · 2024Pooled it
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Harmonic and power spectrum analysis of vertical ground reaction force in patients who have a stroke within 3 months.Experimental physiology · 2026Article
- Preliminary Technical Feasibility of Integrating Auxetic Foam into Foot Orthoses for Diverse Neuropathic Etiologies: A Small-Scale Pilot Observation.Bioengineering (Basel, Switzerland) · 2026Article
- Spatio-temporal graph convolutional networks with transfer learning for continuous ground reaction force estimation in hemiparetic gait.Frontiers in bioengineering and biotechnology · 2026Article
- Fiber Bragg Grating Embedded 3D-Printed Insole with Commercial and Portable Reader for Stance Phase Determination.Biosensors · 2025Article
- Examination of the gait evaluation index using shoes with built-in motion sensors: intra-rater reliability during comfortable walking and fastest walking.Journal of physical therapy science · 2025Article
- Performance of deep-learning models incorporating knee alignment information for predicting ground reaction force during walking.Biomedical engineering online · 2025Article
- Use of Smartphone-Based Experimental Data for the Calibration of Biodynamic Spring-Mass-Damper (SMD) Pedestrian Models.Sensors (Basel, Switzerland) · 2025Article
- Evaluation method of Driver's olfactory preferences: a machine learning model based on multimodal physiological signals.Frontiers in bioengineering and biotechnology · 2024Article
- Prediction of Three-Directional Ground Reaction Forces during Walking Using a Shoe Sole Sensor System and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most important forces generated during gait is the vertical ground reaction force (vGRF). This force can be measured using force plates, but these can limit the scope of gait analysis. This paper presents a method to estimate the vGRF using inertial measurement units (IMU) and machine learning techniques. Four wearable IMUs were used to obtain flexion/extension angles of the hip, knee, and ankle joints, and an IMU placed over the C7 vertebra to measure vertical acceleration. We trained and compared the performance of two machine learning algorithms: feedforward neural networks (FNN) and random forest (RF). We investigated the importance of the inputs introduced into the models and analyzed in detail the contribution of lower limb kinematics and vertical acceleration to model performance. The results suggest that the inclusion of vertical acceleration increases the root mean square error in the FNN, while the RF appears to decrease it. We also analyzed the ability of the models to construct the force signal, with particular emphasis on the magnitude and timing of the vGRF peaks. Using the proposed method, we concluded that FNN and RF models can estimate the vGRF with high accuracy.
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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.