Exosuit Control Optimization for Personalized Gait Assistance
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Solution Overview
Problem
Wearable exosuits and exoskeletons exhibit significant variability in effectiveness across different individuals due to the need for individualized assistance patterns, leading to inconsistent metabolic benefits and reduced efficacy.
Innovation Solution
The development of systems and methods to optimize actuation parameters in real-time using wearable sensors, allowing for adjustments in actuation timing, amplitude, rate, and profile shape to maximize or minimize objective functions such as energy expenditure, comfort, and operational efficiency, through gradient-descent or Bayesian optimization approaches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a one-size-fits-all control approach is used for exosuits, then device complexity is reduced, but effectiveness and metabolic benefits vary significantly across different individuals
Solution Approach 1:
The control system dynamically adapts actuation parameters in real-time based on individual user characteristics and gait patterns. The controller modifies actuation timing, amplitude, and profile shape during operation to optimize effectiveness for each user, transforming a static one-size-fits-all approach into a dynamic personalized system.
Solution Approach 2:
The system changes multiple actuation parameters simultaneously including timing, amplitude, rate, and profile shape to tailor the assistance pattern to each individual user. These parameter adjustments are made in real-time based on measured performance metrics, allowing the exosuit to adapt to individual variability without requiring complete redesign for each user.
2Reliability
If actuation parameters are optimized in real-time for each individual, then effectiveness and metabolic benefits are maximized, but device complexity and computational requirements increase
Solution Approach 1:
The control system uses feedback from sensors measuring metabolic cost, gait parameters, and performance metrics to continuously adjust actuation parameters. This closed-loop feedback mechanism enables real-time optimization of effectiveness while keeping the control architecture manageable through systematic parameter adjustment based on measured outcomes.
Solution Approach 2:
The system performs preliminary identification of individual user characteristics and gait patterns during initial operation or calibration phases. This preliminary action establishes a baseline model for each user that guides subsequent real-time parameter optimization, reducing the computational burden during actual operation by pre-characterizing user-specific parameters.
3Adaptability or versatility
If multiple actuation parameters are adjusted simultaneously, then individualized assistance patterns are achieved, but control difficulty and optimization complexity increase
Solution Approach 1:
The control system segments the optimization process into distinct parameter groups or adjustment stages. Rather than optimizing all parameters simultaneously, the system divides the complex multi-parameter optimization into manageable segments, adjusting timing, amplitude, and profile shape in a structured sequence or through modular optimization routines.
Data Source
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AI summary
A wearable system comprising an exosuit or exoskeleton; an actuator(s) configured to generate force in the exosuit or exoskeleton; a sensor(s) configured to measure information for evaluating an objective function associated with providing physical assistance to the wearer, an interaction between the wearer and the exosuit or exoskeleton, and/or an operation of the exosuit or exoskeleton; and a controller(s) configured to: actuate the actuator(s) according to an actuation profile(s), evaluate the objective function based on the information measured by the at least one sensor to determine a resulting change in the objective function, adjust a parameter(s) of the actuation profile(s) based on the resulting change in the objective function, and continue to actuate, evaluate, and adjust to optimize the actuation parameter(s) for maximizing or minimizing the objective function. Wearable systems configured to assist or promote an improvement in the wearer's gait and optimized using a gradient descent or Bayesian approach.