Autoconfiguring Myoelectric Prosthesis Control Algorithm
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Solution Overview
Problem
Conventional myoelectric prostheses require extensive initial configuration and recalibration, often necessitating additional hardware and technological capacity, limiting user capability and convenience, especially for pattern-recognition controlled prosthetic limbs which demand specific user data for optimal performance.
Innovation Solution
A prosthesis guided training system incorporating sensors for electromyographic activity and a computing device with a real-time pattern recognition control algorithm and an autoconfiguring pattern recognition training algorithm, allowing for on-device processing and adaptation of operational parameters to enhance movement control and recalibration without the need for external hardware or visual displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pattern recognition training systems are used to achieve optimal prosthesis performance, then movement control accuracy is improved, but device complexity and user burden increase due to requirements for additional hardware and technological capacity
Solution Approach 1:
The prosthesis system performs self-calibration and self-training using the autoconfiguring pattern recognition training algorithm. The system automatically collects EMG data, identifies movement patterns, and configures control parameters without requiring external training equipment or专业技术 personnel, enabling users to maintain optimal performance independently
Solution Approach 2:
The control system integrates both real-time pattern recognition control and autoconfiguring training functions within a single device. The same processor and sensors used for operation are leveraged for calibration, eliminating the need for separate training hardware and making the system adaptable to different user needs and conditions
2Measurement precision
If extensive initial configuration and recalibration are performed to optimize prosthesis performance, then movement control precision is improved, but time consumption and user convenience deteriorate
Solution Approach 1:
The system performs preliminary calibration automatically during initial setup and allows for quick recalibration when needed. The autoconfiguring algorithm pre-processes EMG data and rapidly identifies movement patterns, significantly reducing the time required compared to conventional manual configuration methods
Solution Approach 2:
Users can independently perform recalibration without requiring visits to healthcare professionals or extensive training sessions. The system guides users through automated procedures that adapt to their specific muscle patterns, reducing both time and expertise requirements
3Reliability
If additional hardware and technological capacity are provided for pattern recognition training, then prosthesis performance is improved, but ease of operation and user capability are reduced
Solution Approach 1:
The prosthesis system autonomously performs the complex task of pattern recognition training using built-in sensors and processors. Users simply need to perform natural movements while the system automatically collects data, processes it through the training algorithm, and configures optimal control parameters, making the process as easy as moving the limb
Solution Approach 2:
The system combines sensing, processing, training, and control functions into an integrated unit. The same microprocessors and sensors used for operation handle training functions, eliminating the need for separate training equipment and simplifying the user interface to require no specialized knowledge
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
Enables efficient and user-friendly recalibration and operation of myoelectric prostheses, improving performance and convenience by allowing self-calibration and reducing the need for additional equipment, enhancing user experience and reducing the number of sessions required for satisfactory performance.
Implementation Method 1
a plurality of sensors for detecting electromyographic activity. A computing device, which can include a processor and memory, extracts data from the electromyographic activity
Data Source
AI summary
Embodiments of the invention provide for a prosthesis guided training system that includes a plurality of sensors for detecting electromyographic activity. A computing device, which can include a processor and memory, can extract data from the electromyographic activity. A real-time pattern recognition control algorithm and an autoconfiguring pattern recognition training algorithm can be stored in the memory. The computing device can determine movement of a prosthesis based on the execution of the real-time pattern recognition control algorithm. The computing device can also alter operational parameters of the real-time pattern recognition control algorithm based on execution of the autoconfiguring pattern recognition training algorithm.