Multi-source physiological information acquisition device

Abstract

The invention discloses a multi-source physiological information acquisition device, and relates to the technical field of physiological information acquisition. Comprising an embedded hardware circuit, a plurality of sensors, a close-fitting elastic band and a terminal computer in communication connection with the embedded hardware circuit, and the plurality of sensors comprise bipolar electrodes; electrocardiosignals of a wearer are collected through a bipolar electrode, extended Kalman filtering operation is carried out on the electrocardiosignals, and de-noised electrocardiosignals are obtained; performing empirical mode decomposition on the de-noised electrocardiosignal to obtain a plurality of intrinsic mode functions; processing each intrinsic mode function by adopting an improved shrinkage function, and adding all the processed intrinsic mode functions to obtain a filtered de-noised electrocardiosignal; and synchronously transmitting the filtered de-noised electrocardiosignals and physiological information collected by other sensors to a terminal computer. The space limitation of traditional equipment is broken through, real physiological data in a dynamic scene can be collected, and the system is closer to the daily state of a tested person.

Description

Technical Field

[0001] This invention relates to the field of physiological information acquisition technology, and in particular to a multi-source physiological information acquisition device. Background Technology

[0002] With increasing societal focus on mental and physical health, researchers are seeking more accurate methods to assess cognitive and emotional states in order to better understand and address mental and physical health issues. In the industrial and transportation sectors, monitoring workers' cognitive states and fatigue levels has become crucial for improving operational safety and work efficiency. This research background aims to gain a deeper understanding of human mental and physiological states through scientific methods, conducting cognitive and emotional assessments to support improvements in mental health, work performance, and quality of life.

[0003] Currently, numerous researchers are studying algorithms for cognitive state assessment, emotional state assessment, and fatigue monitoring. The data sources for these studies are all multi-source physiological data. First, rigid, fixed sensors (such as electrodes or blood oxygen probes) are installed in specific body parts (e.g., chest, fingers). These sensors are then connected to an external, independent host unit via multiple wires. The host unit integrates signal acquisition, processing, and storage modules; it is relatively large and needs to be placed on a desktop or in a fixed location. During the procedure, the subject must remain relatively still to avoid movement that could cause the wires to become tangled or the sensors to fall off. Data is transmitted to the host unit via wired connections.

[0004] Traditional multi-source physiological monitoring equipment relies on wired connections, bulky main units, and fixed sensors. When collecting multi-source physiological data, the subjects can only remain relatively still, causing the data to deviate from real-life scenarios and making it impossible to measure the most authentic physiological data of the subjects in daily life. Summary of the Invention

[0005] Therefore, it is necessary to provide a multi-source physiological information acquisition device to address the aforementioned technical problems.

[0006] This invention provides a multi-source physiological information acquisition device, comprising: Embedded hardware circuitry, multiple sensors for acquiring multi-source physiological information, a close-fitting elastic band for carrying the embedded hardware circuitry and multiple sensors, and a terminal computer for communicating with the embedded hardware circuitry, wherein the multiple sensors include bipolar electrodes for acquiring electrocardiogram signals. The wearer's electrocardiogram (ECG) signal is acquired through bipolar electrodes. The ECG signal is then subjected to an extended Kalman filter to obtain a denoised ECG signal. Empirical mode decomposition (EMD) is performed on the denoised ECG signal to obtain multiple intrinsic mode functions (IMFs). An improved contraction function is used to process each IMF. The improved contraction function is obtained by operating the contraction function with a sign function. All processed IMFs are then summed to obtain the filtered denoised ECG signal. The filtered and denoised ECG signal and physiological information collected by other sensors are synchronously transmitted to the terminal computer through embedded hardware circuitry.

[0007] Optionally, the various sensors also include: flexible force-sensitive resistor arrays, reflective blood oxygen saturation sensors, and body temperature sensors; A flexible force-sensitive resistor array is used to collect the wearer's breathing rate; A reflective blood oxygen saturation sensor is used to collect the wearer's blood oxygen saturation. A body temperature sensor is used to collect the wearer's body temperature data.

