Bio-signal Feedback Learning Cycle for Neuroplasticity Recovery
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Solution Overview
Problem
Conventional rehabilitation models for patients with neurological, emotional, or physical disorders fail to effectively leverage neuroplasticity by only providing feedback on physical motion at a gross level, neglecting the integration of mind and body states, which limits the potential for accelerated recovery.
Innovation Solution
A computerized method that uses sensor devices to continuously sense internal signals, calculating real-time mind and body states to administer targeted functional development activity sequences, optimizing recovery by integrating feedback and feedforward mechanisms through a movement-based learning model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional rehabilitation models provide feedback only on physical motion at a gross level, then the feedback system remains simple and easy to implement, but the effectiveness of leveraging neuroplasticity is limited and recovery acceleration is hindered
Solution Approach 1:
The patent implements a comprehensive feedback system that integrates multiple bio-signal types (EEG for brain waves, EMG for muscle signals, ECG for heart rate, EDA for skin conductance) to provide real-time feedback on both physical motion and internal mind-body states. This multi-layered feedback approach enables the system to effectively leverage neuroplasticity by monitoring and responding to the patient's physiological and neurological states during rehabilitation exercises, thereby resolving the contradiction between feedback simplicity and neuroplasticity effectiveness.
Solution Approach 2:
The system introduces a computerized processing system as an intermediary that receives raw bio-signal data from multiple sensors, processes this information through algorithms, and translates it into meaningful feedback and exercise modifications. This intermediary processing layer enables the integration of complex multi-source data without requiring direct complexity in the sensor-patient interface, thus maintaining ease of use while achieving effective neuroplasticity leverage through sophisticated signal analysis and interpretation.
2Productivity
If conventional rehabilitation models use repetitive task performance without integrating mind and body states, then the training protocol remains simple to administer, but the potential for accelerated recovery through neuroplasticity is not fully realized
Solution Approach 1:
The patent implements dynamic adjustment of rehabilitation exercises based on real-time analysis of multiple bio-signals. The system continuously monitors brain waves, muscle signals, heart rate, and skin conductance to dynamically modify exercise intensity, type, and progression. This dynamic approach accelerates recovery by optimizing training protocols according to the patient's instantaneous mind-body state, while the automated computerized system manages the complexity of coordinating these multiple adaptive parameters.
Solution Approach 2:
The system changes multiple parameters simultaneously based on integrated bio-signal analysis, including exercise intensity, duration, type, and feedback delivery methods. By monitoring physiological parameters (heart rate, muscle activation) and neurological parameters (brain wave patterns), the system automatically adjusts training parameters to optimize neuroplasticity induction, thereby accelerating recovery without requiring manual complexity in protocol administration.
3Measurement precision
If conventional rehabilitation models provide gross-level motion feedback, then the feedback is easy to perceive and understand, but the precision of monitoring mind and body states is insufficient for optimized recovery
Solution Approach 1:
The patent replaces simple mechanical motion sensors with sophisticated physiological and neurological sensing systems. Instead of only measuring external motion, the system uses EEG electrodes to detect brain wave patterns, EMG sensors to measure muscle electrical activity, ECG to monitor heart rate, and EDA sensors to detect skin conductance changes. This substitution of measurement mechanisms enables precise monitoring of internal mind-body states that are invisible to conventional mechanical sensing, thereby achieving high measurement precision for neurological and physiological parameters.
Data Source
AI summary
Disclosed is a learning model employed to treat patients with functional impairments due to psychological, mental cognitive or physical disorders or disabilities. The treatment includes accessing at which transition stage of the learning model that a subject has movement transition deficits based on the subject's bio-signals, such as brain state and muscle state signals. The movement transition deficits are addressed so that the subject can attain an upward spiral, accelerating functional recovery or development and avoid a downward spiral towards compensation and slowing down of recovery.