Wearable Robot Parameter Tuning Using Physiological Feedback
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Solution Overview
Problem
Current wearable robot personalization methods are device-specific and require reprogramming, making them less accessible and inefficient due to high inter-subject variability in biomechanics, leading to varying assistance outcomes.
Innovation Solution
An open-source, device-independent personalization framework using human-in-the-loop optimization with cost functions and optimization algorithms that adjust wearable robot parameters based on physiological responses, incorporating modules for human-machine interaction, cost function estimation, and Bayesian optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If device-specific personalization methods are used to address inter-subject variability, then assistance outcomes can be optimized for individual users, but the system requires reprogramming and becomes less accessible
Solution Approach 1:
The patent implements a universal communication protocol that enables a single device architecture to work with multiple different wearable robots and measurement devices. The standardized interface allows the optimization system to communicate with various devices without requiring device-specific reprogramming, thus maintaining personalization accuracy while improving accessibility. The protocol acts as a universal translator between diverse hardware components and the optimization algorithm.
Solution Approach 2:
The system dynamically adjusts wearable robot parameters (such as torque, stiffness, and assistance level) based on real-time physiological feedback from measurement devices. By continuously modifying these parameters through the optimization algorithm, the system achieves personalized assistance outcomes without requiring physical reconfiguration or reprogramming of the device, thereby maintaining accuracy while enhancing ease of operation.
2Reliability
If wearable robot parameters are manually adjusted for each user, then personalized assistance can be provided, but the process becomes time-consuming and complex
Solution Approach 1:
The system automatically performs parameter optimization through an closed-loop process where physiological measurement devices provide real-time feedback to the optimization algorithm, which then autonomously adjusts wearable robot parameters. This self-service mechanism eliminates the need for manual configuration by therapists or technicians, reducing both time and complexity while maintaining reliable personalized assistance. The system serves itself by continuously monitoring and adjusting parameters based on user response.
Solution Approach 2:
The patent implements a feedback-driven optimization loop where physiological measurements (such as muscle activity, joint angles, or metabolic rate) are continuously monitored and fed back to the control system. The optimization algorithm processes this feedback and automatically adjusts robot parameters to improve assistance effectiveness. This automated feedback mechanism replaces complex manual tuning procedures with an intelligent, adaptive system that reduces configuration complexity while ensuring reliable personalized performance.
3Productivity
If real-time physiological feedback is used to optimize wearable robot dynamics, then energy efficiency and comfort are improved, but the system requires continuous data processing and control adjustments
Solution Approach 1:
The system performs preliminary optimization by pre-calculating optimal parameter ranges and preparing adjustment strategies before actual use. The communication protocol includes pre-configured parameter sets and optimization algorithms that are ready to execute, reducing the real-time computational burden. This preliminary preparation allows the system to maintain high energy efficiency through automated control while reducing the processing load during actual operation.
Data Source
AI summary
A system for customizing a wearable robot is disclosed. The system includes at least one human parameter measurement device and a computing device configured to receive feedback from the at least one human parameter measurement device. The computing device is further configured to communicate with the wearable robot to adjust dynamics of the wearable robot. The computing device has at least one processor and memory in communication with the at least one processor. The memory has instructions that, when executed by the at least one processor, cause the at least one processor to apply an optimization algorithm to iteratively adjust the dynamics of the wearable robot. Each iterative adjustment of the dynamics of the wearable robot by the optimization algorithm includes the steps of receiving at least one measurement from the at least one human parameter measurement device and adjusting the dynamics of the wearable robot based on the at least one measurement.