[0008] Optionally, one electrode of the bipolar electrode is placed on the upper right chest and the other electrode is placed on the lower left chest; The flexible force-sensitive resistor array is located in the lower right chest area; The reflective blood oxygen saturation sensor and body temperature sensor are located together in the right chest area.

[0009] Optionally, the embedded hardware circuit includes: a signal conditioning module, a resistor voltage divider circuit, a serial communication module, an analog-to-digital converter, and a microcontroller; the signal conditioning module includes: a bandpass filter and a signal amplifier. The bipolar electrode is connected to the signal conditioning module to acquire the wearer's electrocardiogram (ECG) signal. The ECG signal is denoised by software and filtered by hardware through a bandpass filter and a signal amplifier, respectively. The processed ECG signal is converted into a digital signal by an analog-to-digital converter and transmitted to the terminal computer through a microcontroller. A flexible force-sensitive resistor array is connected to a resistor voltage divider circuit to determine the wearer's breathing rate by the change in the resistance of the resistor voltage divider circuit. The breathing rate is converted into a digital signal by an analog-to-digital converter and transmitted to the terminal computer by a microcontroller. The reflective blood oxygen saturation sensor and body temperature sensor are used to acquire the wearer's blood oxygen saturation and body temperature data, which are then read by the serial communication module and transmitted to the microcontroller, and then transmitted to the terminal computer by the microcontroller.

[0010] Optionally, the microcontroller is a low-power chip, which uses the STM32L152 or MSP430 series chips and operates in the range of 1.65V–3.3V.

[0011] Optionally, the embedded hardware circuit also includes a communication module, which includes a Bluetooth or Zigbee module for transmitting the wearer's physiological data obtained by the embedded hardware circuit to a terminal computer in real time.

[0012] Optionally, an extended Kalman filter is performed on the ECG signal based on the following formula to obtain a denoised ECG signal: ; ; ; ; in, Sampling time, Heart rate, The number of Gaussian functions. The phase of the observed value, This is a measured electrocardiogram with noise. and All are time steps k Measurement noise, For time step k Phase state variables, For the first i The center phase of a Gaussian function For the first i The amplitude coefficients of a Gaussian function, For the first i The width parameter of a Gaussian function, For time step k Ideal electrocardiogram signal, For time step k The denoised ECG signal For time step k Noisy ECG signals, For time step k The noise estimate; Empirical mode decomposition was performed on the denoised ECG signal to obtain multiple intrinsic mode functions; Based on the following formula, an improved contraction function is used to process each intrinsic mode function: ; Where sgn() represents the sign function, For high threshold, For low threshold, To improve the contraction function, Y The intrinsic mode function is used as the input to the contraction function; The filtered, denoised ECG signal is obtained by summing all the processed intrinsic mode functions according to the following formula: ; in, V This is the filtered electrocardiogram signal. For the first n The processed intrinsic mode function n =1, 2, 3, ... N , N This represents the total number of IMF components.

[0013] The multi-source physiological information acquisition device provided in this embodiment of the invention has the following advantages compared with the prior art: This invention integrates the dual functions of software denoising and hardware filtering into an embedded hardware circuit. It completes signal processing locally and transmits the filtered and denoised ECG signal and physiological information collected by other sensors to the terminal computer synchronously through the embedded hardware circuit. This completely eliminates the constraints of wired connections, significantly reduces the size of the host device, and integrates it into an elastic band. This design not only breaks through the space limitations of traditional devices but also enables the collection of real physiological data in dynamic scenarios, which is closer to the daily state of the test subjects.

[0014] Meanwhile, using a close-fitting elastic band as a carrier, it highly integrates multiple sensors. The flexible material fits the human body, ensuring stable contact between the sensors and the skin while avoiding the restriction of movement by rigid equipment, allowing the test subject to wear it naturally during daily activities (such as walking and exercising). Attached Figure Description

[0015] Figure 1 This is a schematic diagram of a multi-source physiological information acquisition device provided in one embodiment; Figure 2 This is a hardware circuit design logic diagram of a multi-source physiological information acquisition device provided in one embodiment.

