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Vibro-Acoustic Sensing of Instrument Interactions as a Potential Source of Texture-Related Information in Robotic Palpation

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 informa...

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Detalles Bibliográficos
Autores principales: Sühn, Thomas, Esmaeili, Nazila, Mattepu, Sandeep Y., Spiller, Moritz, Boese, Axel, Urrutia, Robin, Poblete, Victor, Hansen, Christian, Lohmann, Christoph H., Illanes, Alfredo, Friebe, Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10056323/
https://www.ncbi.nlm.nih.gov/pubmed/36991854
http://dx.doi.org/10.3390/s23063141
Descripción
Sumario: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: see text] and velocity [Formula: see text] 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: see text] and [Formula: see text]. 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: see text] and [Formula: see text]. 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: see text] and [Formula: see text] 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.