EMG Exoskeleton Control for Predictive Movement Intent
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Solution Overview
Problem
Current actuated exoskeletons face challenges in accurately recognizing and predicting user movement intentions due to limitations in sensing technology and data-processing lag, leading to difficulties in controlling and powering these systems for extended periods.
Innovation Solution
An enhanced exoskeleton system that utilizes a control subsystem with a historical movement library, current and future intent classifiers, and a motor control module to process sensor data from EMG sensors, allowing for real-time recognition and prediction of user movement intentions through probabilistic pattern recognition techniques and machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional sensing technology and data-processing routines are used in actuated exoskeletons, then the system structure is established, but real-time input capability is hindered due to processing lag
Solution Approach 1:
The system performs preliminary classification of sensor data to identify current movement intent before full processing is required. By pre-processing and categorizing data streams to determine user intent early in the processing pipeline, the system reduces the critical path delay for actuation commands while maintaining accurate control.
2Adaptability or versatility
If multiple actuators are used to provide full-body exoskeleton functionality, then coverage and capability are improved, but system complexity and difficulty of control increase
Solution Approach 1:
The exoskeleton system is divided into multiple independent actuator modules, each controlled by localized intent classification. This segmentation allows each module to operate semi-autonomously based on local sensor data and movement intent, reducing the complexity of centralized control while maintaining full-body functionality.
Solution Approach 2:
A universal intent classification algorithm is implemented that can interpret sensor data from multiple body regions and translate it into appropriate actuator commands. This multi-functional approach allows the same control subsystem to manage diverse actuators across different body segments, reducing overall system complexity.
3Measurement precision
If traditional EMG sensors and processing methods are used, then muscle activity can be detected, but accurate prediction of future movement intent is difficult due to processing lag
Solution Approach 1:
The system performs preliminary classification of EMG sensor data to identify current movement intent patterns before complete processing is required. By pre-processing EMG signals to recognize muscle activation patterns and predict intent early, the system reduces prediction lag while maintaining high accuracy in movement intent recognition.
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 naturalistic and efficient control of exoskeletons by accurately predicting user intentions, reducing lag and improving the ability to power and control the system for extended periods, enhancing user performance and operator interaction.
Implementation Method 1
the sensor data comprises an anticipatory data value from an electromyography (EMG) sensor
Data Source
AI summary
An enhanced exoskeleton system is disclosed comprising an exoskeleton, a base layer comprising at least one sensor, an exoskeleton actuator configured to actuate the exoskeleton, a control subsystem comprising one or more processors, and memory elements including instructions that, when executed, cause the processors to perform operations comprising: receiving sensor data from the sensor, determining a future movement intent of a user of the exoskeleton, determining a command for an exoskeleton actuator based on the future movement intent, and communicating the command to the exoskeleton actuator whereby the exoskeleton is actuated by the exoskeleton actuator. In some embodiments, the sensor data comprises an anticipatory data value from an anticipatory sensor. In some embodiments, the sensor data comprises an anticipatory data value from an electromyography (EMG) sensor.