Heart rate and blood oxygen non-inductive detection method and system based on mobile phone shell

Abstract

The application discloses a heart rate and blood oxygen non-inductive detection method and system based on a mobile phone shell, comprising the following steps: (1) collecting red light spectrum signal data and near-infrared light spectrum signal data; (2) evaluating whether the data collected in step (1) participates in heart rate and blood oxygen calculation based on a signal quality evaluation mechanism; and (3) detecting blood oxygen saturation based on a ratio method. When a user unconsciously or consciously holds the mobile phone and places fingers naturally in a designated detection area of the mobile phone shell, the application can automatically complete detection, and detection data is transmitted in real time to a mobile phone end through Bluetooth communication and processed, analyzed and synchronized on a cloud end by a health management App, so that a personal vital sign monitoring system is constructed, centralized storage and unified management of data are realized, long-term trend analysis, individual health baseline modeling and abnormal fluctuation identification can be carried out on the basis, and reliable data support is provided for application scenes such as exercise health evaluation.

Description

Technical Field

[0001] This invention relates to the field of smart wearable devices and mobile health monitoring technology, specifically to a non-intrusive heart rate and blood oxygen detection method and system based on a mobile phone case. Background Technology

[0002] Heart rate (HR) and blood oxygen saturation (SpO2) are two core vital signs that reflect the cardiopulmonary function and circulatory system status of the human body. They are of great significance in scenarios such as cardiovascular disease screening, chronic disease management, sports and health, sleep monitoring, and early warning of sudden health risks.

[0003] Currently, heart rate and blood oxygen monitoring mainly rely on the following types of equipment: Medical monitoring equipment, such as finger-clip pulse oximeters and multi-parameter monitors, although highly accurate, are bulky and limited in application scenarios, making it difficult to meet the needs of daily continuous monitoring.

[0004] Wearable devices, such as smart bracelets and smartwatches, typically rely on photoplethysmography (PPG) for detection. However, these devices require active wearing by the user, which can lead to issues such as discomfort, forgetting to wear them, or removing them for charging, resulting in insufficient continuity and completeness of the monitoring data.

[0005] Mobile phone camera detection solution: Using the mobile phone camera and flash to detect fingertips, although no additional hardware is required, the measurement process depends on the user's active operation, the detection posture is unstable, and there are heat and power consumption issues after long-term use.

[0006] In summary, existing technologies generally suffer from the following shortcomings: High user dependence: They require active wearing or deliberate operation, making it difficult to form truly daily, burden-free monitoring; Insufficient continuity: Monitoring behavior is fragmented, making it difficult to capture potential physiological trend changes; Fragmented devices: The detection device is separated from the user's high-frequency terminal (mobile phone), resulting in high costs for data integration and management; The value of health data is not fully explored: Most devices only provide real-time values ​​and lack long-term analysis, risk assessment, and cloud collaboration capabilities.

[0007] As smartphones become the most frequently used personal devices, how to naturally integrate vital sign monitoring capabilities into users' daily mobile phone usage without adding extra burden has become a pressing technical problem in the field of intelligent health monitoring.

[0008] An existing health monitoring phone case (CN212546911U) includes a phone case body, on which a medical module and a Bluetooth module are integrated. The medical module wirelessly interacts with a mobile app via Bluetooth. This medical module primarily integrates a temperature sensor and a heart rate sensor, enabling the collection and transmission of body temperature and heart rate data. However, the existing technology has limited detection capabilities: it only collects and transmits body temperature and heart rate data, without addressing blood oxygen saturation (SpO2) detection, thus failing to comprehensively reflect the user's cardiopulmonary and circulatory system health status. The detection mechanism is entirely passive: the phone case uses a fixed-time collection mode, performing detection according to a preset cycle regardless of finger contact or signal quality, resulting in numerous invalid measurements, high power consumption, and a high false detection rate. It lacks a signal quality evaluation mechanism: without a signal quality evaluation step, unreliable detection results may still be output when there is poor finger contact or ambient light interference. Finally, it lacks cloud-based health management collaboration capabilities: the phone case only wirelessly transmits detection data to the mobile app, without cloud-based health data storage, long-term trend analysis, or anomaly alerts, limiting the value extraction of health data. Unsensory detection mechanism: The detection of this phone case relies on the user's active operation and does not have the unconscious / conscious natural holding trigger mechanism proposed by the invention. Summary of the Invention

[0009] The purpose of this invention is to achieve high-frequency, low-interference, and continuous monitoring of heart rate and blood oxygen without changing user habits or adding extra wearing burden, and to improve the long-term management and risk assessment capabilities of personal health data. This is achieved by proposing a non-intrusive heart rate and blood oxygen detection method and health management system based on a phone case. The heart rate and blood oxygen saturation detection system and its data processing method, integrated into the phone case, belong to the interdisciplinary field of non-intrusive physiological parameter acquisition, mobile terminal health management, and IoT-based medical assistance technology. The detection is automatically completed when the user unconsciously or consciously holds the phone and naturally places their fingers on the designated detection area of ​​the phone case. The detection data is transmitted to the mobile phone in real time via Bluetooth communication, and processed, analyzed, and synchronized to the cloud by the health management app, thereby constructing a low-invasive, high-frequency, and continuous personal vital sign monitoring system.

