ArticleFrontiers in bioengineering and biotechnology2022
Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Intelligent prediction of dynamic characteristics during exercise in patients with stroke.Journal of neuroengineering and rehabilitation · 2025Article
- OptiSelect and EnShap: Integrating machine learning and game theory for ischemic stroke prediction.PloS one · 2025Article
- Predicting stroke occurrences: a stacked machine learning approach with feature selection and data preprocessing.BMC bioinformatics · 2024Article
- A Machine Learning Approach for Predicting Pedaling Force Profile in Cycling.Sensors (Basel, Switzerland) · 2024Article
- PSA-FL-CDM: A Novel Federated Learning-Based Consensus Model for Post-Stroke Assessment.Sensors (Basel, Switzerland) · 2024Article
- Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks.Frontiers in bioengineering and biotechnology · 2023Article
- Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.Frontiers in bioengineering and biotechnology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants
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Registered trials
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