ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Surrogate Simulation of Subject-Specific Lateral Pinch via Deep Learning.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Transfer Learning With Simulated and Recorded Data Improves Predictions of Lateral Pinch Thumb-Tip Forces.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Musculoskeletal modeling and simulation is often a lengthy and computationally expensive process, particularly when developing and using personalized models. We present a deep learning-based adaptive surrogate model for lateral pinch, which accepts both musculoskeletal parameters and muscle activations as input for personalization and simulation. This model matches traditional OpenSim forward dynamics with an average root-mean-squared error (RMSE) of 2.27 N, within standard errors of experimental measurements, while demonstrating sensitivity to both categories of input and performing thousand of simulations in seconds (10-1000x faster than traditional multi-body simulations). In addition to direct use as a surrogate, the differentiable nature of the model may support future use in optimization problems, while its flexibility may support adaptation to modeling of experimental data.
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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.