ArticleSensors (Basel, Switzerland)2020
An Exploration of Machine-Learning Estimation of Ground Reaction Force from Wearable Sensor Data.
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 12 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Unlocking Creative Movement with Inertial Technology.Brain sciences · 2025Article
- 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
- Using Inertial Measurement Units and Machine Learning to Classify Body Positions of Adults in a Hospital Bed.Sensors (Basel, Switzerland) · 2025Article
- A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling.Sensors (Basel, Switzerland) · 2024Article
- A Wearable Real-time Kinematic and Kinetic Measurement Sensor Setup for Human Locomotion.Wearable technologies · 2023Article
- Machine-Learning-Based Methodology for Estimation of Shoulder Load in Wheelchair-Related Activities Using Wearables.Sensors (Basel, Switzerland) · 2023Article
- Timing in Lower Limb Complex Movement Tests for DanceSport Athletes: Relation between FitLight Trainer and IMU Measurements.Sensors (Basel, Switzerland) · 2023Article
- Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose.Sensors (Basel, Switzerland) · 2022Article
- Article
- The Use of Synthetic IMU Signals in the Training of Deep Learning Models Significantly Improves the Accuracy of Joint Kinematic Predictions.Sensors (Basel, Switzerland) · 2021Article
- Principal Component Analysis of the Running Ground Reaction Forces With Different Speeds.Frontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
This study aimed to develop a wearable sensor system, using machine-learning models, capable of accurately estimating peak ground reaction force (GRF) during ballet jumps in the field. Female dancers (n = 30) performed a series of bilateral and unilateral ballet jumps. Dancers wore six ActiGraph Link wearable sensors (100 Hz). Data were collected simultaneously from two AMTI force platforms and synchronised with the ActiGraph data. Due to sensor hardware malfunctions and synchronisation issues, a multistage approach to model development, using a reduced data set, was taken. Using data from the 14 dancers with complete multi-sensor synchronised data, the best single sensor was determined. Subsequently, the best single sensor model was refined and validated using all available data for that sensor (23 dancers). Root mean square error (RMSE) in body weight (BW) and correlation coefficients (r) were used to assess the GRF profile, and Bland-Altman plots were used to assess model peak GRF accuracy. The model based on sacrum data was the most accurate single sensor model (unilateral landings: RMSE = 0.24 BW, r = 0.95; bilateral landings: RMSE = 0.21 BW, r = 0.98) with the refined model still showing good accuracy (unilateral: RMSE = 0.42 BW, r = 0.80; bilateral: RMSE = 0.39 BW, r = 0.92). Machine-learning models applied to wearable sensor data can provide a field-based system for GRF estimation during ballet jumps.
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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.