Neural Motion Control for Motorized Medical Component Support
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Solution Overview
Problem
Conventional motion control algorithms for medical devices are highly dependent on operator experience and skill, leading to inefficient and potentially unsafe movements, especially for inexperienced operators, and result in increased wear and tear on drive units.
Innovation Solution
A method using a neural network to process force measurement data from a control element, combined with environmental data, to determine control data for a drive unit, optimizing motor assistance and ensuring smoother, safer, and more efficient movement of medical device components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional motion control algorithms are used, then the system is simple to implement, but the movement efficiency and smoothness depend heavily on operator experience
Solution Approach 1:
The patent replaces the mechanical dependency on operator skill with an intelligent software system. A neural network processes force measurement data from sensors to generate optimized control signals, substituting the need for operator experience with automated intelligent decision-making. This resolves the contradiction by achieving high movement efficiency through AI while maintaining a relatively simple hardware architecture.
Solution Approach 2:
The control system performs self-optimization by automatically adapting to different operators and situations. The neural network learns from force measurement patterns and autonomously adjusts control parameters without requiring manual configuration or operator intervention. This enables the system to achieve high productivity automatically, resolving the contradiction between efficiency and complexity.
2Reliability
If force-assisted motion control with conventional algorithms is used, then the hardware configuration is simple, but jerky movements and wear occur with inexperienced operators
Solution Approach 1:
The patent replaces conventional control algorithms with a neural network-based intelligent system that processes force measurement data and generates smooth control signals. This intelligent substitution eliminates jerky movements and wear issues while maintaining simple hardware configuration, resolving the contradiction between reliability and complexity.
3Adaptability or versatility
If manual parameterization of motion control algorithms is implemented, then adaptability to different operators is improved, but the system becomes complex and difficult to configure
Solution Approach 1:
The neural network performs automatic adaptation to different operators by learning from their force measurement patterns. The system self-configures control parameters without requiring manual intervention, achieving high operator adaptability while keeping the system simple to deploy. This resolves the contradiction by automating the adaptation process that would otherwise be complex and difficult to configure.
Solution Approach 2:
The patent replaces manual parameterization processes with intelligent neural network-based automatic configuration. The system uses machine learning to adapt to different operators autonomously, eliminating the need for complex manual setup procedures while maintaining high adaptability.
4Loss of time
If conventional control systems are used, then the system is easy to operate, but workflow time is significantly lengthened for inexperienced operators
Solution Approach 1:
The patent replaces conventional control systems with an AI-based neural network that optimizes movement in real-time. This intelligent substitution reduces workflow time by providing optimized control signals regardless of operator experience level, while maintaining ease of operation through automatic adaptation.
Solution Approach 2:
The control system performs self-optimization to minimize workflow time by automatically adapting to each operator's characteristics. The neural network autonomously adjusts control parameters to achieve efficient movements, reducing time loss without requiring complex manual configuration or operator expertise.
Data Source
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AI summary
The invention relates to a technique for controlling a drive unit (306) for the motor assistance of a movement of a mobile medical device component (708). A computer-implemented method comprises acquiring (S102) force measurement data representing a force applied to a control element, in particular a handle (702), on the medical device component (708) for movement control. The acquired (S102) force measurement data are processed (S106) by means of a neural network to determine (S108) control data for controlling the drive unit (306) to provide motor assistance for the movement of the medical device component (708). The drive unit (306) is controlled (S110) by means of the determined (S108) control data.