ArticleSensors (Basel, Switzerland)2020
Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.
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 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 37 citations in OpenAlex.
- Review
- Robot-Aided Motion Analysis in Neurorehabilitation: Benefits and Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Estimation of Lower Limb Joint Angles and Joint Moments during Different Locomotive Activities Using the Inertial Measurement Units and a Hybrid Deep Learning Model.Sensors (Basel, Switzerland) · 2023Article
- BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors.Sensors (Basel, Switzerland) · 2023Article
- Closing the Wearable Gap: Foot-ankle kinematic modeling via deep learning models based on a smart sock wearable.Wearable technologies · 2023Article
- Performance of machine learning models in estimation of ground reaction forces during balance exergaming.Journal of neuroengineering and rehabilitation · 2022Article
- Predicting Knee Joint Kinematics from Wearable Sensor Data in People with Knee Osteoarthritis and Clinical Considerations for Future Machine Learning Models.Sensors (Basel, Switzerland) · 2022Article
- A Deep Learning Approach for Foot Trajectory Estimation in Gait Analysis Using Inertial Sensors.Sensors (Basel, Switzerland) · 2021Article
- 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
- Neural Reduction of Image Data in Order to Determine the Quality of Malting Barley.Sensors (Basel, Switzerland) · 2021Article
- A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles. Additionally, the dimensions of the problem can be simplified using principal component analysis. Training a long short-term memory neural network on the prediction of 3D lower limb joint angles based on inertial data showed that three sensors placed on the pelvis and both shanks are sufficient. The application of principal component analysis to the data of five sensors did not reveal improved results. The use of longer motion sequences compared to time-normalised gait cycles seems to be advantageous for the prediction accuracy, which bridges the gap to real-time applications of long short-term memory neural networks in the future.
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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.