Neuromuscular-to-motion decoder for paretic limb rehabilitation
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Solution Overview
Problem
Current rehabilitation methods for paretic limbs, particularly those based on EMG signals, often fail to effectively induce meaningful motor recovery in stroke survivors, as they rely on pre-programmed assistive movements or proportional force applications, lacking personalized feedback and adaptive correction mechanisms.
Innovation Solution
A system that generates a neuromuscular-to-motion decoder using sensors and machine learning algorithms to map neuromuscular signals from healthy limbs to corresponding motions, allowing for personalized rehabilitation of paretic limbs by providing hybrid motion guidance combining neuromuscular activity with corrective feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-programmed assistive movements are used based on EMG signals, then the system can provide standardized rehabilitation assistance, but the system lacks personalized feedback and adaptive correction mechanisms
Solution Approach 1:
The patent implements a feedback mechanism where the actual motion of the paretic limb is measured and compared with the desired motion trajectory. The difference (error) is used to generate corrective assistance forces through the robotic device, enabling adaptive correction that personalizes the rehabilitation process to each patient's specific needs and progress.
Solution Approach 2:
The system leverages the patient's own residual neuromuscular activity (EMG signals from the paretic limb) as the control input. The patient's voluntary muscle efforts are amplified and guided by the robotic device, making the patient an active participant in their own rehabilitation rather than a passive recipient of pre-programmed movements.
2Productivity
If EMG signals from the paretic limb are used to trigger pre-programmed assistive movements, then the system can provide automated assistance, but the system cannot effectively induce meaningful motor recovery
Solution Approach 1:
The system continuously monitors the actual motion of the paretic limb and compares it with the desired trajectory. This feedback loop enables real-time correction of movement errors, ensuring that the patient learns accurate movement patterns rather than relying on pre-programmed movements that may not match the patient's actual capabilities or progress.
Solution Approach 2:
The assistance provided by the system is dynamically adjusted based on the patient's instantaneous performance. The level and direction of assistance change continuously according to the error between actual and desired motion, allowing the system to adapt to the patient's evolving motor control capabilities throughout the rehabilitation process.
3Adaptability or versatility
If proportional controllers are used where assistive force is proportional to EMG signal amplitude, then the system can provide continuous assistance, but the system lacks intuitive feedback and adaptive correction
Solution Approach 1:
The system implements a feedback mechanism where the actual motion of the paretic limb is measured and compared with the desired motion trajectory. The difference (error) is used to generate corrective assistance forces, providing intuitive feedback that guides the patient toward correct movement patterns while adapting to their performance in real-time.
Solution Approach 2:
The patent replaces complex brain-computer interface systems with a simpler EMG-based control system. By using surface electromyography sensors to detect muscle activation, the system achieves effective control and feedback without the need for invasive brain signal processing, reducing overall system complexity while maintaining adaptability.
4Adaptability or versatility
If brain neurosignal processing systems are used for rehabilitation, then the system can provide advanced control, but the system becomes more complex and costly
Solution Approach 1:
The patent replaces complex brain-computer interface systems with a simpler EMG-based control system. By using surface electromyography sensors to detect muscle activation, the system achieves effective control and feedback without the need for invasive brain signal processing, reducing overall system complexity and cost while maintaining adaptability.
Solution Approach 2:
The system uses EMG signals from the healthy limb to generate a neuromuscular-to-motion decoder that is then applied to the paretic limb. This copying approach allows the patient's own healthy neuromuscular patterns to serve as a template for rehabilitation, providing personalized control without requiring complex external brain signal processing systems.
Data Source
AI summary
Generator systems and methods are provided for generating a neuromuscular-to-motion decoder from a healthy limb. The generator system is configured to receive neuromuscular signals from neuromuscular sensors associated to predefined muscle/nerve locations of at least one pair of agonist and antagonist muscles/nerves of the healthy limb, obtained during performance by the person of a predefined exercise (defined by predefined exercise data) with the healthy limb; to receive motion signals from motion sensors associated to predefined positions of the healthy limb, during performance by the person of the predefined exercise with the healthy limb; and to generate the neuromuscular-to-motion decoder by mapping the neuromuscular signals to the motion signals over time using a mapping method. Rehabilitation systems are also provided for rehabilitating a paretic limb by using a neuromuscular-to-motion decoder produced by a generator system.