ArticleScientific reports2025
Learning based lower limb joint kinematic estimation using open source IMU data.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Real-Time Prediction of Lower-Limb Joint Kinematics, Kinetics, and Ground Reaction Force Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2026Article
- [Advances in deep learning multimodal fusion for early diagnosis of knee osteoarthritis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Review
- A Multimodal Dataset of Full-Body Kinematics and Kinetics from Laboratory- and Wearable-Based Systems.Scientific data · 2026Article
- Pocket Racer: An accessible autonomous racing educational platform.Scientific reports · 2026Article
- Deep Learning 1D-CNN-Based Ground Contact Detection in Sprint Acceleration Using Inertial Measurement Units.Sensors (Basel, Switzerland) · 2026Article
- FusionTCN-Attention: A Causality-Preserving Temporal Model for Unilateral IMU-Based Gait Prediction and Cooperative Exoskeleton Control.Biomimetics (Basel, Switzerland) · 2026Article
- Estimation of walking energy expenditure using a single sacrum-mounted IMU based on biomechanically-informed machine learning.Scientific reports · 2025Article
- Musculoskeletal Inverse Kinematics Tool for Inertial Motion Capture Data Based on the Adaptive Unscented Kalman Smoother: An Implementation for OpenSim.Annals of biomedical engineering · 2025Article
- Computational Analysis of Neuromuscular Adaptations to Strength and Plyometric Training: An Integrated Modeling Study.Sports (Basel, Switzerland) · 2025Article
- Use of Technologies for the Acquisition and Processing Strategies for Motion Data Analysis.Biomimetics (Basel, Switzerland) · 2025Review
- Learning-based 3D human kinematics estimation using behavioral constraints from activity classification.Nature communications · 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
- Robust Human Gait Speed Recognition Under Non-Ideal Conditions for Suspension-Assisted Walking Systems.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require substantial data. To meet this demand, we leveraged an open-source dataset to develop and evaluate three training approaches. The first involved training a model exclusively on data from a single user, resulting in high accuracy for that individual only. The second approach trained a model on data from multiple users to generalize across individuals; however, demonstrated lower accuracy due to variations in gait patterns across users. The third approach added transfer learning to the second, improving estimation accuracy for new users through fine-tuning with a small portion of their data. This model overcame the limitations of the previous methods' dependency on extensive data collection, and achieved comparable performance to inverse kinematics, making it an effective solution for diverse populations. Additionally, our analysis on IMU combinations suggests that IMUs placed on the femur and calcaneus are the best for most cases. This framework not only reduces the need for extensive data collection but also enhances personalized gait analysis, enabling more efficient and accessible applications in both clinical assessments and real-world environments for broader use.
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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.