[0010] The present invention is achieved by at least one of the following technical solutions.

[0011] A non-invasive method for detecting heart rate and blood oxygen based on a mobile phone case includes the following steps: (1) Collect red light spectral signal data and near-infrared light spectral signal data; (2) Evaluate whether the data collected in step (1) based on the signal quality assessment mechanism is used in the calculation of heart rate and blood oxygen; (3) Blood oxygen saturation was detected based on the ratio method.

[0012] Furthermore, in step (1), the detection is automatically initiated when the following conditions are met:

[0013] in express The light intensity signal received at any time, Minimum detection threshold for optical signal amplitude; The duration for which the condition is met continuously. This represents the minimum duration for which signal stability is achieved. This represents the light intensity signal received at time t. Represents the energy of the exchange component. This represents the minimum acceptable threshold for the AC component energy of the SPP signal.

[0014] Furthermore, in step (2), a signal quality index is introduced. Determine whether the detected data is used in heart rate and blood oxygen calculations:

[0015] in: Signal-to-noise ratio (SNR) is the ratio of useful pulse signal energy to noise energy in a PPG signal. The morphological correlation coefficient between adjacent cardiac cycles represents the morphological consistency of continuous pulse waveforms, and its value ranges from 0 to 1. The standard deviation of the pulse interval represents the stability of the heart rate. , , These are the weighting coefficients for the three parameter terms; when When this data segment is used, it is discarded and not included in the calculation of heart rate and blood oxygen. This is the lowest acceptable threshold for the signal quality index.

[0016] Furthermore, in step (3), the detection of blood oxygen saturation is carried out in the following specific steps: The first step is to perform blood flow-modal bandpass filtering on the sampling signals of the red light channel and the near-infrared light channel respectively, and then extract the AC and DC components of each channel to obtain the AC / DC ratio of the red light channel optical signal and the AC / DC ratio of the near-infrared light channel optical signal. The second step is to calculate the AC / DC ratio of red light and near-infrared light. The third step is to calculate blood oxygen saturation based on the ratio by using a linear fitting relationship.

[0017] The system for implementing the non-invasive heart rate and blood oxygen detection method based on a mobile phone case includes: The optical emitting unit is used to emit light signals of a certain wavelength to the skin tissue of the finger being tested, providing a light source for the acquisition of PPG signals by photoplethysmography. The emitted light signals are received by the optical receiving unit after being reflected by the finger tissue. An optical receiving unit is used to receive light signals reflected or transmitted through human tissue and convert the received light signals into electrical signals for output to the analog front-end circuit. The analog front-end circuit (AFE) is used to amplify, filter, and perform analog-to-digital conversion on the weak electrical signal output from the optical receiving unit, and output the digitized PPG signal to the microcontroller unit. The microcontroller unit (MCU) is used to sample and control the digital PPG signal output by the analog front-end circuit (AFE), perform basic digital signal processing, and manage the communication process of the Bluetooth Low Energy (BLE) communication module. The Bluetooth Low Energy (BLE) communication module is used to send the detection results of heart rate and blood oxygen in the form of data frames to the mobile phone under the control of the microcontroller unit (MCU) via the BLE protocol, and at the same time receive configuration commands from the mobile phone. An independent power supply unit and a power management unit are used to provide independent power to each unit and control the system to enter a sleep state to reduce power consumption when there is no effective detection.

[0018] Furthermore, the microcontroller unit (MCU) sends blood pressure sensor data frames to the mobile phone via the Bluetooth Low Energy (BLE) communication module. Each frame of uploaded data includes the unique device identifier of the heart rate and blood oxygen detection module, a timestamp, heart rate value, blood oxygen saturation, and signal quality index.

[0019] Furthermore, the Bluetooth Low Energy (BLE) communication module employs the following power control strategy for communication: (1) When the detection window on the back or side of the phone case comes into contact with a finger but the signal quality is not up to standard, the Bluetooth Low Energy (BLE) communication module enters the low-power broadcast mode. (2) When there is no effective detection, i.e. when the finger leaves the detection area, the Bluetooth Low Energy (BLE) communication module enters sleep mode and the MCU switches to deep sleep. (3) After each valid detection is completed, the Bluetooth Low Energy (BLE) communication module sends the detection results to the mobile phone in batches in an event-driven manner, and returns to the sleep state immediately after sending. (4) By dynamically adjusting the BLE broadcast interval of the Bluetooth Low Energy communication module, the optimal balance between detection frequency and power consumption is achieved.

