Exoskeleton Intent Recognition With Adaptive Classification Updates
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Solution Overview
Problem
Conventional exoskeleton systems face challenges in accurately recognizing user intent in real-time, often resulting in delayed responses due to sensor noise and the need for expert supervision, which limits their adaptability and customization to individual user behavior.
Innovation Solution
The development of data-driven intent recognition programs that adapt over time, using sensor data from exoskeleton systems to refine classification methods automatically, allowing for unsupervised refinement and user-specific performance improvements without human intervention, and incorporating user feedback to tune adaptation speed and classification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sensor-based intent recognition is used, then the system can detect user movements, but the recognition accuracy is reduced due to sensor noise and delayed responses
Solution Approach 1:
The system implements feedback loops where classification results are continuously refined based on actual sensor data outcomes. The classification program is updated iteratively using feedback from sensor measurements, allowing the system to learn from past classifications and improve future recognition accuracy while reducing delays through adaptive tuning.
Solution Approach 2:
The exoskeleton system performs self-learning and self-tuning of its classification program without requiring external expert supervision. The system automatically refines its own intent recognition capabilities by processing sensor data and updating its classification algorithms, enabling autonomous improvement of recognition accuracy and response time.
2Measurement precision
If expert supervision is used to refine classification programs, then classification accuracy can be improved, but the system complexity and operational overhead increase
Solution Approach 1:
The system automatically performs classification program refinement without requiring external expert supervision. The exoskeleton's processor continuously updates the classification program using sensor data from multiple sources, enabling self-learning that improves accuracy while eliminating the need for complex expert intervention protocols.
Solution Approach 2:
The patent replaces manual expert supervision with automated computational algorithms. Instead of relying on mechanical processes of expert analysis and manual program adjustment, the system uses data-driven machine learning algorithms that automatically refine classification programs, reducing system complexity while maintaining or improving accuracy.
3Adaptability or versatility
If generic classification programs are used, then the system can operate without customization, but the adaptability to individual user behavior is reduced
Solution Approach 1:
The classification program is designed to be dynamic and adaptive rather than static. The system continuously evolves the classification program based on individual user sensor data patterns, allowing it to adapt to each user's unique behavior while maintaining ease of operation through automated learning processes that require no manual reconfiguration.
Solution Approach 2:
The system automatically customizes its classification program for each individual user through self-learning from sensor data. The exoskeleton performs unsupervised refinement tailored to each user's specific movement patterns and intent signals, achieving personalized adaptability without requiring manual setup or expert configuration for each user.
Data Source
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AI summary
The disclosure includes a method of operating an exoskeleton system. The method includes determining a first state estimate for a current classification program being implemented by the exoskeleton system, determining a second state estimate for a reference classification program; determining that a difference between the first and second state estimate is greater than a classification program replacement threshold; generating an updated classification program; and replacing the current classification program with the updated classification program based at least in part on the determining that the difference between the first and second state estimates is greater than the classification program replacement threshold.