ArticleInternational IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering2023
Nonlinear System Identification of Tremors Dynamics: A Data-driven Approximation Using Koopman Operator Theory.
Article in International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Ultrasound-Informed State Estimation of Wrist Tremor Dynamics via Koopman Operator for Personalized Sensory Peripheral Nerve Stimulation.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2026Article
- Towards Ultrasound Imaging-based Closed-loop Peripheral Nerve Stimulation for Tremor Suppression.Current opinion in biomedical engineering · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
People who suffer from tremors have difficulty performing activities of daily living. Efforts in developing a model of a limb with tremors can pave the way for non-surgical tremor suppression techniques. However, due to the nonlinearity, developing an accurate model of tremors is challenging. This paper implements a data-driven method for approximating the Koopman operator, which is capable of presenting nonlinear dynamics in a linear framework and is promising for predicting the nonlinear system. A dynamic model of tremors is developed with ultrasound (US) image data collected from a patient with essential tremor as they grasp objects. The method is applied to predict the patient's tremor dynamics and is compared with the nonlinear Hammerstein-Wiener system identification technique.
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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 Socratic graph.