Hybrid Optical-Inertial Surgical Tracking System
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Solution Overview
Problem
Current methods for tracking surgical instruments in three-dimensional space during navigated surgery are hindered by the lack of low-cost, accurate sensors, with optical tracking requiring a clear line of sight and being expensive, and inertial sensor systems being unreliable due to noise and signal drift.
Innovation Solution
A hybrid system that augments the bandwidth of a 'slow' optical tracking system with data from a 'fast' inertial tracking system, using an estimation algorithm to merge optical and inertial data for precise position, velocity, and orientation updates at high frequencies, such as 300 Hz, reducing cost, latency, and computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical tracking systems are used to accurately determine the position and orientation of surgical instruments, then measurement precision is improved, but device complexity and cost increase, and line of sight requirements are imposed
Solution Approach 1:
The patent combines optical tracking systems with inertial measurement units (IMUs) to create a hybrid tracking system. The optical system provides accurate position and orientation measurements, while the IMU provides complementary data about motion and orientation changes. This merging allows the system to maintain high measurement precision while reducing overall system complexity by using simpler, lower-cost components that work together synergistically.
Solution Approach 2:
The patent introduces an estimation algorithm as an intermediary that processes data from both optical and inertial sensors. This algorithm acts as a mediator, fusing the complementary strengths of both sensor types to produce accurate tracking results without requiring the full complexity of high-bandwidth optical systems alone.
2Speed
If high bandwidth optical sensor systems are used to provide rapid updates for fast response surgical instruments, then speed is improved, but device complexity and cost increase prohibitively
Solution Approach 1:
The patent merges low-bandwidth optical tracking with high-bandwidth inertial sensing to achieve rapid tracking updates at lower cost. The IMU provides high-frequency motion data that complements the optical system's positional data, enabling fast response tracking without requiring expensive high-bandwidth optical hardware.
Solution Approach 2:
The patent changes the operating parameters by using an estimation algorithm that processes data at different sampling rates. The system can operate at high effective bandwidth through intelligent data fusion rather than relying solely on high-bandwidth sensors, thereby reducing hardware complexity while maintaining speed.
3Speed
If inertial sensor systems are used to provide rapid tracking updates at low cost, then speed and cost-effectiveness are improved, but reliability deteriorates due to noise and signal drift
Solution Approach 1:
The patent combines inertial sensors with optical sensors to create a hybrid system where the optical measurements serve as a reference to correct drift and noise in the inertial data. This merging maintains the speed advantages of inertial sensing while significantly improving reliability through cross-validation and data fusion.
Solution Approach 2:
The patent implements feedback mechanisms where optical tracking data is used to correct and refine inertial sensor readings. The estimation algorithm continuously adjusts the tracking based on the reliability of each sensor type, using optical data to compensate for inertial drift and noise, thereby improving overall signal accuracy.
4Manufacturing precision
If cutting guides are used to provide accurate guidance for surgical instruments, then manufacturing precision is improved, but loss of time increases due to installation and removal requirements
Solution Approach 1:
The patent extracts the guidance function from physical cutting guides and implements it through a tracking system that provides real-time positional and orientational information. This allows surgical instruments to be guided without requiring the installation and removal of separate cutting guide components, thereby reducing time loss while maintaining precision.
Solution Approach 2:
The patent replaces the mechanical cutting guide system with an electronic/optical tracking system. Instead of using physical guides that must be installed and removed, the system uses sensors and estimation algorithms to provide guidance information, eliminating the time-consuming mechanical setup while maintaining surgical precision.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The hybrid system provides accurate and rapid tracking of surgical instruments, enhancing surgical precision and reducing surgery duration by compensating for motion disturbances, thus improving surgical instrument control and accuracy.
Implementation Method 1
at least one camera for imaging said plurality of markers and generating image data indicative of the object location
Implementation Method 2
The inertial transmitter can include gyroscopic and accelerometer sensors to provide six degrees of movement tracking
Implementation Method 3
The inertial transmitter can include gyroscopic and accelerometer sensors to provide six degrees of movement tracking
Data Source
AI summary
Methods and systems for object tracking are disclosed in which the bandwidth of a “slow” tracking system (e.g., an optical tracking system) is augmented with sensor data generated by a “fast” tracking system (e.g., an inertial tracking system). The tracking data generated by the respective systems can be used to estimate and/or predict a position, velocity, and orientation of a tracked object that can be updated at the sample rate of the “fast” tracking system. The methods and systems disclosed herein generally involve an estimation algorithm that operates on raw sensor data (e.g., two-dimensional pixel coordinates in a captured image) as opposed to first processing and/or calculating object position and orientation using a triangulation or “back projection” algorithm.