Exoskeleton Controller Mode Switching via Neural Signals
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Solution Overview
Problem
Conventional robotic exoskeletons are limited in their ability to automatically transition between controlled activities, which require precise movement and low force, and explosive activities, which demand high force and velocity, necessitating manual mode switching, which is inconvenient and awkward for users.
Innovation Solution
A controller responsive to neural and neuromuscular sensors, such as EEG and myoelectric sensors, automatically selects between controlled and explosive activity modes based on detected user intentions, allowing seamless transitions between different operational scenarios without user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual mode switching is used to transition between controlled and explosive activities, then the exoskeleton can provide different operational modes, but the operation becomes inconvenient and awkward for users
Solution Approach 1:
The exoskeleton system automatically detects user intent through EEG and EMG sensors and autonomously transitions between controlled and explosive activity modes without requiring manual user input. The system serves itself by interpreting neural and muscular signals to determine the appropriate operational mode, eliminating the need for awkward manual switching mechanisms
Solution Approach 2:
The system continuously monitors EEG signals from the brain and EMG signals from muscles to detect user intent in real-time. This feedback loop allows the exoskeleton to automatically adjust its operational mode based on the user's neural and muscular states, providing seamless transitions between controlled and explosive activities without manual intervention
2Force
If the exoskeleton provides amplified force for explosive activities, then high force and velocity are achieved, but precise movement control becomes difficult
Solution Approach 1:
The exoskeleton dynamically adjusts its control characteristics based on the detected activity mode. During explosive activities, the system provides high force amplification with relaxed precision control, while during controlled activities, it switches to high-precision movement control with reduced force amplification. This dynamic adaptation allows the system to optimize both force and precision for different task requirements
Solution Approach 2:
The control system changes key parameters including force amplification factor, velocity limits, and control stiffness based on the detected activity mode. When transitioning from controlled to explosive mode, the system increases force amplification and velocity parameters while reducing position control stiffness, enabling the same hardware to deliver both precise slow movements and powerful fast movements
3Measurement precision
If the exoskeleton provides precise movement control for controlled activities, then accurate positioning is achieved, but force output is limited
Solution Approach 1:
The system dynamically switches between two control regimes: a high-precision low-force regime for controlled activities and a high-force low-precision regime for explosive activities. The transition between these regimes is automatically triggered by detected changes in user intent through EEG and EMG signal analysis, allowing the exoskeleton to optimize its performance characteristics for the current task
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 the exoskeleton to adapt its operation dynamically to match user needs, providing transparent control during controlled activities and amplified force during explosive activities, enhancing user experience and operational efficiency.
Implementation Method 1
EEG sensors are used for detecting electrical signals from a brain of a user
Implementation Method 2
myoelectric sensors are used for detecting electrical signals from a body of a user
Data Source
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AI summary
System and method for operating a robotic exoskeleton involves using a control system (107) to monitor an output one or more electrical activity sensors (202) disposed on a human operator. The control system determines if an output of the electrical activity sensors corresponds to a predetermined neural or neuromuscular condition of the user. Based on the determining step, the control system automatically chooses an operating mode from among a plurality of different operating modes. The operating mode selected determines the response the control system will have to control inputs from the human operator.