Wearable Electrocardiography and Telemetry System Dynamically Calibrated with Accelerometer Data

Abstract

This invention is an electrocardiography (ECG) and telemetry system in the field of wearable health technologies that autonomously filters electrical muscle and cable noise (artifacts) generated while a person is in motion. The system essentially consists of a main microcontroller, a biopotential sensor, and 3-axis accelerometer modules. Simultaneous motion data from the accelerometer dynamically changes the coefficients of the software-based digital filters within the microcontroller. When the person walks or runs, the filters become more aggressive, suppressing the noise, and return to sensitive measurement mode when the person stops. Possible rhythm disturbances (arrhythmia / tachycardia) are detected hardware-wise from the cleaned signal and instantly transmitted to a remote server via wireless network.

Description

1 TARIFF WEARABLE, DYNAMICALLY CALIBRATED USING ACCELERATOR DATA. ELECTROCARDIOGRAPHY AND TELEMETRICS SYSTEM Technical Field to Which the Invention Relates This invention has implications for wearable health technologies, the Internet of Things (IoT), and biosignal processing. and relates to the field of mechatronic systems. The invention, in particular, enables the user to experience instantaneous acceleration and By detecting the movement status, it analyzes the raw data from the electrocardiography (ECG) sensor. autonomously and dynamically apply digital signal processing coefficients to the data It relates to a changing hardware architecture. Prior State of the Technique 10 Portable ECG devices and biopotential measurement modules used today, It is designed for scenarios where the user remains stationary. When the user walks, electrode cables during running or performing daily activities "Baseline wander" is a motion artifact resulting from swaying and muscle tension. and muscle noise (EMG interference) occurs. Devices in known technology detect these 15 It attempts to clean up hardware noise with fixed software filters, which either the signal completely disappears while moving or the signal changes while stationary This results from excessive filtering, which masks genuine rhythm disturbances. The Purpose of the Invention and the Problems It Solves The primary aim of this invention is to address motion artifacts in existing wearable ECG systems and 20 The invention aims to solve the noise problem through the integration of a multi-axis accelerometer. The accelerometer instantly detects the user's walking or running status. The microcontroller inside the device detects that the movement has exceeded the threshold value. The mathematical moving average applied to ECG readings and It autonomously makes the baseline filters more aggressive. Thus, it reduces the physical shock by 25. High-frequency noise generated is suppressed when the user stops or moves. When the sensitivity decreases, the filter coefficients are lowered, and the system returns to high sensitivity mode. It rotates. In addition, the system detects rhythm disorders (arrhythmia, etc.) through the filtered clear signal. It can detect tachycardia locally and transmit it via integrated wireless connectivity. It can send real-time telemetry data to the remote server. 30 Explaining the Figures Figure 1: System architecture and data / power flow between hardware components. It is a block diagram showing the function. Explaining References in Figures 1. Main microcontroller unit 35 2 2. ECG (Biopotential) sensor module 3. 3-axis accelerometer module 4. Integrated TFT screen 5. Battery power circuit Detailed Description of the Invention 40 The system that is the subject of this invention essentially consists of the following hardware components: A motherboard containing the central processing unit and the wireless communications module. microcontroller (1), biopotential that reads electrical activity from skin surface The ECG module (2) is a digital accelerometer that measures acceleration in the xyz axes. module (3), an integrated TFT screen (4) showing the measurement results in real time and 45 Battery power circuit that enables the portability of the system (5). When the system starts, the raw ECG signals received from the analog pins are converted to ECG. The sensor module (2) transmits the information to the main microcontroller (1). Simultaneously instantaneous from the 3-axis accelerometer module (3) via digital communication protocol Motion vectors are read. In the system's software architecture, the acceleration vector is 50. There is a dynamic coefficient matrix that comes into play depending on its magnitude. When the accelerometer data exceeds a certain threshold (active movement / walking state), the main microcontroller (1), low-pass filter (LPF) applied to ECG signal sequence and It autonomously increases the moving average multiples. This dynamic calibration, The electrical noise resulting from muscle contractions and cable oscillations instantly decreases to 55. It allows for damping. When the motion data stabilizes (when the user stops or rests), By returning the filter coefficients to their normal values, the smallest QRS complexes are obtained. This allows even variations to be detected. Through the cleaned signal... Anomalies in the calculated RR ranges are detected by the main microcontroller (1) 60 The device is analyzed, and if a crisis condition (arrhythmia) is detected, the device will send a signal via its Wi-Fi module. Autonomous log records and device geographic location are sent to the integrated cloud server. synchronizes as follows. All this dynamic process is displayed on the device's integrated TFT screen (4) It is visualized instantly. Power requirement is via the battery power circuit (5). is provided. 65

Claims

3 REQUESTS 1. It is a wearable health tracking and telemetry device whose feature is; it monitors the user's real-time acceleration. a 3-axis accelerometer that measures vectors, a device that reads biopotential signals an electrocardiography (ECG) sensor and a microcontroller that processes these two data points simultaneously. It includes 5 based on the instantaneous motion intensity data that the microcontroller receives from the accelerometer. Mathematical digital filtering is applied to the raw signal from the ECG sensor. It is the autonomous and dynamic modification of its coefficients.

2. The device mentioned in Claim 1 is characterized by its ability to detect changes in the ECG signal during movement. To prevent artifacts (cable wobble and muscle noise), the acceleration data must meet a certain threshold. When it exceeds 10, the moving average and low-pass filters increasing its intensity, and when no motion is detected in the system, it uses sensitive measurement coefficients. It is the return.

3. The device mentioned in Claim 1 is characterized by its ability to detect pulse rate from filtered net data. Discrepancies in (BPM) and RR range values ​​are detected locally within the microcontroller. By analyzing it, it can instantly detect possible rhythm disorders (arrhythmia or tachycardia) and 15 This crisis situation and the device's location are monitored via the integrated wireless communication module. It is the process of autonomously synchronizing the data with a remote cloud server.

4. The device mentioned in Claim 1 is characterized by its ability to produce a filtered, clear ECG graph. instantaneous power consumption data, connection status, and detected system parameters. It is visualized in real time on an integrated screen located on it. 20

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