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 $\alpha$ and velocity $\overrightarrow{v}$ 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 $\alpha$ and $\overrightarrow{v}$. 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 $\alpha$ and $\overrightarrow{v}$. 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 $99.67\%$ and $96.00\%$ 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.

This article provides a comprehensive analysis of the feature extraction methods applied to vibro-acoustic signals (VA signals) in the context of robot-assisted interventions. The primary objective is to extract valuable information from these signals to understand tissue behaviour better and build upon prior research. This study is divided into three key stages: feature extraction using the Cepstrum Transform (CT), Mel-Frequency Cepstral Coefficients (MFCCs), and Fast Chirplet Transform (FCT); dimensionality reduction employing techniques such as Principal Component Analysis (PCA), t-Distributed Stochastic Neighbour Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP); and, finally, classification using a nearest neighbours classifier. The results demonstrate that using feature extraction techniques, especially the combination of CT and MFCC with dimensionality reduction algorithms, yields highly efficient outcomes. The classification metrics (Accuracy, Recall, and F1-score) approach 99%, and the clustering metric is 0.61. The performance of the CT–UMAP combination stands out in the evaluation metrics.

Nonlinear dimensionality reduction techniques, often referred to as manifold learning, are increasingly valuable for data visualization and unsupervised clustering. In the context of surgery and medicine, these methods facilitate the analysis of complex datasets, enabling pattern recognition in surgical data. This study explores the characterization of six tissue types through manifold learning and unsupervised clustering, utilizing vibro-acoustic (VA) signals collected from manual palpation experiments. A wireless sensor mounted at the tip of a surgical instrument was used to acquire 1,680 VA signals, which were processed using Fourier transform and cepstral analysis for feature extraction.We assessed the performance of two dimensionality reduction techniques: uniform manifold approximation and projection (UMAP) and variational autoencoder (VAE). Results indicate that cepstral features combined with UMAP yield superior clustering performance compared to VAE, achieving higher classification accuracy (92 % vs. 87 %) and better-defined clusters with greater compactness. The observed differences in performance are linked to the intrinsic properties of the tissues, particularly surface characteristics such as friction and moisture, which affect signal consistency. Additionally, we compared our approach with previous works, including a study utilizing the same dataset, where our methodology demonstrated improved accuracy. Future research will focus on refining the VAE model, increasing the diversity of tissue samples, and validating the proposed approach in real surgical settings to enhance its applicability in minimally invasive surgery.

Background/Objectives: Establishing laparoscopic access remains a critical and complication-prone step in minimally invasive surgery. Previous work has shown that proximal vibroacoustic sensing can identify peritoneal puncture events in porcine cadavers. The present pilot study evaluated whether these findings translate to human anatomy under controlled, ex vivo conditions. Methods: A vibroacoustic sensing prototype was proximally attached to a standard Veress needle during 14 insertions into a fresh human body donor (within 48 h post-mortem). An endoscope was introduced laterally to provide visual ground truth of peritoneal entry. Vibroacoustic signals were recorded at the proximal end of the instrument. Time–frequency analyses, transient excitation detection, and statistical comparisons were performed to assess whether (1) peritoneal puncture can be identified in the vibroacoustic signal, (2) signal phases and dynamics correspond to those previously observed in porcine cadavers, and (3) peritoneal punctures can be statistically differentiated from non-peritoneal events. Results: All 14 peritoneal punctures were identifiable in the vibroacoustic signal under the experimental conditions. Characteristic signal phases previously described in porcine tissue, including transient excitation associated with cavity entry, were consistently reproduced with comparable temporal and spectral profiles. Statistical analyses demonstrated group-level differences between peritoneal and non-peritoneal events, and the peritoneal puncture was the highest-energy event of its insertion in 13 of 14 cases (92.9%). Conclusions: Under the controlled ex vivo conditions of this single-donor pilot study, vibroacoustic sensing was feasible for identifying peritoneal puncture in human tissue and reproduced signal dynamics observed in porcine models. To our knowledge, this is the first demonstration of the proximal vibroacoustic sensing concept on a human body donor and the first cross-species replication of the previously reported puncture phase structure, establishing an important translational stepping stone between animal cadaver studies and in vivo investigations. The study demonstrates feasibility rather than clinical reliability: the single-donor design and the retrospective annotation framework limit generalizability. Prospective validation in living patients, across multiple subjects and operators, is required before clinical deployment.

This study evaluates the use of vibroacoustic (VA) sensing to detect material transitions during needle insertions into layered synthetic phantoms. Experiments were conducted using foams with different pore densities and configurations including internal cavities. Time-domain and wavelet analyses showed that denser foams produced more frequent vibrational events, while less dense foams resulted in sparser signals. In setups with elastic membranes and cavities, differences in time-frequency patterns were observed: high-frequency activity appeared after membrane rupture but was absent in cavity insertions. These results support the use of VA sensing to identify mechanical changes during minimally invasive procedures.