[0016] Among them, 1. Embedded hardware circuit; 2. Bipolar electrode; 3. Flexible force-sensitive resistor array; 4. Reflective blood oxygen saturation sensor; 5. Body temperature sensor; 6. Body-fitting elastic band. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0018] In one embodiment, a multi-source physiological information acquisition device is provided, such as... Figure 1 As shown, the device includes: an embedded hardware circuit 1, multiple sensors, a close-fitting elastic band 6 for carrying the embedded hardware circuit 1 and the multiple sensors, and a terminal computer that is communicatively connected to the embedded hardware circuit 1.

[0019] Multiple sensors are included: a bipolar electrode 2, a flexible force-sensitive resistor array 3, a reflective blood oxygen saturation sensor 4, and a body temperature sensor 5. The bipolar electrode 2, flexible force-sensitive resistor array 3, reflective blood oxygen saturation sensor 4, and body temperature sensor 5 simultaneously acquire the wearer's electrocardiogram (ECG) signal, respiratory rate, blood oxygen saturation, and body temperature data. After software denoising and hardware filtering of the ECG signal through embedded hardware circuit 1, the filtered denoised ECG signal, respiratory rate, blood oxygen saturation, and body temperature data are synchronously transmitted to the terminal computer.

[0020] The connection methods and functions of the various devices in this invention are as follows: The electrical signal from the bipolar electrode 2 is amplified and filtered by the embedded hardware circuit 1, and then the electrocardiogram signal is read out by the analog-to-digital converter (ADC). The resistance of the flexible force-sensitive resistor array 3 changes according to the pressure changes during the breathing of the person being tested. The embedded hardware circuit 1 can calculate the respiratory rate of the person being tested by measuring the resistance of the resistor array 3. The reflective blood oxygen saturation sensor 4 and the body temperature sensor 5 send the measured blood oxygen saturation and body temperature of the person being tested to the hardware circuit 1 through serial port, analog signal, etc. The hardware circuit 1 sends the multi-source physiological data to a remote terminal computer through Internet of Things communication methods such as Bluetooth or Zigbee, and the terminal computer saves the data locally.

[0021] As a preferred embodiment of the present invention, one electrode of the bipolar electrode 2 is placed in the upper right chest (position V1 in a standard 12-lead electrocardiogram), and the other electrode is placed in the lower left chest (position V6 in a standard 12-lead electrocardiogram). This connection method can acquire as much electrocardiogram information as possible through the bipolar electrodes. The flexible force-sensitive resistor array 3 is located in the lower right chest, which is a position with a large respiratory movement amplitude, and can better measure pressure. The reflective blood oxygen saturation sensor 4 and the body temperature sensor 5 are placed in the right chest position to reduce interference with the electrocardiogram signal in the left chest. At the same time, the chest is closer to the core than the back, so the data will be more accurate.

[0022] As a preferred embodiment of the present invention, using a close-fitting elastic band 6 to secure the hardware circuit 1 and sensors 2-5 makes the data acquisition device easy to wear. This method of securing the electrodes also avoids the discomfort caused to the skin by prolonged wear of other securing methods such as adhesive electrodes and clip-on electrodes. To obtain accurate electrocardiogram signals, good contact and stability between the electrodes and the skin must be ensured.

[0023] As a preferred embodiment of the present invention, a resistor voltage divider circuit is used to measure the resistance of the flexible force-sensitive resistor array 3, and the pressure on the resistor is calculated by measuring the resistance value, thereby analyzing the breathing state of the manual personnel.

[0024] Embedded hardware circuit 1 includes: a signal conditioning module, a resistor voltage divider circuit, a serial communication module, an analog-to-digital converter, and a microcontroller unit (MCU). The signal conditioning module includes: a bandpass filter and a signal amplifier.

