Rehabilitation Sensor Model Predicts Walking Ability
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Solution Overview
Problem
Current rehabilitation systems face challenges in accurately assessing the movement ability of patients, particularly due to the requirement for extensive space and the need for constant assistance from trained staff, leading to infrequent and potentially biased scoring of the walking FIM index.
Innovation Solution
A rehabilitation support system comprising an actuator for assisting movement, a sensor for data detection, and a learned model that predicts the walking ability of a patient based on input data, allowing for frequent and unbiased assessment of movement ability without the need for extensive space or constant staff assistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the walking FIM is measured by assessing the amount of assistance when the trainee walks 50 m on flat ground, then the movement ability of the trainee can be assessed, but it is impossible to frequently measure the walking FIM due to space limitations and the need for constant staff assistance
Solution Approach 1:
The patent replaces the manual assessment method (mechanical observation and scoring by staff) with an automated sensor-based system. Sensors detect movement data objectively, and a learned model automatically calculates the walking FIM score, eliminating the need for staff to manually assess and score patient movement ability during walking trials.
Solution Approach 2:
The system enables self-assessment of movement ability by using the trainee's own movement data captured by sensors. The learned model processes the sensor data to generate the walking FIM score without requiring external assessment by staff members, allowing frequent automated measurements during rehabilitation sessions.
2Measurement precision
If a trainee must move 50 m or more flat ground to measure the walking FIM, then the walking FIM can be assessed, but it may be difficult for the trainee to move to a place for measuring the FIM in a hospital where space is limited
Solution Approach 1:
The patent changes the measurement parameters by using sensor-collected movement data (such as step count, distance, speed, or other kinematic parameters) to calculate the walking FIM score. This allows the assessment to be performed over shorter distances or in confined spaces while maintaining measurement validity through the learned model that correlates sensor parameters with the standard walking FIM scale.
3Measurement precision
If the amount of assistance by an assistant during walking on flat ground is assessed, then the walking FIM can be measured, but a deviation may occur in scoring of the walking FIM depending on who the assistant is
Solution Approach 1:
The patent replaces human assistants with an automated sensor-based assessment system. Sensors objectively detect movement parameters, and a learned model automatically determines the walking FIM score based on these parameters. This eliminates inter-rater variability and ensures consistent scoring regardless of which staff member would have performed the assessment.
Data Source
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AI summary
A learning device includes a data acquisition unit (520) configured to acquire rehabilitation data from a rehabilitation support device having an actuator configured to assist a rehabilitation movement of a trainee and a sensor configured to detect data on the rehabilitation movement assisted by the actuator, a data generation unit (510a) configured to generate, as data for learning, the rehabilitation data that includes detected data corresponding to a detection result by the sensor, and a learning unit (510b) configured to generate, by performing machine learning using the data for learning, a learning model that outputs an index indicating a movement ability of the trainee, upon receiving an input of the detected data.