Bone collision detection method for robot assisted fracture reduction based on force curve slope.

Area of Science:

• Robotics
• Biomechanics
• Medical Engineering

Background:

• Robot-assisted fracture reduction offers minimally invasive and accurate surgery.
• Existing systems lack crucial bone collision detection capabilities, posing safety risks.
• Real-time detection is essential for safe and effective robotic surgical interventions.

Purpose of the Study:

• To develop a real-time bone collision detection method for robot-assisted fracture reduction.
• To address the safety deficiencies in current robotic surgical systems.
• To improve the precision and reliability of fracture reduction procedures.

Main Methods:

• Established a collision mechanical model using a three-element viscoelastic model.
• Analyzed factors influencing the force curve slope ratio, including speed, soft tissue, and angle.
• Developed and optimized a collision detection model based on the slope ratio of the force curve.

Main Results:

• Collision experiments demonstrated that increased speed and soft tissue mass decrease the slope ratio, while increased collision angle increases it.
• The optimized model effectively filters invalid waveforms and reduces false detections.
• A detection threshold of 10 reliably identifies bone collisions with minimal delay.

Conclusions:

• A robust bone collision detection method based on force curve slope ratio was developed.
• The optimized method exhibits adaptability across various experimental conditions.
• Implementing this method in robotic systems significantly enhances surgical safety and accuracy.