Personalized AFO Controller Selection for Crouch Gait
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Selecting an optimal Ankle Foot Orthosis (AFO) for individuals with crouch gait, particularly in children with cerebral palsy, is challenging due to the difficulty in predicting how a specific AFO design will impact muscle action and reduce the energy cost of walking, as conventional AFOs lack personalized optimization.
Innovation Solution
A method and system for personalized and optimal selection of AFO using motion-captured crouch gait data, joint ankle angle kinematics, and musculoskeletal human lower limb models to compute AFO torque, muscle forces, and response metrics, ranking AFO controllers based on muscle impulse, yank, co-activation, and energetic cost of walking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional passive AFOs are prescribed to assist ankle dynamics and improve gait kinematics, then ankle stabilization and prevention of foot drop are achieved, but the energy cost of walking is not optimized and muscle demand is not reduced
Solution Approach 1:
The patent applies dynamics by transitioning from static, passive AFO designs to dynamic, adaptive AFO controllers that modify stiffness and equilibrium angle in real-time based on gait phase and subject-specific parameters. The AFO controller adjusts mechanical properties dynamically to optimize energy storage and release during the gait cycle, reducing the energy cost of walking while maintaining ankle stabilization.
Solution Approach 2:
The patent implements parameter changes by modifying key AFO parameters (stiffness, equilibrium angle) based on computed muscle response metrics and subject-specific data. The system optimizes these parameters to maximize passive energy storage and release, thereby reducing the energy cost of walking while maintaining reliable ankle stabilization and foot drop prevention.
2Stability of the object's composition
If solid AFO is used to counteract excessive knee flexion during stance phase, then knee kinematics are normalized, but push-off power is not enhanced
Solution Approach 1:
The patent applies dynamics by enabling the AFO controller to adapt its mechanical behavior during different gait phases. During stance phase, the controller maintains stiffness for knee stabilization, while during push-off phase, it modifies parameters to enhance power generation. This dynamic adjustment allows the system to simultaneously achieve knee kinematics normalization and push-off power enhancement.
Solution Approach 2:
The patent implements periodic action by applying different mechanical characteristics to the AFO during different phases of the gait cycle. The controller periodically adjusts stiffness and equilibrium angle to provide knee stabilization during stance phase and power enhancement during push-off phase, optimizing both functions through phase-specific parameter modulation.
3Power
If spring-like AFO is used to enhance push-off power, then push-off power is improved, but knee flexion control is limited
Solution Approach 1:
The patent applies dynamics by enabling the AFO controller to transition between different mechanical behaviors based on gait phase detection. The system dynamically increases stiffness during stance phase for knee flexion control, then reduces stiffness during push-off phase to allow spring-like power enhancement. This temporal separation of functions resolves the contradiction between knee control and push-off power.
Solution Approach 2:
The patent implements periodic action by applying high stiffness during stance phase for knee flexion control and low stiffness during push-off phase for power enhancement. This periodic modulation of mechanical properties allows the AFO to sequentially perform both functions—knee stabilization and push-off power generation—without compromising either.
4Adaptability or versatility
If multiple AFO variants are available to address different gait requirements, then treatment options are increased, but selection of optimal AFO becomes challenging
Solution Approach 1:
The patent applies feedback by using motion-captured gait data and computed muscle response metrics to guide AFO controller selection. The system evaluates multiple AFO variants against subject-specific gait characteristics and selects the optimal controller based on predicted performance in reducing energy cost and improving gait efficiency. This feedback-driven selection process simplifies the decision-making despite the availability of multiple adaptive AFO options.
Solution Approach 2:
The patent implements self-service by enabling the AFO controller to automatically adjust its parameters based on real-time gait phase detection and subject-specific mechanical properties. The selected AFO controller autonomously optimizes its performance without requiring manual intervention or complex selection processes, as the system self-adjusts to provide optimal treatment based on the subject's unique gait characteristics.
Data Source
AI summary
In state of art techniques, it is challenging to predict how a specific Ankle Foot Orthosis (AFO) will impact muscle action and reduce an energy cost of walking for individual subjects. The disclosed method focusses on personalized and optimal selection of an AFO controller using an AFO torque, and a plurality joint ankle angles of each of a plurality of AFO controllers integrated with a musculoskeletal human lower limb model (MHLLM). The plurality of muscle forces is computed using the MHLLM for each of the plurality of AFO controllers. Further the method computes a plurality of muscle response metrics, from the plurality of muscle forces and an additional joint torque for each of the AFO controllers. Further the method combines the plurality of muscle response metrics which enables the selection of a personalized optimal AFO controller among the plurality of AFO controllers of a (cerebral palsy) CP subject.