Prosthetic Hand Fine Motor Control via EMG Signal Segmentation
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Solution Overview
Problem
Current prosthetic hands lack fine motor control capabilities, making it difficult for users to perform tasks that require delicate finger movements, such as writing, painting, or eating, as they rely on preprogrammed grips and external muscle use to generate motion.
Innovation Solution
A prosthetic hand system utilizing electromyographic (EMG) signals from the forearm muscles to control finger motion, with electrodes placed on the skin or implanted to detect neuronal signals, determining a threshold for intentional muscle contractions to activate motors and mimic natural stroking motions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If preprogrammed grips are used in prosthetic hands, then device complexity is reduced, but fine motor control capability is lost
Solution Approach 1:
The control system is segmented into multiple independent EMG sensor channels, each monitoring specific forearm muscles. This allows the system to process multiple muscle signals simultaneously to generate complex finger movements, resolving the contradiction by dividing the control function into manageable segments that collectively provide fine motor control.
Solution Approach 2:
The prosthetic hand transitions from static preprogrammed grips to dynamic, real-time control based on EMG signal processing. The system dynamically adjusts finger positions and forces by continuously monitoring muscle activation patterns, enabling adaptive fine motor control while maintaining manageable system complexity through incremental control algorithms.
2Adaptability or versatility
If multiple EMG signals are processed to control individual fingers, then fine motor control is improved, but device complexity increases
Solution Approach 1:
The system performs preliminary processing of EMG signals by filtering and normalizing muscle activation patterns before they reach the control algorithm. This preliminary action simplifies the subsequent control processing by providing pre-processed, standardized signals, thereby reducing the overall computational complexity while maintaining fine motor control precision.
Solution Approach 2:
The patent introduces an intermediary control layer that translates complex multi-channel EMG signals into simplified motor commands for individual fingers. This intermediary processing stage acts as a mediator between the complex sensor input and the actuator output, managing signal processing complexity while preserving fine motor control capabilities.
3Measurement precision
If electrodes are placed to maximize peak signal detection, then signal quality is improved, but baseline signal increases
Solution Approach 1:
The system dynamically adjusts the threshold parameters for signal activation based on the detected baseline signal levels. By changing the activation threshold parameter in response to baseline conditions, the system maintains high peak signal detection quality while compensating for increased baseline noise, thereby preserving the effective signal-to-noise ratio.
Solution Approach 2:
The patent employs partial signal activation where only portions of the detected EMG signal exceeding the threshold are used to trigger finger movements. This selective action filters out baseline noise while capturing meaningful muscle activation patterns, improving peak signal detection without being unduly affected by elevated baseline signals.
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 users to perform tasks like writing and painting with naturalistic finger movements, reducing strain on arm and shoulder muscles and improving dexterity, while being potentially more affordable with advancements in 3D printing and electronics.
Implementation Method 1
The systems and methods described herein use electromyographic (EMG) signals and, more particularly, combinations of electromyographic signals, from muscles in the forearm to activate one or more motors of the prosthetic hand
Implementation Method 2
various sensors (e.g., electrodes) to detect the action potential propagation through a muscle—also called electromyography—and use the detection of the action potential as an input signal
Data Source
AI summary
The present invention generally relates to a system and method for fine motor control of fingers on a prosthetic hand. In particular, the present disclosure describes a system and method for controlling the flexion or extension of one or more fingers of a prosthetic hand to reproduce a natural stroke such as for, e.g., writing, painting, brushing teeth, or eating. The systems and methods described herein use electromyographic (EMG) signals and, more particularly, combinations of electromyographic signals, from muscles in the forearm to activate one or more motors of the prosthetic hand that control the motion of the prosthetic fingers. The electromyographic signals may be used to cause fingers of a prosthetic hand to, for example, imitate a writing stroke while the fingers of the prosthetic hand hold a writing utensil. Additionally, the present invention describes electrode placement locations that maximize peak signal detected while maintaining a low base-line signal.