[0020] Furthermore, the Bluetooth Low Energy (BLE) broadcast interval of the Bluetooth Low Energy communication module is dynamically adjusted using the following formula:

[0021] in This represents the average power consumption over a complete duty cycle. This indicates the power consumption of the Bluetooth Low Energy (BLE) communication module when it is in transmit mode. This indicates the cumulative time the Bluetooth Low Energy (BLE) communication module has been in the transmitting state. This indicates the power consumption when the Bluetooth Low Energy (BLE) communication module or MCU enters sleep mode; This indicates the cumulative time the system has been in a sleep state; This represents the total time of a complete work cycle.

[0022] A computer device according to the present invention includes a memory and a processor, the memory being electrically connected to the processor, the memory storing a computer program, which, when executed by the processor, causes the processor to implement the method described herein.

[0023] The present invention provides a computer-readable storage medium storing a computer program, wherein when the computer program is executed by a processor, the processor implements the method described herein.

[0024] Compared with the prior art, the present invention has at least the following significant and verifiable beneficial effects: (1) Achieve truly seamless vital sign monitoring and significantly improve user compliance.

[0025] This invention integrates heart rate and blood oxygen detection modules into a single device within a phone case, creating a stable connection between the detection hardware and the user's frequently used smartphone. During normal phone use, such as holding, browsing, or making calls, the user's fingers will unconsciously or consciously touch the detection area, and the system will automatically collect vital signs data without requiring additional wearing or user intervention. Compared to existing detection solutions that rely on wearable bracelets, watches, or dedicated finger clips, this invention fundamentally eliminates human interference factors such as "forgetting to wear," "removing to charge," and "discomfort while wearing," effectively solving the common problem of insufficient compliance in long-term health monitoring scenarios for wearable devices. This allows vital sign monitoring to truly integrate into the user's daily behavior, resulting in a higher likelihood of continued use.

[0026] (2) Significantly improve monitoring frequency and data continuity, providing a basis for trend analysis.

[0027] Traditional vital sign monitoring primarily relies on single measurements, resulting in highly discrete data that fails to reflect the dynamic changes in the human body's physiological state. This invention utilizes a non-invasive detection mechanism, allowing users to collect heart rate and blood oxygen saturation data multiple times a day for short periods. This creates a high-frequency, quasi-continuous sequence of vital sign data without increasing the user's burden. This method of acquiring data through multiple short-term, routine collections helps to statistically mitigate the impact of random errors and effectively captures potential trends such as heart rate variability and blood oxygen fluctuations, providing a more comprehensive and reliable data foundation for subsequent health assessments, anomaly identification, and individual baseline modeling. This shift from "outcome-based detection" to "process-based monitoring" is difficult to achieve with existing technologies.

[0028] (3) Deep collaboration between hardware and software significantly improves the reliability and stability of system measurement.

[0029] This invention does not simply add hardware detection functions, but rather improves overall measurement quality through deep collaboration between hardware acquisition, signal processing algorithms, and software strategies. At the hardware level, a dual-wavelength optical detection structure is employed to ensure the integrity of the physiological information required for heart rate and blood oxygenation calculations. At the algorithm level, a Signal Quality Index (SQI) evaluation mechanism is introduced to comprehensively quantify and judge the signal-to-noise ratio, periodicity consistency, and stability of the acquired signals. By real-time identification and elimination of low-quality signals, and combined with a dual-wavelength joint modeling method to calculate heart rate and blood oxygenation parameters, this invention effectively reduces the probability of false measurements caused by unstable finger contact, ambient light interference, or instantaneous movement. It significantly improves the stability and reliability of the detection results in complex daily usage environments, enabling the system to meet the basic requirements for data reliability in long-term health management scenarios.

[0030] (4) Reduce system usage costs and deployment complexity, and have good universality and scalability.

[0031] Compared to specialized testing equipment or wearable devices that require separate purchase, maintenance, and charging, this invention empowers ordinary smartphones with vital sign monitoring capabilities simply by replacing the phone case with one that includes a detection function, significantly lowering the barrier to entry for users and reducing overall costs. This solution fully leverages the existing advantages of smartphones in computing power, display interaction, and network communication, avoiding the duplication of complex system development. It features a simple structure, convenient deployment, and strong adaptability, making it suitable for different brands and models of mobile terminals, and possesses excellent potential for large-scale promotion and commercial application feasibility.

[0032] (5) Supports cloud-based health management and risk warning expansion, and has long-term evolution capabilities.

[0033] This invention uses Bluetooth communication and a health management app to upload collected vital sign data to a cloud server, achieving centralized storage and unified management of the data. Based on this, further long-term trend analysis, individual health baseline modeling, and abnormal fluctuation identification can be conducted, providing reliable data support for applications such as chronic disease management, sub-health screening, and exercise health assessment. Compared to localized devices that can only display immediate measurement results, this invention reserves expansion interfaces for health assessment, risk warning, and intelligent analysis at the system architecture level, possessing the capability to evolve from basic vital sign collection to an intelligent health management platform. It has a long technological lifespan and broad application expansion potential. Attached Figure Description

[0034] Figure 1 This is an architecture diagram of a mobile phone case-based non-contact heart rate and blood oxygen detection system as an example.

