Ankle Exoskeleton Personalized Control via Biomechanical Simulation
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Solution Overview
Problem
Current exoskeleton devices for gait rehabilitation are not tailored to individual users, leading to insufficient improvement in gait function restoration and lack of active engagement during rehabilitation, as they do not effectively track user performance over time or provide real-time feedback.
Innovation Solution
An ankle exoskeleton device that collects biomechanical data points, develops individualized musculoskeletal simulations, and optimizes design and control parameters using predictive simulations to provide personalized assistance or resistance, incorporating sensors and actuators for dynamic actuation and stability control, and offers feedback through various modalities for enhanced user engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If exoskeleton devices use standardized control parameters for all users, then device complexity is reduced, but gait rehabilitation effectiveness deteriorates due to lack of personalization
Solution Approach 1:
The system performs preliminary gait analysis and muscle activation pattern detection before rehabilitation treatment begins. Biomechanical data is collected and used to pre-compute personalized control parameters and musculoskeletal simulations, so that when rehabilitation starts, the device is already optimized for the individual user's specific needs, resolving the contradiction between personalization and complexity.
Solution Approach 2:
The system creates virtual copies of the user's musculoskeletal system through computational modeling. These digital twins replicate individual anatomy and physiology, allowing personalized control strategies to be developed and tested virtually before implementation, thereby achieving personalization without proportionally increasing physical device complexity.
2Reliability
If exoskeleton devices collect and process extensive biomechanical data for personalization, then gait rehabilitation effectiveness is improved, but device complexity and data processing requirements worsen
Solution Approach 1:
The system extracts only the most critical biomechanical features and muscle activation patterns from extensive sensor data using the pre-computed musculoskeletal simulations. By identifying and focusing on key parameters that most influence gait rehabilitation outcomes, the system achieves personalization effectiveness while reducing the complexity of real-time data processing requirements.
3Reliability
If exoskeleton devices provide active engagement and real-time feedback, then user motivation and rehabilitation outcomes improve, but device complexity and control algorithm complexity worsen
Solution Approach 1:
The system implements real-time feedback by continuously monitoring biomechanical data and comparing actual performance against the personalized musculoskeletal model predictions. The control algorithm adjusts actuation commands based on this feedback loop, providing active engagement and adaptive assistance that improves rehabilitation outcomes while managing complexity through model-based control strategies.
Data Source
AI summary
A method of using an ankle exoskeleton device is provided herein. The method includes collecting one or more biomechanical data points from an individual. The method also includes developing individualized musculoskeletal simulations based on the one or more biomechanical data points. In addition, the method includes creating predictive simulations by modeling effects of an ankle exoskeleton device on the individualized musculoskeletal simulations. The method also includes utilizing established device-user relationships with real-time measurements to adjust device control. Lastly, the method includes optimizing design parameters of the exoskeleton device based on the predictive simulations and user responses.