Upper Limb Prosthetic Control Through EMG and Motion Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing upper limb prosthetic control systems have limited control input capability, leading to complexity in achieving advanced output control movements.
Innovation Solution
The integration of electromyography (EMG) sensors and motion sensors, such as inertial measurement units (IMUs), to enhance the control of upper limb prosthetics by analyzing muscle contractions and limb movements, allowing for advanced analysis techniques and generation of control signals for prosthetic movements like digit actuation, hand and wrist rotation, and elbow rotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensor types (EMG and motion sensors) are integrated to increase control input capability, then the dexterity and functionality of the prosthesis are improved, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensor types (EMG sensors and motion sensors such as IMUs) into a unified control system that processes signals from both sources simultaneously. This merging allows the system to leverage the complementary strengths of each sensor type - EMG for intent detection and motion sensors for actual movement tracking - thereby increasing control input capability without requiring separate control systems for each sensor type.
Solution Approach 2:
The control system is designed to handle multiple types of input signals from different sensor modalities through a unified processing framework. The system can interpret various control inputs (muscle contractions, limb movements, gestures) and translate them into appropriate prosthetic actions, making the control system multi-functional and adaptable to different control strategies.
2Measurement precision
If advanced analysis techniques are used to process EMG and motion data, then the precision of prosthetic control is improved, but the computational complexity increases
Solution Approach 1:
The system performs preliminary processing and filtering of EMG and motion sensor data before full analysis. Raw signals are pre-processed to remove noise and extract relevant features, which reduces the computational burden of subsequent advanced analysis techniques while maintaining control precision.
Solution Approach 2:
The control system incorporates feedback mechanisms where the processed EMG and motion data continuously inform adjustments to prosthetic actuation. This feedback loop allows the system to refine control signals in real-time based on actual sensor measurements, improving precision through iterative optimization rather than requiring overly complex open-loop control algorithms.
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 increases the dexterity and functionality of prosthetics without complicating their use, enabling more precise and responsive control of prosthetic devices.
Implementation Method 1
The prosthesis may detect electromyography (EMG) signals produced by one or more muscles
Implementation Method 2
detect motion signals associated with one or more prosthetics or limbs
Data Source
Figure 1A
Figure 1B
Figure 1C
AI summary
A prosthesis and control approach using electromyography (EMG) data and motion data. EMG sensors and a motion sensor provide inputs to generate control signals. The EMG sensor detects EMG signals from the user's body. The motion sensor may be one or more inertial measurement sensors (IMS) and/or a magnetic field sensor. The EMG and motion data is analyzed according to various techniques to provide control of one or more actuatable prosthetic joints of an upper limb prosthesis, such as a prosthetic elbow, wrist, hand, and/or digits.