[0025] Bipolar electrode 2 connects to a signal conditioning module to acquire the wearer's electrocardiogram (ECG) signal. The ECG signal undergoes software denoising and hardware filtering via a bandpass filter and signal amplifier, respectively. An analog-to-digital converter (ADC) converts the processed ECG signal into a digital signal, which is then transmitted to the terminal computer via a microcontroller. Flexible force-sensitive resistor array 3 connects to a resistor divider circuit to determine the wearer's respiratory rate based on the resistance change of the circuit. The respiratory rate is converted into a digital signal via an ADC and transmitted to the terminal computer via the microcontroller. A reflective blood oxygen saturation sensor 4 and a body temperature sensor 5 acquire the wearer's blood oxygen saturation and body temperature data. These data are read via a serial communication module and transmitted to the microcontroller, which then transmits the data to the terminal computer.

[0026] like Figure 2 As shown, the embedded hardware circuit 1 mainly consists of a microcontroller, power supply, resistor voltage divider circuit, bandpass filter, signal amplifier, analog-to-digital converter, serial communication module, electrode signal, blood oxygen sensor, body temperature sensor, and Bluetooth module.

[0027] The connection to the power supply represents the power supply circuit, which powers the microcontroller, bipolar electrodes, resistor divider circuit, blood oxygen sensor, and body temperature sensor. The connection not to the power supply represents the signal circuit. The analog signals from the bipolar electrodes and resistor divider circuit are filtered by a filter circuit, then amplified by a signal amplifier circuit, and finally converted into digital signals by an analog-to-digital converter for the microcontroller to read. Data from the blood oxygen sensor and body temperature sensor is transmitted to the microcontroller via a serial communication module. Simultaneously, IoT communication modules such as Bluetooth also process data via serial communication. The microcontroller then transmits the processed digital signals to the terminal computer via the IoT communication module.

[0028] As a preferred embodiment of the present invention, the microcontroller is a low-power chip, employing an STM32L152 or MSP430 series chip, with an operating voltage range of 1.65V–3.3V. It is a low-power, small-size microcontroller that can be directly connected to the BLE module via a serial port. Other low-power MCUs such as the MSP430 series or STM32L series can be used as alternatives; the specific selection should consider whether any communication interfaces other than those required by the present invention are needed.

[0029] As a preferred embodiment of the present invention, the signal processing of the bipolar electrode 2 employs the BMD101 chip, which features a powerful digital signal processing architecture and excellent analog front-end circuitry, enabling the circuit to process biological signal inputs ranging from volts to millivolts. To filter the ECG signal and remove unwanted noise and interference, the circuit simultaneously uses high-pass and low-pass filters to create a bandpass filter with a passband of 0.5-40 Hz. The use of this filter is crucial for obtaining accurate ECG signals, as it helps to remove noise and interference that affect signal quality.

[0030] As a preferred embodiment of the present invention, the PCB size of the multi-source physiological data acquisition device is very small (50×80 mm), similar to the size of a credit card. It is powered by a 3V lithium button battery. The microcontroller chip, sensors, electrodes, and other devices requiring power in this invention operate at voltages between 1.65V and 3.3V. Using a 3V button battery to power the low-power circuitry of this invention ensures sufficient voltage and maintains portability.

[0031] Signals acquired via an ADC may contain noise and interference from various sources, such as power lines, motion artifacts, and electromagnetic interference. To remove these unwanted signals and improve the quality of the ECG signal, signal conditioning is necessary. Signal conditioning employs filtering, amplification, and baseline removal. Filtering uses high-pass, low-pass, or band-pass filters to remove unwanted frequency components; amplification increases the amplitude of the ECG signal to improve its visibility and distinguish it from noise; baseline removal eliminates the DC bias of the ECG signal, which can blur the waveform and make analysis difficult.

[0032] In a preferred embodiment of the present invention, the hardware circuit 1 communicates with the terminal computer 7 via Bluetooth. The hardware circuit 1 connects directly to the terminal computer's Bluetooth via an external or onboard Bluetooth serial communication module, and sends the collected multi-source physiological data to the terminal computer. Another option for IoT communication is Zigbee communication. Compared to Bluetooth, Zigbee has lower power consumption, allowing for longer battery life, but its communication speed is lower, and for most terminal devices, an additional Zigbee USB adapter is required for communication.