[0035] Figure 2 The flowchart for blood oxygen saturation detection based on a dual-wavelength optical model is shown in the example.

[0036] Figure 3 This is a schematic diagram of the processing flow between the health management app and the cloud-based collaborative processing system, as shown in the example. Detailed Implementation

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] The heart rate and blood oxygen detection system based on a mobile phone case in this embodiment includes: Optical emission unit: Used to emit light signals of a specific wavelength to the skin tissue of the finger being tested according to control commands, providing a light source for subsequent PPG (photoplethysmography) signal acquisition. This unit includes at least: a set of red light-emitting diodes (LEDs) and a set of near-infrared light-emitting diodes (LEDs). The set of red LEDs (with a center wavelength of approximately 660 nm) is used to emit red light spectral signals; the set of near-infrared LEDs (with a center wavelength of approximately 940 nm) is used to emit near-infrared light spectral signals. Red light is mainly used to detect changes in the concentration of oxyhemoglobin, while near-infrared light is more sensitive to changes in the concentration of deoxyhemoglobin. Using both together, blood oxygen saturation (SpO2) can be calculated based on the ratio of ratios (ROR) principle. Both the red LEDs and near-infrared LEDs are reflectively mounted on the inside of the phone case facing the front of the phone at the detection window. That is, when the user holds the phone, the finger naturally rests on the upper surface of the detection window, and the light emitted by the LEDs is reflected by the finger tissue and received by the optical receiving unit.

[0039] Optical receiving unit: Used to receive light signals reflected or transmitted through human tissues (skin, blood vessels, etc.) and convert the received light signals into electrical signals for output. This optical receiving unit mainly includes one or a group of photodiodes (PDs). The photodiodes are reflectively mounted in the detection window inside the phone case, arranged parallel to the LEDs in the optical emitting unit, to receive light signals reflected by a finger. The raw current signal output by the photodiode is converted from current to voltage (IV) by an analog front-end (AFE) circuit and subsequently conditioned before being sent to the microcontroller unit.

[0040] Analog Front-End Circuit (AFE): This circuit amplifies, filters, and performs analog-to-digital conversion on the weak electrical signal output from the photodiode, outputting a digitized PPG signal to the microcontroller unit (MCU). In this embodiment, the AFE can be either the MAX30102 module or the MAX86140 module manufactured by Maxim Integrated (now part of Analog Devices). Both are commercially available integrated PPG analog front-end chips, internally integrating a red LED driver, an infrared LED driver, a photodetector, a low-noise amplifier, an analog-to-digital converter, an ambient light suppression circuit, and a digital filter. The AFE's input is connected to the photodiode in the optical receiving unit, converting the photocurrent signal into a voltage signal and performing filtering and analog-to-digital conversion. The AFE's output is connected to the MCU's I²C communication pin via an I²C interface or an I²S bus, sending the digitized PPG data to the MCU. For the MAX30102, refer to the Maxim Integrated Datasheet (Search for: "MAX30102 Maxim Integrated Datasheet").

[0041] Microcontroller Unit (MCU): Used for sampling and controlling the digital PPG signal output by the AFE, performing basic digital signal processing (such as filtering, peak detection, heart rate and blood oxygen calculation), and managing the communication process of the Bluetooth Low Energy (BLE) communication module. In this embodiment, the MCU can be any low-power microcontroller, such as the STM32L0 series, nRF52832, etc. The MCU inputs include the digital PPG data signal sent by the AFE through the I²C interface; the MCU outputs include data packets sent to the BLE communication module through General Purpose Input Output (GPIO) pins or the Serial Peripheral Interface (SPI), and control commands (such as LED switching, sampling rate configuration, etc.) sent to the AFE through the I²C interface.

[0042] The Bluetooth Low Energy (BLE) communication module is used to send heart rate, blood oxygen, and other detection results in the form of data frames to a mobile device via the BLE protocol under the control of the MCU. It also receives configuration commands from the mobile device (such as triggering a conscious detection). The BLE communication module's transmit / receive pins are connected to the MCU's GPIO pins or UART (Universal Asynchronous Receiver / Transmitter) interface. The MCU writes the detection result data to be uploaded into the BLE module's transmit FIFO (First-In-First-Out queue), and the BLE module automatically completes the data transmission at the Bluetooth protocol layer.

[0043] Independent power supply unit and power management unit: These provide independent power to the aforementioned hardware units and control the system to enter a sleep state to reduce power consumption when there is no effective detection. In this embodiment, the power supply unit can use a miniature button battery or a thin-film battery (approximately 0.4 mm thick) embedded inside the phone casing, providing 3.3 V or 1.8 V voltage to the MCU, BLE module, and measuring head via a PMIC (Power Management Integrated Circuit). The power management unit switches the MCU to Deep Sleep Mode during the detection interval, reducing the average current to the microampere (μA) level.

[0044] The connection relationships between the various hardware units are summarized as follows: (1) Inside the PPG optical inspection module: Red light emitting diode / near-infrared light emitting diode is connected to the analog front-end circuit (AFE) through the LED driver circuit; the output terminal of the photodiode is connected to the PD input pin of the AFE.

