Upper Limb Rehab Robot Feedback for Compensatory Motion Suppression
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Solution Overview
Problem
Current rehabilitation robotic training systems fail to effectively monitor and suppress compensatory movements in hemiplegic upper limbs, which hinder the rehabilitation process and motor function recovery.
Innovation Solution
A rehabilitation robot training system that includes position trackers, a force feedback glove, a pressure cushion, and an interactive display screen to monitor and analyze compensatory movements, providing real-time feedback and adjusting movement velocity, range, and auxiliary force to assist patients in correcting these movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rehabilitation robot training is performed without monitoring compensatory movements, then the training can proceed without complex monitoring systems, but the patient forms erroneous movement modes that adversely affect rehabilitation effectiveness
Solution Approach 1:
The monitoring system is segmented into multiple independent sensors: position trackers on upper arm and forearm, force feedback glove on hand, and pressure cushion on seat. Each sensor monitors specific aspects of compensatory movements independently, allowing comprehensive monitoring without requiring a single complex monitoring system
Solution Approach 2:
The system implements real-time feedback by detecting compensatory movements through sensors and providing tactile feedback via the force feedback glove and visual feedback through the display screen. This feedback mechanism guides patients to correct erroneous movement modes, improving rehabilitation effectiveness
2Measurement precision
If position trackers and force feedback glove are used to monitor arm and hand movements, then compensatory movements can be detected in real time, but the device complexity and cost increase
Solution Approach 1:
The measurement function is segmented across multiple simple sensors rather than using a single complex sensor system. Position trackers monitor upper arm and forearm positions separately, the force feedback glove monitors hand movements, and the pressure cushion monitors trunk movements, achieving comprehensive high-precision detection through simple individual components
Solution Approach 2:
The force feedback glove serves multiple functions: it acts as a position tracker for hand movements, provides tactile feedback to guide correct movements, and can detect grip force. This multi-functionality reduces the need for separate devices, lowering overall system complexity while maintaining high measurement precision
3Measurement precision
If pressure cushion is added to monitor trunk compensation, then comprehensive compensatory movement monitoring is achieved, but the system complexity and data processing burden increase
Solution Approach 1:
The trunk monitoring function is segmented from the upper limb monitoring system. The pressure cushion independently monitors trunk compensatory movements while position trackers and force feedback glove monitor upper limb movements. This segmentation allows specialized processing of trunk data without complicating the overall system architecture
Solution Approach 2:
The host computer control center acts as an intermediary that receives data from all sensors, processes the information to identify compensatory movements, and coordinates the feedback responses. This centralized intermediary simplifies data processing by providing a single processing point rather than requiring distributed processing across multiple devices
4Reliability
If the rehabilitation robot adjusts movement velocity and auxiliary force based on compensation monitoring, then compensatory movements are suppressed, but the control system complexity increases
Solution Approach 1:
The control system uses real-time feedback from position trackers and force feedback glove to detect compensatory movements. When compensation is detected, the system automatically adjusts movement velocity and auxiliary force parameters to guide the patient toward correct movement patterns, achieving reliable compensatory movement suppression through feedback-based adaptive control
Solution Approach 2:
The rehabilitation robot implements dynamic adjustment of control parameters including movement velocity and auxiliary force based on real-time detection of compensatory movements. These parameters are not fixed but adapt dynamically during training, allowing the system to respond to changing patient needs while maintaining relatively simple control logic through parameter modulation
Data Source
AI summary
Disclosed is a rehabilitation robot training system for monitoring and suppressing the compensatory movement of a hemiplegic upper limb. The system includes an upper computer control center, an interaction display screen, a force feedback glove, a position tracker, upper limb rehabilitation robot tail end connectors, an upper limb rehabilitation robot, a base and a pressure cushion. One upper limb rehabilitation robot tail end connector is mounted on a tail end of each of two robotic arms of the upper limb rehabilitation robot, and the upper limb rehabilitation robot tail end connectors are respectively worn on an upper arm and a forearm of a patient to drive an arm to move; the upper computer control center stores and processes data, collected by the position tracker, the force feedback glove and the pressure cushion in real time, of the patient, and monitors and analyses whether the patient does a compensatory gesture.