Controller Motion Detection for Unintentional Input in Robotic Surgery
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Solution Overview
Problem
Robotic surgery systems face safety risks due to accidental drops or fumbles of user input devices, which can lead to unintended and potentially dangerous robotic arm movements, as existing methods struggle to reliably distinguish between intentional and unintentional controller motions.
Innovation Solution
The implementation of a machine learning-based system using neural networks, such as recurrent neural networks (RNN) or long-short term memory (LSTM) networks, to detect unintentional movements by analyzing data from accelerometers, gyroscopes, and other sensors, combined with heuristic methods and capacitive sensors, to preemptively disable robot motion when unintentional actions are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based methods are used to detect controller movement, then basic motion tracking is achieved, but the system cannot reliably distinguish between intentional and unintentional movements
Solution Approach 1:
The patent combines multiple sensor types (accelerometer, gyroscope, capacitive sensors) and multiple detection methods (machine learning classification, heuristic rules) into an integrated system. This merging allows the system to cross-validate data from different sources, thereby achieving reliable discrimination between intentional and unintentional movements that no single sensor or method could achieve alone.
Solution Approach 2:
The patent introduces machine learning classifiers and heuristic rule systems as intermediary processing layers between the raw sensor data and the final safety determination. These intermediaries analyze patterns in the sensor data, contextualize the movements, and provide a reliable interpretation that distinguishes intentional from unintentional actions, resolving the limitation of direct sensor-based detection.
2Object-affected harmful factors
If the system restricts robotic arm movement frequently to ensure safety, then patient safety is improved, but surgical procedure efficiency deteriorates
Solution Approach 1:
The patent implements dynamic safety monitoring where the system continuously adapts its response based on real-time analysis of controller movement patterns. Rather than applying static restrictions, the system dynamically adjusts safety interventions, restricting movement only when unintentional controller behavior is detected through pattern recognition, thereby maintaining surgical efficiency while ensuring patient safety.
Solution Approach 2:
The system employs continuous feedback loops where sensor data is constantly monitored, analyzed by machine learning models, and used to adjust robotic arm control in real-time. This feedback mechanism allows the system to learn from surgical patterns and distinguish between intentional surgical adjustments and unintentional controller drops, enabling safety restrictions only when necessary and thus preserving surgical productivity.
3Loss of time
If machine learning models are trained on limited data, then training time and computational resources are reduced, but detection accuracy deteriorates
Solution Approach 1:
The patent employs pre-trained machine learning models that have been trained in advance on comprehensive datasets containing various surgical scenarios, intentional movements, and unintentional drops. This preliminary training action allows the models to be deployed with high detection accuracy already embedded, eliminating the need for extensive real-time training while maintaining high precision in distinguishing intentional from unintentional movements during actual surgical procedures.
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
This approach effectively reduces the risk of patient injury by accurately differentiating between intentional and unintentional controller motions, allowing for timely restriction of robotic arm movements, thereby enhancing safety during surgical procedures.
Implementation Method 1
An accelerometer disposed within the controller may output movement data
Implementation Method 2
A gyroscope disposed within the controller may output movement data
Data Source
AI summary
A user interface system includes one or more controllers configured to move freely in three dimensions, where the one or more controllers include an inertial sensor coupled to measure movement of the one or more controllers, and output movement data including information about the movement. The user interface system further includes a processor coupled to receive the movement data, where the processor includes logic that, when executed by the processor, causes the user interface system to perform operations, including receiving the movement data with the processor, identifying an unintentional movement in the movement data with the processor, and outputting unintentional movement data that identifies the unintentional movement.