Exosuit Control Parameter Tuning for Personalized Gait Assistance
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Solution Overview
Problem
Soft exosuits exhibit high variability in efficacy across different individuals due to the need for individualized assistance patterns, leading to inconsistent metabolic benefits, as a one-size-fits-all approach fails to optimize actuation parameters effectively for each wearer.
Innovation Solution
The development of systems and methods that adjust actuation parameters such as timing, amplitude, rate, and profile shape of actuation to optimize objective functions related to physical assistance, interaction between the wearer and exosuit, and exosuit operation, using wearable sensors to evaluate and adjust these parameters in real-time to maximize efficacy and minimize variability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a one-size-fits-all control approach is used for exosuits, then device complexity is reduced and ease of operation is improved, but efficacy and metabolic benefit vary significantly across different individuals
Solution Approach 1:
The control system dynamically adapts actuation parameters in real-time based on individual wearer characteristics and gait patterns. The controller continuously adjusts timing, amplitude, and profile shape of actuation signals to optimize assistance for each user, transforming a static one-size-fits-all approach into a dynamic personalized system that maintains both ease of operation and high efficacy
Solution Approach 2:
The system optimizes multiple actuation parameters including timing relative to gait cycle, amplitude of actuation force, rate of actuation, and profile shape. By varying these parameters based on individual wearer data collected during operation, the system achieves personalized control without requiring complex manual configuration, resolving the contradiction between operational simplicity and treatment effectiveness
2Reliability
If actuation parameters are optimized for each individual wearer, then efficacy and metabolic benefit are maximized, but device complexity and control complexity increase
Solution Approach 1:
The exosuit control system performs self-adjustment by automatically collecting wearer data during operation and using this data to optimize actuation parameters in real-time. The system serves itself by eliminating the need for external manual configuration or complex setup procedures, achieving personalized optimization while maintaining relatively simple device architecture
Solution Approach 2:
The system implements closed-loop control by continuously monitoring wearer responses and gait patterns, then using this feedback to adjust actuation parameters. This automated feedback mechanism enables personalized optimization without requiring complex manual intervention, as the system self-regulates based on real-time performance data
3Use of energy by moving object
If real-time optimization is implemented during walking, then metabolic cost reduction is maximized for each subject, but computational requirements and control complexity increase
Solution Approach 1:
The system performs preliminary data collection and analysis during initial wear periods to establish baseline wearer characteristics and gait patterns. This preliminary action enables the controller to make informed real-time adjustments without requiring complex computational processing during actual walking, as much of the optimization work is prepared in advance based on collected data
Data Source
AI summary
A wearable system includes 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.