Improving Hand Motion Recognition in Robot-assisted Endovascular Surgery with Resolution-based Dynamic Time Warping

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dc.contributor.authorOlatunji M.O.
dc.contributor.authorToluwanimi O.A.
dc.contributor.authorWenke, D.
dc.contributor.authorWenjing, D.
dc.contributor.authorLei Wang
dc.date.accessioned2024-11-25T16:19:39Z
dc.date.available2024-11-25T16:19:39Z
dc.date.issued2024-11-25
dc.descriptionScholarly article
dc.description.abstractHand motion recognition is pivotal for enhancing skill learning in endovascular surgery. However, catheterization procedures typically involve multimodal sources that introduce data dissimilarities, leading to unstable and poor performance in learning-based methods used for recognizing surgical tasks. This study proposes a resolution-based dynamic time warping (rDTW) method for aligning multimodal signals to improve hand motion recognition in endovascular surgery. The rDTW model is designed to align multimodal signals from wearable sensors, referencing the resolution of a unique source. The reference signal traces the events, creating a consistent progression that can localize a surgeon’s hand motions across all signals. Other signals are stretched or folded to match the reference timeline, facilitating event localization relative to the reference signal. By preprocessing multimodal signals from 33 catheterization trials, we show that the proposed rDTW method enhances hand motion recognition performance in two neural network models. When the data was used directly for the motion recognition task without signal warping, a performance decrease of 2.59% was observed. Also, the rDTW method exhibits higher data similarity index and lower warping time compared to three existing DTW techniques, showing its effectiveness in aligning multimodal signals for surgeons’ hand motion recognition.
dc.identifier.citationOlatunji M. O., Toluwanimi O. A., Wenke Duan, Wenjing D., & Lei W. (2024). “Improving Hand Motion Recognition in Robot-assisted Endovascular Surgery with Resolution-based Dynamic Time Warping". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024), pp. 131-139, MIRG
dc.identifier.isbn978-978-771-680-9
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13047
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2024
dc.titleImproving Hand Motion Recognition in Robot-assisted Endovascular Surgery with Resolution-based Dynamic Time Warping
dc.typeArticle
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