Real-Time Movement Prediction System Using EIT and EMG Sensors
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Solution Overview
Problem
Current systems for monitoring and improving complex user movement sequences face limitations in real-time analysis and feedback, requiring separate evaluations and delayed visualization, which hampers the provision of timely and reliable training instructions.
Innovation Solution
A system comprising sensors for data collection using EIT, EMG, and UWB, integrated with machine learning for real-time data processing and actuation, enabling latency-free prediction and feedback through a compact sensor system and actuator setup, utilizing compressed sensing and Convolutional Neural Networks for efficient data processing and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If separate evaluation and visualization of movement data is performed, then measurement data can be processed, but time delays occur and training instructions are not provided in real-time
Solution Approach 1:
The patent combines the evaluation and visualization functions into a single integrated system. The processing unit simultaneously performs movement evaluation and generates visual feedback, eliminating the sequential delay between separate evaluation and visualization steps. This merging enables real-time training instructions to be provided without time loss.
2Measurement precision
If complex movement sequences are monitored with high body control requirements, then accurate movement data is obtained, but intensive training is required and system complexity increases
Solution Approach 1:
The sensor system is designed with multi-functionality to monitor multiple body parameters simultaneously (position, velocity, acceleration, muscle activity). This universal approach allows accurate measurement of complex movement sequences without requiring separate specialized sensors for each parameter, thereby reducing overall system complexity while maintaining high measurement precision.
Solution Approach 2:
The processing unit acts as an intermediary that receives raw data from multiple sensors, performs integrated evaluation, and generates coherent training instructions. This intermediary processing simplifies the connection between complex sensor inputs and actionable feedback, reducing the perceived system complexity for users while maintaining accurate movement monitoring.
3Speed
If real-time processing of measurement data is implemented, then latency-free feedback is achieved, but processing power requirements and system complexity increase
Solution Approach 1:
The processing unit segments the data processing task into distinct functional modules: data acquisition from sensors, movement evaluation algorithms, and visualization generation. This segmentation allows each module to be optimized independently for real-time performance while keeping the overall system architecture manageable and not excessively complex.
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
Enables reliable and timely provision of training instructions by processing user movement data in real-time, reducing latency and improving training effectiveness through accurate prediction and visualization of movement sequences.
Implementation Method 1
The acquisition of the user's body state is carried out by means of EIT (electrical impedance tomography)
Implementation Method 2
The acquisition of the user's body state is carried out by means of EIT (electrical impedance tomography) and/or EMG (electromyography)
Implementation Method 3
The acquisition of the user's body state is carried out by means of EIT (electrical impedance tomography) and/or EMG (electromyography) and/or UWB (ultra-wideband) real-time localization
Data Source
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AI summary
A system (1) for detecting and predicting movements of a user (2) comprises a detection area (3) with at least one sensor (31), a modeling area (5) and an actuator (10), wherein the sensor (31) is suitable for collecting measurement data (6; 61) about the body state of the user (2) and forwarding it to the modeling area (5).