D3-2.4 - Automatic Sensor Data Fusion for the Detection of Bone Layer Transitions and Channel Length Measurement in Axial Oscillation Bone Drilling
- Event
- 23. ITG/GMA-Fachtagung Sensoren und Messsysteme 2026
2026-06-09 - 2026-06-10
Nürnberg - Band
- Vorträge
- Chapter
- Messunsicherheit und Selbstvalidierung
- Author(s)
- A. Boaron, D. Stöbener, A. Fischer - University of Bremen, Bremen
- Pages
- 278 - 284
- DOI
- 10.5162/sensoren2026/D3-2.4
- ISBN
- 978-3-910600-11-9
- Price
- free
Abstract
In surgery, bone drilling techniques depend largely on the surgeon’s tactile perception, which can be subjective and unreliable. Indeed, accurate detection of bone layer transitions during drilling is critical in orthopedic surgery to avoid over-penetration, as well as thermal and mechanical damage to surrounding tissue. This study introduces automatic sensor data fusion approaches to improve the detection of transitions between cortical and cancellous bone during axial oscillation drilling. Here, multiple signals from a multisensory drive train are fused to enable automatic identification of layer boundaries and overall drill channel length. Experimental validation on pig rib bones shows that time-domain analysis achieves high accuracy, with a standard deviation of 0.22 mm and a mean systematic deviation of 0.26 mm relative to caliper references. The time–frequency approach exhibits larger deviations, with a mean systematic deviation of 1.05 mm and variability up to 1.52 mm. While drill breakthrough is consistently detected, internal layer transition detection reaches 60 % success. Overall, the results demonstrate reliable drill length estimation and highlight the need for improved algorithms for transition detection, supporting the potential of sensor fusion for precise, feedback-driven surgical drilling.