ArticleNature communications2025
Learning-based 3D human kinematics estimation using behavioral constraints from activity classification.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- Understanding Motor Adaptation in the Transition to Sustained Pain: Protocol for a Longitudinal Experimental Study.JMIR research protocols · 2026Article
- Advancing Wearable-Based Upper-Limb Stroke Recovery Assessment to the Clinic: A Comparison of Movement Segmentation Strategies.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Multimodal Motion Capture Toolbox for Enhanced Analysis of Intersegmental Coordination in Children with Cerebral Palsy and Typically Developing.Journal of visualized experiments : JoVE · 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
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Inertial measurement units offer a cost-effective, portable alternative to lab-based motion capture systems. However, measuring joint angles and movement trajectories with inertial measurement units is challenging due to signal drift errors caused by biases and noise, which are amplified by numerical integration. Existing approaches use anatomical constraints to reduce drift but require body parameter measurements. Learning-based approaches show promise but often lack accuracy for broad applications (e.g., strength training). Here, we introduce the Activity-in-the-loop Kinematics Estimator, an end-to-end machine learning model incorporating human behavioral constraints for enhanced kinematics estimation using two inertial measurement units. It integrates activity classification with kinematics estimation, leveraging limited movement patterns during specific activities. In dynamic scenarios, our approach achieved trajectory and shoulder joint angle errors under 0.021 m and
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Registered trials
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