[0045] (2) Connection between AFE and microcontroller unit (MCU): AFE sends digitized PPG signals and raw data to MCU via I²C bus (SCL clock line, SDA data line); at the same time, it receives configuration instructions from MCU via I²C bus.

[0046] (3) Connection between MCU and Bluetooth Low Energy (BLE) module: The MCU drives the transmit / receive pins of the BLE module through the UART interface of the BLE module or directly through the GPI port to write the data frame to be transmitted to the BLE module, and at the same time receive the control commands from the mobile phone terminal from the BLE module.

[0047] (4) Communication between BLE communication module and mobile phone: BLE module broadcasts BLE signal in the role of Peripheral, and mobile phone scans and pairs in the role of Central; after successful pairing, MCU writes the detection result data into the BLE module's transmission queue, and BLE module automatically completes the data transmission of BLE wireless link to the mobile phone's health management App.

[0048] (5) Power supply: An independent power supply unit provides power to the AFE, MCU and BLE module simultaneously.

[0049] The aforementioned optical transmitting unit, optical receiving unit, analog front-end circuit and microcontroller unit, Bluetooth low power communication module and independent power supply unit and power management unit are all encapsulated and fixed inside the phone case. The detection window is located on the back or side of the phone case, in a position that conforms to the area where the user's fingers naturally touch when holding the phone.

[0050] In this invention, the PPG optical detection module, composed of the aforementioned optical emitting units (red LED and near-infrared LED), optical receiving unit, and AFE module, forms a complete optical sensing link. When the LED emitted light illuminates the user's finger tissue, the PPG optical detection module obtains the raw PPG signal reflecting heart rate and blood oxygenation information based on the difference in light absorption caused by changes in blood volume. The PPG signal is processed internally by the AFE and then further acquired and calculated by the MCU.

[0051] The signal acquisition circuit includes: signal conditioning circuitry in the analog front-end circuit (AFE) (containing a transimpedance amplifier (TIA), a programmable gain amplifier (PGA), hardware filtering circuitry, and an ADC circuit) and digital sampling control logic in the MCU. The signal acquisition circuit can convert the continuous-time signal from the photodiode after AFE conversion according to the sampling rate. (Sampling frequency, in one embodiment) Sampling was performed at 100 Hz to obtain the following discrete time series:

[0052] in, Represents a discrete time series. Indicates a continuous PPG signal. This indicates the vertical line and its subscript, meaning when... hour, Indicates the sampling frequency. The term is a subscript. This invention integrates a PPG optical detection module, a signal acquisition circuit, and a Bluetooth Low Energy communication module into a single mobile phone case through an integrated heart rate and blood oxygen detection structure design, thus making the detection hardware and the mobile phone an integrated peripheral device.

[0053] This invention employs a non-invasive detection triggering mechanism based on natural gripping behavior. By utilizing finger contact status and optical signal quality assessment, it achieves automatic detection initiation and termination without additional operation, based on a PPG-based joint modeling method for heart rate and blood oxygenation. Within the same optical signal acquisition framework, it enables synchronous calculation of HR and SpO2, improving system consistency. Furthermore, it proposes a collaborative processing mechanism between a mobile health management app and the cloud, enabling local data preprocessing, long-term trend analysis, anomaly warning, and cloud-based health record management.

[0054] like Figure 1 As shown, the method for implementing the non-contact heart rate and blood oxygen detection system based on a mobile phone case includes the following steps: S1. Start data acquisition through a non-intrusive triggering and data acquisition strategy, specifically including the following steps: S11. Determine whether to enter a valid detection state based on the unconscious / conscious detection trigger mechanism.

[0055] The following conditions are used to determine whether to enter an effective detection state: light intensity change threshold judgment; signal-to-noise ratio (SNR) evaluation; and finger stability detection (such as short-time variance judgment). Data acquisition and transmission are only initiated when signal quality requirements are met to reduce false detection rate and power consumption.

[0056] The system will automatically start the detection when the following conditions are met:

[0057] in express The light intensity signal received at any time, Minimum detection threshold for optical signal amplitude; The duration for which the condition is met continuously. Indicates the minimum duration for which signal stability is achieved; Exchange component energy as follows:

[0058] in, This represents the instantaneous value of the AC component of the PPG signal at time t, which is the pulse signal extracted after bandpass filtering (typically 0.5-4 Hz). The light intensity signal received at time t is expressed in volts (V). The minimum detection threshold for optical signal amplitude, expressed in volts (V), when If the finger is below this threshold, the sensor is considered not to be in effective contact with the finger, and the detection is stopped. The AC component (Alternating Current component) of the PPG signal is shown in the analysis window. , The energy within the bandpass filter represents the total power of the PPG pulsation signal after a short period of time. It is used to determine whether the PPG signal contains sufficient pulsation characteristics. The unit is volts squared (V²)·second (s). The minimum acceptable threshold for the AC component energy of the SPP signal is expressed in volts squared (V²)·second (s). < The PPG signal is considered too weak to meet the heart rate detection criteria. The time length during which the above three judgment conditions are met continuously, in seconds (s); The shortest duration required for signal stability to meet effective detection requirements, measured in seconds (s), is specified in this embodiment. The value ranges from 1 to 3 seconds. Below At that time, it was considered that finger contact was brief and unstable, and therefore the formal testing process was not initiated.

