Cabled Differential Robot for Rehabilitation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic rehabilitation systems, whether low-DOF or high-DOF exoskeletal, face limitations such as restricted range of motion, mechanical complexity, high cost, and discomfort for patients, as well as challenges in monitoring compensatory movements and assessing patient progress during therapy sessions, especially in group settings.
Innovation Solution
A multi-active-axis, non-exoskeletal robotic device with three active degrees of freedom, utilizing a cabled differential to shift motor mass away from the patient's workspace, and an AI-based computer system for monitoring and assessing patient movements, enabling bi-manual rehabilitation and real-time feedback to therapists.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If high-DOF exoskeletal systems are used to provide greater range of motion, then the range of motion is improved, but the device complexity and cost increase significantly
Solution Approach 1:
The system divides the robotic device into multiple independent end-effectors, each with specific DOFs, rather than using a single complex exoskeletal structure. This segmentation allows each component to be simpler while the system as a whole achieves comprehensive motion capability through coordination of multiple devices.
Solution Approach 2:
The robotic system is designed to perform multiple rehabilitation functions using a standardized platform with configurable end-effectors. The same base system can be adapted for different therapeutic needs by changing the end-effector configuration, reducing overall system complexity while maintaining versatility.
2Force
If exoskeletal systems wrap around the patient's limb to provide support, then the support ability is improved, but the patient comfort and ease of operation deteriorate
Solution Approach 1:
The system extracts the support function from the traditional exoskeletal wrapping structure and implements it through a different mechanism. The robotic arm provides support through controlled positioning and force application at the endpoint rather than through continuous wrapping contact, reducing discomfort while maintaining support capability.
3Device complexity
If low-DOF systems are used to reduce device complexity and cost, then the device complexity is reduced, but the range of motion and rehabilitation task variety are limited
Solution Approach 1:
The system employs dynamic configuration where the functional degrees of freedom can be changed during operation. The robotic arm can adapt its motion capabilities dynamically to perform various rehabilitation tasks, allowing a physically simpler system to achieve greater functional versatility through software control and dynamic reconfiguration.
4Productivity
If therapists manually monitor multiple patients simultaneously, then the therapy coverage is improved, but the measurement precision of compensatory movements deteriorates
Solution Approach 1:
The system implements automated feedback mechanisms using sensors and computer vision to continuously monitor patient movements. This automated feedback system maintains high measurement precision for detecting compensatory movements while allowing therapists to manage multiple patients simultaneously, as the system independently tracks and analyzes each patient's motion data.
Solution Approach 2:
The system replaces manual visual monitoring with automated optical and sensor-based detection systems. This substitution of mechanical/human monitoring with automated detection technology enables precise measurement of patient movements without requiring therapist attention, thereby maintaining detection accuracy while increasing therapy coverage.
Data Source
AI summary
A system for facilitating delivery of physical therapy to a patient, the system comprising: a robot-assisted therapy device configured for engagement with a limb of the patient; a camera configured to obtain image data of the patient performing the physical therapy; and an AI-based movement detection system, wherein the AI-based movement detection system is configured to receive image data of the patient from the camera, analyze the image data of the patient and determine at least one from the group consisting of (i) identity of the patient, (ii) movement of the patient, and (iii) posture of the patient.