Bimanual Motion Recognition for Real-Time Teleoperation Feedback
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Solution Overview
Problem
Existing robotic systems lack effective methods for integrating human bimanual coordination recognition, particularly in complex tasks like robot-assisted surgery, hindering the provision of meaningful feedback and training.
Innovation Solution
A geometric framework is developed to classify bimanual movements using geometric descriptors of hand motion, incorporating direction and symmetry metrics, enabling online recognition and feedback to enhance training and performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If robotic systems use complex bimanual coordination recognition methods, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent segments bimanual coordination recognition into distinct modes (unimanual, symmetric bimanual, asymmetric bimanual) and uses separate geometric descriptors for each mode. This segmentation allows the system to accurately recognize complex coordination patterns while maintaining manageable system complexity by handling each mode with specialized, simplified algorithms.
Solution Approach 2:
The patent changes parameters by using different geometric descriptors (e.g., symmetry metrics, trajectory curvature, velocity ratios) depending on the coordination mode detected. This parameter adaptation enables high measurement precision for each specific mode while avoiding the need for a single overly complex recognition system.
2Productivity
If robotic systems provide real-time feedback for bimanual tasks, then training efficiency improves, but computing time increases
Solution Approach 1:
The patent performs preliminary classification of coordination modes using simple geometric checks before applying more computationally intensive analysis. By pre-categorizing movements into basic modes, the system reduces computing time for real-time feedback while maintaining training efficiency through timely, mode-appropriate feedback delivery.
Solution Approach 2:
The patent applies partial analysis by focusing computational resources on the most relevant geometric features for each coordination mode rather than analyzing all possible parameters. This selective approach provides sufficient feedback for training efficiency while minimizing unnecessary computing time consumption.
3Measurement precision
If robotic systems use detailed geometric descriptors for movement classification, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent applies local quality by using different levels of geometric descriptor detail appropriate for each coordination mode. For example, symmetric bimanual movements use symmetry-based descriptors, while asymmetric movements use trajectory-based descriptors. This localized approach achieves high measurement precision without requiring all algorithms to be uniformly complex.
Data Source
AI summary
Systems, methods and computer program products for online recognition of bimanual or bipedal coordination modes directed to completing a task using a robot. Such a method includes the steps of determining an onset of bimanual or bipedal motion of a subject operating the robot, and, in response, receiving trajectory data of left and right hands, or left and right feet, of the subject. The method also includes the step(s) of computing, based on the trajectory data, geometric descriptors of the segment, which include a direction metric and a symmetry metric. The method further includes the step(s) classifying, based on the direction and symmetry metrics, the types of direction and symmetry for the segment. The method also includes at least one of the steps of: generating task performance feedback, and controlling the robot, based on the types of direction and symmetry.