ArticleSensors (Basel, Switzerland)2020
Estimation of Three-Dimensional Lower Limb Kinetics Data during Walking Using Machine Learning from a Single IMU Attached to the Sacrum.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 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
20 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- The Use of Wearable Sensors and Machine Learning Methods to Estimate Biomechanical Characteristics During Standing Posture or Locomotion: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled 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
- Relationships between External, Wearable Sensor-Based, and Internal Parameters: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Development and Validation of a Kinetics Prediction Model for Football Cutting Using a Single Trunk-Mounted IMU.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
- An Active Spring Mass Model With Biomimetic Ground Reaction Forces for Multiple Terrains.IEEE transactions on bio-medical engineering · 2026Article
- Tactile Sensor-Based Body Center of Pressure Estimation System Using Supervised Deep Learning Models.Sensors (Basel, Switzerland) · 2026Article
- A predictive model of joint dynamics and ground reaction force using only leg length, body mass, and walking cadence.PloS one · 2026Article
- 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
- Multi-Modal Sensing for Propulsion Estimation in People Post-Stroke Across Speeds.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025Article
- Utilizing Inertial Measurement Units for Detecting Dynamic Stability Variations in a Multi-Condition Gait Experiment.Sensors (Basel, Switzerland) · 2024Article
- Errors in Estimating Lower-Limb Joint Angles and Moments during Walking Based on Pelvic Accelerations: Influence of Virtual Inertial Measurement Unit's Frontal Plane Misalignment.Sensors (Basel, Switzerland) · 2024Article
- Predicting Leg Forces and Knee Moments Using Inertial Measurement Units: An In Vitro Study.Journal of biomechanical engineering · 2024Article
- Wearable sensing for understanding and influencing human movement in ecological contexts.Current opinion in biomedical engineering · 2023Article
- Extended Application of Inertial Measurement Units in Biomechanics: From Activity Recognition to Force Estimation.Sensors (Basel, Switzerland) · 2023Review
- Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks.Frontiers in bioengineering and biotechnology · 2023Article
- Fusion of Wearable Kinetic and Kinematic Sensors to Estimate Triceps Surae Work during Outdoor Locomotion on Slopes.Sensors (Basel, Switzerland) · 2022Article
- Predicting vertical ground reaction forces from 3D accelerometry using reservoir computers leads to accurate gait event detection.Frontiers in sports and active living · 2022Article
- Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Kinetics data such as ground reaction forces (GRFs) are commonly used as indicators for rehabilitation and sports performance; however, they are difficult to measure with convenient wearable devices. Therefore, researchers have attempted to estimate accurately unmeasured kinetics data with artificial neural networks (ANNs). Because the inputs to an ANN affect its performance, they must be carefully selected. The GRF and center of pressure (CoP) have a mechanical relationship with the center of mass (CoM) in the three dimensions (3D). This biomechanical characteristic can be used to establish an appropriate input and structure of an ANN. In this study, an ANN for estimating gait kinetics with a single inertial measurement unit (IMU) was designed; the kinematics of the IMU placed on the sacrum as a proxy for the CoM kinematics were applied based on the 3D spring mechanics. The walking data from 17 participants walking at various speeds were used to train and validate the ANN. The estimated 3D GRF, CoP trajectory, and joint torques of the lower limbs were reasonably accurate, with normalized root-mean-square errors (NRMSEs) of 6.7% to 15.6%, 8.2% to 20.0%, and 11.4% to 24.1%, respectively. This result implies that the biomechanical characteristics can be used to estimate the complete three-dimensional gait data with an ANN model and a single IMU.
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.