Next-Generation Tactile Sensing and Machine Learning Integration for Robot-Assisted Minimally Invasive Surgery
Bibliographic record
Abstract
Tactile feedback in robot-assisted minimally invasive surgery (RAMIS) is crucial for surgeons when palpating subsurface tumors and other organ structures. The research presented here is a new approach for tactile sensation generation that aims to provide deformation and texture detection in RAMIS. The proposed solution comprises three phases: feature extraction, recognition and feedback. The feature extraction process is based on data acquisition from two micro-electromechanical systems (MEMS) sensors and a force-sensitive resistor (FSR) sensor attached to an EndoWrist thoracic grasper instrument compatible with the da Vinci Surgical System. The acquired data is processed using digital signal processing methods and utilized in the recognition phase. The recognition segment receives the features as inputs for training and testing two advanced machine learning algorithms. The first algorithm is a Reflex Fuzzy Min-Max Neural Network (RFMN); the other is a Time Series Classification - Learning Shapelets (TSC-LS) method. The machine learning algorithms aim to accurately recognize and classify physiological structures with different softness and roughness into a corresponding deformation or texture label. Lastly, a means of mechanically giving the labeled data as feedback to the surgeon via a visual-tactile display and a wearable device located on the surgeon's forearm is accomplished to mimic palpation feedback during RAMIS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".