Cable-Driven Robotic Treadmill for Adaptive Gait Resistance
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Solution Overview
Problem
Current robotic-assisted body weight supported treadmill training (BWSTT) techniques for individuals with spinal cord injury (SCI) and stroke are limited in effectiveness, as they often rely on fixed kinematic trajectories, which restrict natural variability in leg movement and do not adequately engage adaptive sensorimotor processes, leading to suboptimal motor learning and rehabilitation outcomes.
Innovation Solution
A novel cable-driven robotic system that applies targeted resistance or assistance to the legs during treadmill walking, allowing for intrinsic variability in stepping patterns and adjusting the load based on the patient's motor performance, thereby enhancing active involvement and adaptive sensorimotor engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If fixed kinematic trajectories are used in robotic-assisted BWSTT, then control precision is improved, but natural variability in leg movement is restricted and adaptive sensorimotor processes are not adequately engaged
Solution Approach 1:
The system transitions from fixed, static trajectories to dynamic, adaptive trajectories that respond to patient-specific sensorimotor processes. The control system continuously adjusts trajectory parameters based on real-time feedback from sensors monitoring leg position, force, and movement patterns, allowing natural variability while maintaining therapeutic effectiveness
Solution Approach 2:
The system implements closed-loop feedback control where sensors detect patient leg movement parameters and the controller adjusts the robotic assistance in real-time. This feedback mechanism enables the system to adapt to individual patient needs, engage sensorimotor processes, and optimize trajectories dynamically during therapy sessions
2Reliability
If high levels of assistance are provided during treadmill training, then patient safety is improved, but patient effort and active involvement are reduced
Solution Approach 1:
The system applies partial assistance rather than complete support, providing just enough robotic force to ensure safety while requiring the patient to actively generate the majority of movement force. This partial action approach maximizes patient effort and active involvement while maintaining safety through continuous monitoring and adaptive support
Solution Approach 2:
The system enables patients to perform therapy independently with minimal therapist intervention. The robotic system autonomously provides adaptive assistance and resistance based on real-time sensor feedback, allowing patients to self-regulate their effort levels and actively engage in their own rehabilitation process
3Extent of automation
If traditional robotic-assisted training is used, then therapist involvement is reduced, but motor learning and rehabilitation outcomes are suboptimal
Solution Approach 1:
The system replaces traditional mechanical robotic actuators with a magnetic field-based actuation system. Magnetic particles embedded in the patient's footwear or orthosis experience controlled magnetic forces that provide adaptive assistance and resistance, eliminating complex mechanical linkages and enabling more nuanced, biologically-compatible interaction while improving motor learning outcomes
Data Source
AI summary
A system and method are provided for targeted training of a person walking on a powered backward moving surface. Kinematic information of motor performance, such as ankle position and velocity, is measured throughout one or more phases of a gait cycle with a detector. The gait phase is determined, and a resistive/assistive force is applied to the leg that differs depending upon the gait phase and the measured kinematic information.