AI Neuroprosthetic Hand Control With Nerve Signal Decoding
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Solution Overview
Problem
Current neuroprosthetic systems face challenges in accurately sensing, processing, and modulating neural signals for precise control of prosthetic limbs due to issues such as poor signal-to-noise ratio, inability to record microvolt-level nerve activity, mismatch errors, and inefficiencies in deep learning deployment on low-power platforms, leading to suboptimal movement control and computational complexity.
Innovation Solution
A neuroprosthetic device with a nerve interface comprising frequency-shaping neural recorders and redundant crossfire stimulators, combined with an artificial intelligence engine and deep learning neural decoder, enables simultaneous recording and stimulation, and translates brain signals into precise prosthetic limb movements, using a portable AI engine and edge computing platform for real-time control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning artificial intelligence neural network-based algorithms are used to analyze and decode neural signals, then movement control precision is improved, but computational complexity increases
Solution Approach 1:
The deep learning model is segmented into multiple components: feature extraction layer, temporal processing layer, and control signal generation layer. This segmentation allows parallel processing of different signal aspects, reducing overall computational complexity while maintaining precision in movement control.
Solution Approach 2:
Neural signals are pre-processed and filtered before being input to the deep learning algorithm. Feature extraction is performed in advance to identify relevant patterns, reducing the computational burden on the main decoding algorithm and enabling faster real-time processing.
2Weight of moving object
If conventional central processing units are used for deep learning processing, then device portability is improved, but processing efficiency deteriorates
Solution Approach 1:
The patent replaces conventional CPU-based processing with specialized neural processing units (NPUs) or field-programmable gate arrays (FPGAs) optimized for neural network operations. This substitution maintains portability while achieving the processing efficiency required for real-time deep learning inference on neural signals.
3Reliability
If electrical stimulation is applied to modulate neural signals, then therapeutic outcome is improved, but signal-to-noise ratio deteriorates due to stimulation artifacts
Solution Approach 1:
Stimulation artifacts are extracted and identified separately from genuine neural signals using template matching and artifact subtraction algorithms. By isolating and removing these known artifact patterns, the system recovers the underlying neural signals, maintaining both therapeutic stimulation and accurate signal recording.
Solution Approach 2:
The system uses feedback from recorded neural signals to adaptively adjust stimulation parameters in real-time. This closed-loop control allows the system to optimize therapeutic outcomes while minimizing artifact generation, as the stimulation is continuously adjusted based on the actual neural response being recorded.
Data Source
AI summary
A prosthetic limb in amputation rehabilitation, having a forearm and a hand with four fingers and a thumb, with the wrist and the fingers & thumb thereof being fully independently controlled by nerve signals originating in the amputee's brain and not being controlled by the actions of nearby muscles in the amputee's upper arm or shoulder. Control of the prosthesis is achieved by a fully contained electronic unit in the forearm of the prosthesis that receives neural signals from the brain, converts the analog neural signals to digital signals that are fed into an artificial intelligence engine circuit that utilizes a library of algorithms to learn from the brain what the signals are that will produce a desired hand and finger movement, then convert its computed digital output to analog electrical signals that are fed to the prosthetic hand and finger to produce actual motion as instructed by the brain.