Multi-step prediction of physiological tremor for robotics applications

Area of Science:

• Robotics
• Signal Processing
• Biomedical Engineering

Background:

• Real-time performance of surgical robotic devices is critically dependent on sensor phase delay and filtering processes.
• An unavoidable phase delay of 16-20 ms occurs due to hardware low-pass filters and pre-filtering for cancellation in surgical robotics.
• This phase delay can significantly impact the precision and effectiveness of robotic surgical procedures.

Purpose of the Study:

• To investigate methods for overcoming the unavoidable phase delay in surgical robotic systems.
• To enhance the accuracy of tremor estimation in the presence of phase delay.
• To evaluate the effectiveness of multi-step prediction techniques for real-time robotic surgery.

Main Methods:

• Employed multi-step prediction utilizing band-limited multiple Fourier linear combiner (BMFLC) and Autoregressive (AR) methods.
• Focused on mitigating the effects of hardware low-pass filtering and pre-filtering stages.
• Applied and tested methods for one degree-of-freedom (1-DOF) tremor estimation.

Main Results:

• Achieved a 60% improvement in overall accuracy for tremor estimation compared to single-step prediction methods.
• Demonstrated the efficacy of the proposed multi-step prediction techniques in the presence of significant phase delay.
• Experimental results confirmed the practical improvement in tremor estimation for robotic applications.

Conclusions:

• Multi-step prediction with BMFLC and AR methods effectively overcomes phase delay challenges in surgical robotics.
• The proposed approach significantly enhances tremor estimation accuracy, crucial for real-time surgical performance.
• This advancement holds promise for improving the safety and efficacy of robotic-assisted surgeries.