[0059] The non-sensory detection triggering of the present invention: the detection does not depend on the user's active command, but is automatically triggered based on natural holding behavior.

[0060] In step S11, after the system automatically starts detection, the microcontroller unit (MCU) controls the analog front-end circuit (AFE) to periodically sample the PPG signal. A heart rate and pulse detection strategy is used to extract the pulse peak and pulse interval from the PPG signal in real time, calculating the instantaneous heart rate value. Simultaneously, the MCU calculates the red light AC / DC ratio and the infrared AC / DC ratio every N sampling cycles (in one embodiment, N=30, i.e., every 0.3 seconds), and calculates the current blood oxygen saturation (SpO2) based on the ratio of ratios (RoR) model. The above parameters (PPG sampling signal, heart rate HR, blood oxygen SpO2, signal quality index SQI) are packaged by the MCU and sent to the mobile phone via the Bluetooth Low Energy (BLE) communication module. Detection is triggered not by user-initiated commands, but based on natural grip behavior—when a finger naturally touches the detection window… Enlarge As it rises, when achieve Then the formal testing process begins; when the finger is removed, Upon sudden drop, the system automatically stops detection and enters hibernation mode.

[0061] When an optical emitting unit emits light of a specific wavelength toward finger tissue, the light is absorbed and scattered in the skin, blood vessels, and bone tissue. The absorption characteristics of hemoglobin in the blood for different wavelengths of light are closely related to changes in blood volume.

[0062] S2. Evaluate whether the data detected in step S1 is used in heart rate and blood oxygen calculation based on the signal quality assessment mechanism: Introducing the signal quality index Determine whether the detected data is used in heart rate and blood oxygen calculations:

[0063] in: Signal-to-noise ratio (SNR) is the dimensionless ratio of the useful pulse signal energy to the noise energy in a PPG signal. The morphological correlation coefficient between adjacent cardiac cycles represents the morphological consistency of continuous pulse waveforms, and its value ranges from 0 to 1. The standard deviation of the pulse interval represents the stability of the heart rate, expressed in milliseconds (ms). , , These are the weighting coefficients for the three parameter terms, each determined by experimental calibration, satisfying... + + = 1.

[0064] when When this data segment is used, it is discarded and not included in the calculation of heart rate and blood oxygen. The minimum acceptable threshold for the signal quality index is determined by experimental calibration. In this embodiment... The value ranges from 0.3 to 0.5. If the value is below this threshold, the signal quality is considered insufficient, and the detection result of this segment is skipped and not included in subsequent calculations.

[0065] S3. Blood oxygen saturation detection based on a dual-wavelength optical model. This invention employs a dual-wavelength optical model based on the ratio of ratios (RoR) method to detect blood oxygen saturation. The model uses simultaneous sampling of red and near-infrared light wavelengths, extracts the AC and DC components of each wavelength, calculates the ratio, and finally uses the calibration relationship between the ratio of ratios and SpO2 to calculate blood oxygen saturation. The specific steps are as follows: Step 1: Dual-wavelength light source transmission and reception; its PPG signal can be represented as:

[0066] in for The digital PPG signal sample value output after the analog front-end circuit (AFE) completes transimpedance amplification, bandpass filtering and analog-to-digital conversion at any time indicates the PPG signal that has entered the digital processing stage of the microcontroller unit; The DC component of the PPG signal is mainly formed by the constant absorption and scattering of light by the finger's epidermal tissue, venous blood, and mean arterial blood volume. The AC component of the PPG signal represents the light intensity-modulated pulsating signal, which is generated by the periodic changes in arterial blood volume caused by heartbeats. The DC component represents the signal formed by static tissue and mean blood volume.

[0067] Heart rate calculation: In heart rate detection, the PPG signal is first bandpass filtered to preserve the typical heart rate frequency range (approximately 0.8–3 Hz). The time interval between adjacent pulse peaks is obtained through peak detection or autocorrelation analysis. .

[0068] The formula for calculating heart rate is:

[0069] in Heart rate is expressed in beats per minute (bpm). This represents the time interval between two adjacent pulse peaks, in seconds. The above heart rate calculation model is a standard physiological signal processing model with a clear biomedical engineering theoretical basis, meeting the patent requirement of "having a theoretical basis".

[0070] After bandpass filtering (typically 0.5–4 Hz), heart rate can be estimated using peak detection or autocorrelation methods. :

[0071] in The time interval between two adjacent pulse peaks (in seconds).