[0033] In one embodiment, the specific process of hardware circuit 1 performing software denoising and hardware filtering includes: After the embedded chip reads the ECG signal, the data is filtered by software. The filtering scheme for bipolar ECG signals involves the following four steps:

[0034] Step 1: Perform an extended Kalman filter on the ECG signal to eliminate irregular waveforms and obtain a denoised ECG signal. (1) Then, the noise estimate is subtracted from the noisy ECG signal to obtain the denoised ECG signal: (2) in, Sampling time, Heart rate, The number of Gaussian functions. The phase of the observed value, This is a measured electrocardiogram with noise. and All are time steps k Measurement noise, For time step k Phase state variables, For the first i The center phase of a Gaussian function For the first i The amplitude coefficients of a Gaussian function, For the first i The width parameter of a Gaussian function, For time step k Ideal electrocardiogram signal, For time step k The denoised ECG signal For time step k Noisy ECG signals, For time step k The noise estimate.

[0035] Step 2: In order to obtain approximate and detailed coefficients, empirical mode decomposition (EMD decomposition) is performed on the denoised ECG signal to obtain multiple intrinsic mode functions (IMF).

[0036] Step 3: An improved contraction function is used to process each intrinsic mode function to remove or reduce noise components. To achieve the best denoising effect, this invention proposes a new improved contraction function for ECG signal denoising:

[0037] (3) Where sgn() represents the sign function, used to return the sign of a real number. In the formula... and These are the high and low thresholds, respectively, and are key parameters for controlling the intensity of signal denoising. To improve the contraction function, YThis is the intrinsic mode function used as the input to the contraction function. This contraction function is smooth, continuous, and adjustable. Compared with traditional contraction functions, it is more resistant to distortion and signal loss, and its parameters can be adjusted to adapt to environments with different noise characteristics.

[0038] Step 4: Add all the processed intrinsic mode functions together to obtain the filtered and denoised ECG signal.

[0039] (4) in, V This is the filtered electrocardiogram signal. For the first n The processed intrinsic mode function n =1, 2, 3, ... N , N This represents the total number of IMF components.

[0040] The present invention has the following advantages: 1. Existing equipment for collecting multi-source physiological data typically involves large measuring devices in fixed settings, requiring the subject to remain relatively still. This invention offers excellent portability, allowing subjects to carry it in their living environment for extended periods, and can measure the most authentic physiological data from their daily lives.

[0041] 2. Compared with existing portable and wearable physiological information monitoring devices, the present invention can measure multi-source physiological data and has higher accuracy and noise resistance.

[0042] 3. The embedded hardware circuit in this invention features low cost and low power consumption. The hardware circuit adopts a "credit card" shape, which occupies little space, requires a small battery, has a long battery life, and does not cause discomfort when worn.

[0043] 4. A hardware circuit filtering and software filtering scheme was implemented for the electrocardiogram signal. By proposing an improved contraction function for noise reduction, more accurate results were obtained.

[0044] 5. This invention is highly portable, allowing test subjects to carry it in their living environment for extended periods. It measures the most authentic physiological data from the test subjects' daily lives, offering higher accuracy and noise resistance. It employs a software denoising and hardware filtering scheme for electrocardiogram signals and proposes a new contraction function for denoising, resulting in more accurate results.

[0045] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A multi-source physiological information acquisition device, characterized in that, include: An embedded hardware circuit (1), multiple sensors for acquiring multi-source physiological information, a close-fitting elastic band (6) for carrying the embedded hardware circuit (1) and the multiple sensors, and a terminal computer that is communicatively connected to the embedded hardware circuit (1), wherein the multiple sensors include a bipolar electrode (2) for acquiring electrocardiogram signals. The wearer's electrocardiogram (ECG) signal is acquired through the bipolar electrode (2), and an extended Kalman filter is applied to the ECG signal to obtain a denoised ECG signal. Empirical mode decomposition is performed on the denoised ECG signal to obtain multiple intrinsic mode functions (IMFs). An improved contraction function is used to process each IMF. The improved contraction function is obtained by operating the contraction function with a sign function. All processed IMFs are then added together to obtain the filtered denoised ECG signal. The filtered and denoised electrocardiogram signal and the physiological information collected by other sensors are synchronously transmitted to the terminal computer through the embedded hardware circuit (1).

