EMG-Controlled Powered Knee Torque for Natural Stair Climbing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing powered prosthetic limbs, particularly for trans-femoral amputees, struggle to enable users to climb stairs naturally, requiring excessive mental effort and physical fatigue due to simplistic control mechanisms that do not replicate the natural gait cycle.
Innovation Solution
A volitional controller that integrates EMG, GRF, and IMU sensors to determine continuous knee torque adjustments, allowing the prosthetic limb to mimic natural flexion and extension based on user muscle activation and limb position, enabling more natural walking and climbing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a powered knee prosthesis uses a volitional controller with multiple sensors (EMG, GRF, IMU) to determine continuous knee torque adjustments, then the ability to climb stairs naturally is improved, but the device complexity increases
Solution Approach 1:
The control system is segmented into multiple independent sensor modules (EMG sensor for muscle activation, GRF sensor for ground reaction force, IMU for limb position) that each capture specific aspects of gait. These segmented sensors feed into a integrated controller that processes their signals separately before combining them to determine knee torque adjustments, allowing complex control to be built from manageable components
Solution Approach 2:
The volitional controller is designed as a multi-functional device that simultaneously processes EMG signals for muscle activation detection, GRF signals for weight-bearing status, and IMU signals for limb positioning. This universal controller handles multiple sensing functions and integrates them to provide comprehensive knee torque control for various activities including stair climbing and level walking
2Ease of operation
If the prosthesis uses continuous torque adjustments based on multiple sensor inputs, then the naturalness of walking and climbing is improved, but the computational requirements and processing complexity increase
Solution Approach 1:
The system performs preliminary signal conditioning and filtering on EMG, GRF, and IMU signals before they are fed into the torque calculation algorithm. Each sensor signal is pre-processed to remove noise and extract relevant features (e.g., muscle activation levels from EMG, force magnitude from GRF, angle and velocity from IMU), reducing the computational burden during real-time torque determination
Solution Approach 2:
The controller implements continuous feedback by constantly monitoring inputs from all three sensors and dynamically adjusting knee torque in real-time based on the current gait phase and user needs. The system compares actual limb position and ground reaction forces with expected values and makes corrective torque adjustments to maintain natural gait patterns during activities like stair climbing
3Measurement precision
If the EMG signal is mapped to different torque signals based on ground state (flexion when off ground, extension when on ground), then the accuracy of gait cycle replication is improved, but the control system complexity increases
Solution Approach 1:
The ground reaction force sensor serves as an intermediary that detects the ground state (whether the prosthetic foot is in contact with the ground) and translates this physical contact information into a control signal that modulates the EMG-to-torque mapping. This intermediary allows the system to automatically switch between flexion and extension torque modes based on actual ground contact rather than relying on complex temporal analysis
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 climb stairs with reduced fatigue and improved balance, allowing hands-free operation and more natural gait, even for those with dual upper leg amputations.
Implementation Method 1
receive an electromyography (EMG) signal from a single EMG sensor source
Implementation Method 2
receive a ground state signal from a ground reaction force (GRF) sensor
Implementation Method 3
receive an inertial measurement unit (IMU) signal from an IMU sensor. Determination of the target knee torque may be further based on the IMU signal. Specifically, the IMU signal may be used to determine a thigh angle and/or a knee angle
Implementation Method 4
output a knee torque signal for controlling a powered knee joint of a powered knee prosthesis
Data Source
AI summary
Disclosed are prosthetic systems comprising a powered knee upper leg prosthesis and a volitional controller configured to provide control of the prosthesis to the user. The prosthetic system may be configured to enable a user to climb a set of stairs. The prosthetic system may be activated by the activation of an EMG signal source, such as the biceps femoris muscle of the upper leg. The volitional controller of the prosthetic system may be further configured to receive a ground state signal and/or an IMU signal to determine a target knee torque for operating the powered knee of the prosthesis.