ECG-Based Limb Position Prediction via CNN
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Solution Overview
Problem
Current methods for detecting the position of body appendages, such as limbs, rely on sensors like accelerometers and gyroscopes, which may not be suitable for rehabilitation and recovery from stroke or athletes who require simultaneous heart monitoring.
Innovation Solution
A method utilizing electrocardiogram (ECG) data to recognize the position of body appendages through a convolutional neural network (CNN) model. The ECG sensor collects data in real-time, which is processed into an image format and input into the CNN model to predict the position of body appendages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensors (accelerometers, gyroscopes) are used to detect limb position, then measurement precision is improved, but the ability to simultaneously monitor heart activity is lost
Solution Approach 1:
The ECG sensor is designed to perform multiple functions: it simultaneously detects heart electrical activity and captures limb position information through impedance changes. This multi-functional approach allows the same device to monitor both cardiac health and肢体 positioning without requiring separate sensor systems.
Solution Approach 2:
The patent combines heart monitoring and limb position detection into a single integrated system using ECG technology. The ECG sensor merges cardiac signal detection with bioimpedance-based position sensing, eliminating the need for separate accelerometer and gyroscope systems.
2Adaptability or versatility
If ECG sensor is used to detect body appendage position, then simultaneous heart monitoring capability is improved, but measurement precision may be compromised
Solution Approach 1:
The patent replaces traditional mechanical sensors (accelerometers, gyroscopes) with an electrical field-based ECG system. The ECG sensor detects limb position through changes in electrical impedance and signal characteristics, substituting mechanical measurement with electrical field sensing to achieve both heart monitoring and position detection.
Solution Approach 2:
The system utilizes changes in ECG signal parameters (amplitude, frequency, morphology) that occur with limb movement to infer position. By monitoring parameter variations in the ECG waveform rather than relying on dedicated position sensors, the system achieves position detection through electrical signal analysis.
3Measurement precision
If multiple sensors are used for accurate limb position detection, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts position detection capability from the traditional multi-sensor system and integrates it into the ECG sensor itself. By taking out the position sensing function and embedding it within the cardiac monitoring device, the system eliminates the need for separate accelerometer and gyroscope modules, reducing overall device complexity.
Data Source
AI summary
A system and method are presented for recognizing position of a person's body appendage such as fingers using electrocardiogram data. An electrocardiogram (ECG) sensor placed on a user is in real time communication with a computing device containing a convolutional neural network (CNN) trained to receive real time ECG data and predict a position of the person's body appendage. The real time ECG sensor data is processed from digital format into an analog signal to form ECG image data. The ECG data may be converted from comma separated values (.csv) to joint photographic experts group (.jpeg) format. The output of the CNN model predicts the user appendage position from among a plurality of classified body appendage positions, such as number of fingers extended. The CNN model may include an input layer, convolutional 2D layer, batch normalization layer, rectified linear units layer, fully connected layer, softmax layer, and classification layer.