6.3.4 - Graph Deviation Networks for Schizophrenia Signatures Detection in Brain Organoids’ MEA Signals
- Event
- EUROSENSORS 2026
2026-09-06 - 2026-09-09
Zurich - Band
- Lectures
- Chapter
- Signal Analysis, Machine Learning and Artificial Intelligence for Sensing
- Author(s)
- A. Riccardi, A. Mencattini, G. Curci, E. Martinelli - University of Rome Tor Vergata,Rome (Italy), W. Zhang, Q. Meng - University of South China,Hunan (China)
- Pages
- 215 - 216
- DOI
- 10.5162/eurosensors2026/6.3.4
- ISBN
- 978-3-910600-12-6
- Price
- free
Abstract
Minimally invasive surgery lacks haptic feedback, limiting the surgeon’s ability to assess tissue interaction forces. This work presents a miniaturized CMOS-based multisensor chip integrated in laparoscopic instruments for multidimensional force sensing. Finite element modeling was used to optimize sensor placement. Machine learning models reconstruct axial, lateral, and gripping forces from 32 sensors per chip. A Random Forest model reached near-perfect accuracy between 0.0001 N and 0.0048 N. Results demonstrate the feasibility of compact, high-precision force sensing in smart surgical instruments.