[0072] Blood oxygen saturation detection principle and formula: The principle of blood oxygen saturation (SpO2) is that oxygenated hemoglobin (HbO2) and deoxygenated hemoglobin (Hb) have different absorption coefficients for red and near-infrared light. By measuring the difference in absorption of the two for two specific wavelengths of light, blood oxygen saturation can be indirectly calculated.

[0073] According to the Lambert-Beer Law, the attenuation of light passing through biological tissues containing light-absorbing substances satisfies the following:

[0074] in, The intensity of incident light is expressed in watts (W). The received light intensity after transmission / reflection is expressed in watts (W). The absorption coefficient of a specific substance, expressed in cm⁻¹. - ¹·M - ¹; The concentration of the light-absorbing substance is expressed in moles per liter (M). The effective optical path length that light travels through tissue is measured in centimeters (cm).

[0075] Step 2: Extraction of AC / DC components for each channel: Calculation of AC / DC ratio for red and near-infrared light.

[0076] After performing blood flow-modal bandpass filtering on the red light channel sampling signal and the near-infrared light channel sampling signal respectively, the AC component and DC component of each channel are extracted:

[0077]

[0078] in, The AC / DC ratio of the red light channel optical signal (dimensionless); The dominant alternating current component in the red light channel is the effective amplitude of the red light PPG pulsating signal, expressed in volts (V). This represents the direct current component of the red light channel, measured in volts (V). The AC / DC ratio (dimensionless) of the near-infrared (IR) channel optical signal. The AC component of the near-infrared light channel refers to the effective amplitude of the near-infrared PPG signal that pulsates synchronously with the cardiac cycle, measured in volts (V). This represents the DC component of the near-infrared light channel, measured in volts (V).

[0079] Step 3: Calculate the ratio parameter value (R, i.e., RoR value).

[0080] The AC / DC ratios of red light and near-infrared light are further compared:

[0081] Step 4: Linearization calculation of blood oxygen saturation.

[0082] Ratio With blood oxygen saturation Within a certain range, they exhibit an approximately linear relationship, and an empirical model can be obtained through experimental calibration.

[0083] Or, for higher accuracy requirements, a quadratic polynomial fitting model can be used:

[0084] in: (or SpO2) is the percentage of oxygen saturation (peripheral oxygen saturation), which is the percentage of oxyhemoglobin HbO2 to total hemoglobin (HbO2+Hb); , , , , The calibration coefficients are obtained by synchronously comparing this method with clinical standard finger-clip pulse oximeters on a large number of subject samples and then performing linear regression or polynomial fitting. Due to individual differences, this method needs to be individually calibrated before use.

[0085] This embodiment also provides a Bluetooth communication and mobile terminal data interaction method. The detection module of this invention (the PPG optical detection module, signal acquisition circuit, and Bluetooth Low Energy communication module integrated in the phone case) wirelessly interacts with the mobile phone (i.e., the user's smartphone) via the Bluetooth Low Energy (BLE) protocol. The detection device (i.e., the PPG optical detection module and Bluetooth communication unit integrated in the phone case) acts as a BLE slave device (Peripheral), and the mobile phone acts as a BLE master device (Central).

[0086] The MCU inside the phone case sends blood pressure sensor data frames (D) to the phone via the BLE module, uploading data for each frame. include:

[0087] in The unique device identifier for the heart rate and blood oxygen detection module consists of an unmodifiable Bluetooth address or product serial number burned in at the factory, used by the mobile phone to identify the data source; The timestamp represents the relative or absolute time of the data acquisition in milliseconds (ms) or seconds (s); HR (or HR_value) is the heart rate value in beats per minute (bpm). Blood oxygen saturation; The Signal Quality Index (SQI) characterizes the signal quality of a given detection result. It is unitless or normalized to [0,1]. See details... Formula explanation.

[0088] Power consumption control strategy: To adapt to the limited power supply conditions (micro-battery) of the phone case, the system adopts an event-driven communication strategy: (1) When the detection window touches the finger but the signal quality is not up to standard, the BLE module enters the low-power broadcast mode (Advertising Mode), and the BLE broadcast interval is adjustable from 1 second to several tens of seconds. (2) When there is no valid detection (finger leaves the detection area), the BLE module enters sleep mode and the MCU switches to deep sleep mode; (3) After each valid detection is completed, the BLE module sends the detection results to the mobile terminal in batches in an event-driven manner, and returns to the sleep state immediately after sending; (4) By dynamically adjusting the BLE broadcast interval, the optimal balance between detection frequency and power consumption is achieved.

[0089] The average power consumption model of the device can be expressed as:

[0090] in: (Average Power) represents the average power consumption over a complete duty cycle, measured in milliwatts (mW). Transmission Power represents the power consumption of the BLE communication module when it is in the transmit state, measured in milliwatts (mW). (Transmission Time) represents the cumulative time the BLE module is in the transmission state, in seconds (s). (Sleep Power) represents the power consumption of a BLE module or MCU when it enters sleep mode, measured in milliwatts (mW). (Sleep Time) represents the cumulative time the system is in a sleep state, measured in seconds (s). (Cycle Time) represents the total time of a complete work cycle, equal to... + It is the sum of the transmission time and the sleep time, expressed in seconds (s).

