BCI and FBG Sensor Fusion for Prosthetic Touch Feedback
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Solution Overview
Problem
Amputees using prosthetic devices lack the sensation of touch, as existing prosthetics do not provide feedback on contact with surfaces, which is crucial for maintaining balance and performing daily tasks effectively.
Innovation Solution
A system that combines fiber Bragg grating (FBG) sensors on an insole to measure plantar pressure and a brain control interface (BCI) to capture EEG signals, processing these signals to generate a touch signal that is relayed to a prosthetic limb or haptic feedback system, allowing amputees to perceive their state of movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If prosthetic devices are used to replace amputated limbs, then mobility and basic functionality are restored, but the sensation of touch and sensory feedback are lost
Solution Approach 1:
The patent implements a feedback system where FBG sensors detect plantar pressure on the prosthetic foot, this pressure information is processed through a classifier and machine learning model to generate touch signals, which are then delivered back to the user via electrical stimulation. This closed-loop feedback restores the sensory information that was lost during amputation, allowing users to perceive ground contact and pressure distribution.
Solution Approach 2:
The patent uses FBG sensors as intermediaries to detect mechanical pressure that the user cannot directly sense. These sensors act as mediators between the physical environment (ground contact) and the user's nervous system, converting mechanical pressure into measurable optical signals that can be processed and transmitted as tactile feedback through the BCI system.
2Loss of information
If multiple sensors and processing systems are integrated to provide touch feedback, then sensory perception is restored, but device complexity increases
Solution Approach 1:
The patent employs a multi-functional system where the FBG sensors serve multiple purposes: detecting plantar pressure, providing input data for the classifier, and enabling generation of touch signals. The same BCI infrastructure processes both the sensor data and generates the electrical stimulation output, reducing the need for separate dedicated components for each function.
Solution Approach 2:
The patent combines the sensing system (FBG sensors), processing system (classifier and machine learning model), and actuation system (electrical stimulation delivery) into an integrated prosthetic control platform. This merging of functions into a unified system reduces overall complexity compared to having separate independent systems for each function.
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 amputees to regain the sensation of touch, improving balance and movement by providing tactile feedback corresponding to the state of the prosthetic device, enhancing their ability to perform daily tasks like walking or standing.
Implementation Method 1
receiving, with a processing circuitry of a computer controller, a plurality of wavelength signals corresponding to an applied plantar pressure from a foot of the subject
Implementation Method 2
receiving, with the processing circuitry of the computer controller, a plurality of electroencephalography (EEG) signals corresponding to brain signals of the subject
Data Source
AI summary
A method of generating a touch signal corresponding to a state of a subject, the method including, receiving, from a foot of the subject, multiple wavelength signals corresponding to an applied plantar pressure based on the state of the subject. The method further includes receiving, using channels of a BCI mounted on the subject's head, a plurality of EEG signals (brain signals) that corresponds with wavelength signals. The method further includes transmitting EEG signals to train a classifier to identify a correlation between EEG signals and wavelength signals. The method further includes selecting a subsection of channels with high correlation with wavelength signals. The method further includes forming a secondary dataset by combining the subsection of channels with wavelength signals. The secondary dataset is passed to train a ML model to generate the touch signal corresponding to the subject's state that is relayed to a lower limb prosthesis.