2. The multi-source physiological information acquisition device as described in claim 1, characterized in that, The various sensors also include: a flexible force-sensitive resistor array (3), a reflective blood oxygen saturation sensor (4), and a body temperature sensor (5). The flexible force-sensitive resistor array (3) is used to collect the wearer's breathing rate; The reflective blood oxygen saturation sensor (4) is used to collect the wearer's blood oxygen saturation. The body temperature sensor (5) is used to collect the wearer's body temperature data.

3. The multi-source physiological information acquisition device as described in claim 2, characterized in that, One electrode of the bipolar electrode (2) is placed on the upper right chest, and the other electrode is placed on the lower left chest; The flexible force-sensitive resistor array (3) is located on the lower right chest. The reflective blood oxygen saturation sensor (4) and the body temperature sensor (5) are located together in the right chest area.

4. The multi-source physiological information acquisition device as described in claim 3, characterized in that, The embedded hardware circuit (1) includes: a signal conditioning module, a resistor voltage divider circuit, a serial communication module, an analog-to-digital converter, and a microcontroller. The signal conditioning module includes: a bandpass filter and a signal amplifier. The bipolar electrode (2) is connected to the signal conditioning module and is used to acquire the wearer's electrocardiogram (ECG) signal. The ECG signal is denoised by software and filtered by hardware through the bandpass filter and the signal amplifier, respectively. The processed ECG signal is converted into a digital signal through the analog-to-digital converter and transmitted to the terminal computer through the microcontroller. The flexible force-sensitive resistor array (3) is connected to the resistor voltage divider circuit, and is used to determine the wearer's breathing frequency by the resistance change of the resistor voltage divider circuit. The breathing frequency is converted into a digital signal by the analog-to-digital converter and transmitted to the terminal computer by the microcontroller. The reflective blood oxygen saturation sensor (4) and the body temperature sensor (5) are used to acquire the wearer's blood oxygen saturation and body temperature data, and transmit them to the microcontroller after reading them through the serial communication module, and then transmit them to the terminal computer through the microcontroller.

5. The multi-source physiological information acquisition device as described in claim 4, characterized in that, The microcontroller is a low-power chip, which uses an STM32L152 or MSP430 series chip and operates in the voltage range of 1.65V–3.3V.

6. The multi-source physiological information acquisition device as described in claim 4, characterized in that, The embedded hardware circuit (1) further includes a communication module, which includes a Bluetooth or Zigbee module for transmitting the wearer's physiological data obtained by the embedded hardware circuit (1) to a terminal computer in real time.

7. The multi-source physiological information acquisition device as described in claim 1, characterized in that, The ECG signal is then subjected to an extended Kalman filter based on the following formula to obtain a denoised ECG signal: ； ； ； ； in, Sampling time, Heart rate, The number of Gaussian functions. The phase of the observed value, This is a measured electrocardiogram with noise. and All are time steps k Measurement noise, For time step k Phase state variables, For the first i The center phase of a Gaussian function For the first i The amplitude coefficients of a Gaussian function, For the first i The width parameter of a Gaussian function, For time step k Ideal electrocardiogram signal, For time step k The denoised ECG signal For time step k Noisy ECG signals, For time step k The noise estimate; Empirical mode decomposition was performed on the denoised ECG signal to obtain multiple intrinsic mode functions; Based on the following formula, an improved contraction function is used to process each intrinsic mode function: ； Where sgn() represents the sign function, For high threshold, For low threshold, To improve the contraction function, Y The intrinsic mode function is used as the input to the contraction function; The filtered, denoised ECG signal is obtained by summing all the processed intrinsic mode functions according to the following formula: ； in, V This is the filtered electrocardiogram signal. For the first n The processed intrinsic mode function n =1, 2, 3, ... N , N This represents the total number of IMF components.

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