Gait Learning Exoskeleton with Dynamic Force Feedback
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Solution Overview
Problem
Current rehabilitation methods for stroke patients, such as Neuro Developmental Treatment (NDT), face challenges due to the limited availability of physical therapists and the inadequacies of mechanical exoskeletons, which fail to induce independent learning, provide correct sensory input, and adjust parameters in real-time to guide gait activity effectively.
Innovation Solution
A gait activity learning assistance system comprising a force-transmission unit, dynamic measurement module, and control module that detects limb movement, adjusts driving force timing, strength, and duration to guide users in achieving correct gait reactions, mimicking the role of physical therapists and reducing the physical burden on rehabilitation staff.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If mechanical exoskeleton is used to provide supporting force for patient's limb, then the physical burden on physical therapist is reduced, but the patient can only passively control the device and cannot achieve independent learning of gait activity
Solution Approach 1:
The system enables patients to independently control the exoskeleton through their own muscle signals (EMG) and sensory feedback, allowing them to autonomously regulate the assisting force and learn gait patterns without constant therapist intervention. The closed-loop control system processes patient's neural signals and adjusts mechanical assistance in real-time, fostering self-directed rehabilitation.
Solution Approach 2:
The system incorporates multiple sensory feedback mechanisms including force feedback through the exoskeleton, visual feedback from motion capture systems, and proprioceptive feedback from muscle sensors. This multi-modal feedback loop enables patients to perceive their movement quality and adjust their gait patterns actively, promoting independent learning while reducing therapist burden.
2Device complexity
If mechanical exoskeleton applies force at fixed positions, then the device structure is simplified, but the patient can only perform motion imitation and cannot achieve the guiding effect of NDT
Solution Approach 1:
The exoskeleton transitions from fixed force application points to dynamically adjustable contact positions. Sensors detect patient's limb position and movement phase, automatically adjusting where and how force is applied to match the therapist's NDT approach. This dynamic adaptability enables proper gait guidance without requiring complex reconfigurable mechanical structures.
Solution Approach 2:
The system varies multiple parameters including force magnitude, application timing, contact position, and direction of assistance based on real-time detection of patient's gait phase and performance. These parameter adjustments are controlled through software algorithms that replicate therapist decision-making, achieving effective NDT guidance without mechanical complexity.
3Reliability
If mechanical exoskeleton provides continuous supporting force, then patient's mobility is improved, but correct sensory input cannot be provided and NDT training method cannot be simulated
Solution Approach 1:
The exoskeleton provides intermittent rather than continuous assistance, synchronizing force application with specific phases of the gait cycle detected through sensors. Assistance is delivered periodically at critical moments (e.g., swing phase initiation, stance phase transition) to provide both mobility support and meaningful sensory feedback that guides motor learning, replicating the pulsed nature of therapeutic manual assistance.
4Reliability
If physical therapist personally assists patient by hand to provide auxiliary force, then correct NDT rehabilitation is achieved, but the physical burden on therapist increases and the number of treatable patients decreases
Solution Approach 1:
The system replaces the therapist's manual mechanical assistance with an automated exoskeleton that uses sensors, controllers, and actuators to provide equivalent therapeutic force. The device replicates the therapist's assessment and intervention capabilities through electronic control systems, maintaining rehabilitation quality while eliminating the physical burden that limits therapist productivity.
Solution Approach 2:
The exoskeleton acts as an intermediary between the therapist's treatment protocol and the patient's body. It translates the therapist's NDT approach into automated mechanical assistance, preserving the therapeutic intent while removing the therapist from direct physical contact. This allows one therapist to supervise multiple patients simultaneously, increasing overall treatment capacity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system enables users to learn gait activities autonomously by providing real-time sensory input and adjusting driving forces, effectively simulating the rehabilitation strategies of physical therapists, thus reducing the need for extensive human resources in physical medicine and rehabilitation.
Implementation Method 1
at least one movement detecting module for detecting the change in movement of a user's limb to generate at least one movement characteristic data
Implementation Method 2
a motor, which receives the motor driving signal and generates a driving force correspondingly
Implementation Method 3
at least one force-transmission unit, which is connected to the motor driver and receives the motor driving signal to drive at least one site of the user correspondingly so that the user generates a gait reaction
Implementation Method 4
at least one dynamic measurement module, which is connected to the at least one force-transmission unit and measures a dynamic change in the at least one force-transmission unit to generate at least one dynamic characteristic data
Data Source
AI summary
A gait activity learning assistance system, and an application method thereof, includes a main body, at least one movement detecting module, a control module, at least one driving module and at least one dynamic measurement module. The system is able to guide and induce a user to learn gait autonomously by disposing at least one force-transmission unit on at least one limb position of the user, besides, the system is able to measure a dynamic change of the at least one force-transmission unit by the at least one dynamic measurement module while user receiving a gait assistance, and send them back to the control module immediately for a real-time analysis.