ArticleFrontiers in robotics and AI2021
Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation.
Article in Frontiers in robotics and AI, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Trajectory Learning Using HMM: Towards Surgical Robotics Implementation.Sensors (Basel, Switzerland) · 2025Article
- N-Dimensional Reduction Algorithm for Learning from Demonstration Path Planning.Sensors (Basel, Switzerland) · 2025Article
- Cerebellar transcranial current stimulation - An intraindividual comparison of different techniques.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
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Abstract
In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors, or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed
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Registered trials
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