Szczegóły publikacji
Opis bibliograficzny
Vibro-acoustic sensing of instrument interactions as a potential source of texture-related information in robotic palpation / Thomas Sühn, Nazila Esmaeili, Sandeep Y. Mattepu, Moritz Spiller, Axel Boese, Robin Urrutia, Victor Poblete, Christian Hansen, Christoph H. Lohmann, Alfredo Illanes, Michael FRIEBE // Sensors [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1424-8220. — 2023 — vol. 23 iss. 6 art. no. 3141, s. 1–21. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 19–21, Abstr. — Publikacja dostępna online od: 2023-03-15. — M. Friebe - dod. afiliacja: INKA Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, Germany; CIB — Center of Innovation and Business Development, FOM University of Applied Sciences, Germany
Autorzy (11)
- Sühn Thomas
- Esmaeili Nazila
- Mattepu Sandeep Y.
- Spiller Moritz
- Boese Axel
- Urrutia Robin
- Poblete Victor
- Hansen Christian
- Lohmann Christoph Hubertus
- Illanes Alfredo
- AGHFriebe Michael
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150048 |
|---|---|
| Data dodania do BaDAP | 2023-12-02 |
| Tekst źródłowy | URL |
| DOI | 10.3390/s23063141 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Sensors |
Abstract
The direct tactile assessment of surface textures during palpation is an essential component of open surgery that is impeded in minimally invasive and robot-assisted surgery. When indirectly palpating with a surgical instrument, the structural vibrations from this interaction contain tactile information that can be extracted and analysed. This study investigates the influence of the parameters contact angle (Formula presented.) and velocity (Formula presented.) on the vibro-acoustic signals from this indirect palpation. A 7-DOF robotic arm, a standard surgical instrument, and a vibration measurement system were used to palpate three different materials with varying (Formula presented.) and (Formula presented.). The signals were processed based on continuous wavelet transformation. They showed material-specific signatures in the time–frequency domain that retained their general characteristic for varying (Formula presented.) and (Formula presented.). Energy-related and statistical features were extracted, and supervised classification was performed, where the testing data comprised only signals acquired with different palpation parameters than for training data. The classifiers support vector machine and k-nearest neighbours provided (Formula presented.) and (Formula presented.) accuracy for the differentiation of the materials. The results indicate the robustness of the features against variations in the palpation parameters. This is a prerequisite for an application in minimally invasive surgery but needs to be confirmed in realistic experiments with biological tissues.