Walking Assist Torque Control Using Smoothed Gait State Feedback
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Solution Overview
Problem
Existing walking assist devices lack individualized control mechanisms to adapt to the unique gait characteristics and physical conditions of each user, leading to inconsistent and potentially hazardous assistance.
Innovation Solution
A method and device that determine a first state variable for a user's gait phase, smooth and time-delay the variable, apply a torque control variable, and adjust assist torque based on a third state variable, incorporating reinforcement learning to optimize control policies for individual users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a walking assist device provides power assistance to users with reduced muscular strength, then walking ability is improved, but safety risks increase due to direct interaction with the user's body
Solution Approach 1:
The control device continuously monitors the user's gait state through sensors and adjusts the assist torque in real-time based on feedback from the user's actual walking condition. This closed-loop control ensures the device responds appropriately to user needs while maintaining safety through continuous monitoring and adjustment.
Solution Approach 2:
The system dynamically adapts the assist torque based on the user's current gait phase and individual characteristics. The control device modifies assistance levels in real-time according to the user's movement state, ensuring both effectiveness and safety through dynamic adjustment rather than fixed assistance patterns.
2Device complexity
If the walking assist device uses a simple control mechanism, then device complexity is reduced, but the assistance becomes inconsistent and potentially hazardous
Solution Approach 1:
The control device employs feedback mechanisms that continuously monitor gait state and adjust assist torque accordingly. This feedback-driven approach ensures consistent and reliable assistance without requiring overly complex mechanical structures, as the intelligence is embedded in the control algorithm rather than mechanical complexity.
Solution Approach 2:
The system changes control parameters (assist torque levels, timing, and magnitude) based on the user's gait phase and individual characteristics. By dynamically adjusting these parameters through software control rather than mechanical complexity, the system achieves consistent assistance with relatively simple device architecture.
3Ease of operation
If the walking assist device provides high assist torque, then walking support is improved, but hindrance to the user increases
Solution Approach 1:
The control device monitors the user's response to assist torque and adjusts the assistance level based on feedback. When high assist torque provides beneficial support, it is applied; when it creates hindrance, the system reduces or modifies the torque. This feedback mechanism balances support and comfort dynamically.
Solution Approach 2:
The system applies assist torque selectively during specific gait phases rather than continuously, and adjusts the magnitude to be just sufficient for assistance without excessive force. This partial action approach provides necessary support while avoiding harmful over-assistance that could hinder natural walking motion.
Data Source
AI summary
A method and device for controlling a walking assist device is disclosed. The method includes determining a first state variable for a gait state of a user wearing the walking assist device based on a gait of the user, obtaining a second state variable which is smoothed and time-delayed from the first state variable, obtaining a third state variable by applying a torque control variable to the second state variable, and determining an assist torque to be provided by the walking assist device based on the obtained third state variable.