[0091] Physical meaning of power model: average power consumption This reflects the arithmetic average power consumption over a duty cycle, which can be increased by... The ratio can significantly reduce average power consumption—this is the theoretical basis for the system's strategy of not performing continuous detection but adopting an event-triggered high burst low duty cycle communication strategy.

[0092] This embodiment also provides a health management app and a cloud-based collaborative processing system, including: Mobile devices are used for data reception and local processing. Mobile health management app, responsible for: (1) Receive and cache detection data: Receive the raw PPG signal and processing results from the detection device via BLE and cache them in the local database; (2) Short-term data smoothing: The RR interval data or PPG instantaneous values ​​collected in multiple consecutive collections are smoothed by using a sliding average filter to reduce random fluctuations in the effective data; The moving average model is:

[0093] in Indicates the first The smoothed output value after moving average processing at any given time; Indicates the first The original signal sample value at time; This indicates the length of the sliding window, i.e., taking the nearest one. In this embodiment, the original data points are averaged. Values One sampling point; This is the data index within the sliding window, with a value range of 0 ≤ ≤ .

[0094] (3) Real-time display: The heart rate and blood oxygen saturation values ​​are displayed in real time on the mobile phone screen, and the measurement reliability indicator is displayed according to the SQI value.

[0095] Cloud servers are used for long-term trend modeling and anomaly detection. The cloud server performs time-series modeling on the user's historical data to calculate the individual's baseline value:

[0096] in: The historical mean, or baseline, represents the typical level of a user's vital signs (heart rate or blood oxygen) in a resting state. Indicates the number of data points or the length of time used to calculate the baseline (e.g., the daily average of the past 7 days, totaling 7 points), with no unit or the unit being days; Indicates the first The time point or the The original physiological parameter values ​​of each sample (such as heart rate HR or blood oxygen SpO2). The standard deviation represents the range of fluctuation of the parameter around its baseline value.

[0097] When real-time data meets the following conditions, it is determined to be in an abnormal state, and an alert or warning message is sent to the user via the App:

[0098] Where Q is an empirical threshold reflecting monitoring sensitivity, in this embodiment Q is taken as 2 to 3 (approximately equal to the 95% to 99% confidence interval of a normal distribution), and can be adjusted according to individual user characteristics (age, medical history, etc.). If the real-time parameter deviates from the individual baseline by more than Q times the standard deviation, an abnormal alarm is triggered. This represents the standard deviation of the data.

[0099] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A non-invasive method for detecting heart rate and blood oxygen based on a mobile phone case, characterized in that, Includes the following steps (1) Collect red light spectral signal data and near-infrared light spectral signal data; (2) Evaluate whether the data collected in step (1) based on the signal quality assessment mechanism is used in the calculation of heart rate and blood oxygen; (3) Blood oxygen saturation was detected based on the ratio method.

2. The non-contact heart rate and blood oxygen detection method based on a mobile phone case according to claim 1, characterized in that, In step (1), the detection is automatically started when the following conditions are met: in express The light intensity signal received at any time, Minimum detection threshold for optical signal amplitude; The duration for which the condition is met continuously. This represents the minimum duration for which signal stability is achieved. This represents the light intensity signal received at time t. Represents the energy of the exchange component. This represents the minimum acceptable threshold for the AC component energy of the SPP signal.

3. The non-intrusive heart rate and blood oxygen detection method based on a mobile phone case according to claim 1, characterized in that, In step (2), a signal quality index is introduced. Determine whether the detected data is used in heart rate and blood oxygen calculations: in: Signal-to-noise ratio (SNR) is the ratio of useful pulse signal energy to noise energy in a PPG signal. The morphological correlation coefficient between adjacent cardiac cycles represents the morphological consistency of continuous pulse waveforms, and its value ranges from 0 to 1. The standard deviation of the pulse interval represents the stability of the heart rate. , , These are the weighting coefficients for the three parameter terms; when When this data segment is used, it is discarded and not included in the calculation of heart rate and blood oxygen. This is the lowest acceptable threshold for the signal quality index.

4. The non-contact heart rate and blood oxygen detection method based on a mobile phone case according to claim 1, characterized in that, In step (3), the blood oxygen saturation is measured, and the specific steps are as follows: The first step is to perform blood flow-modal bandpass filtering on the sampling signals of the red light channel and the near-infrared light channel respectively, and then extract the AC and DC components of each channel to obtain the AC / DC ratio of the red light channel optical signal and the AC / DC ratio of the near-infrared light channel optical signal. The second step is to calculate the AC / DC ratio of red light and near-infrared light. The third step is to calculate blood oxygen saturation based on the ratio by using a linear fitting relationship.

5. A system for implementing the non-contact heart rate and blood oxygen detection method based on a mobile phone case as described in claim 1, characterized in that, include: The optical emitting unit is used to emit light signals of a certain wavelength to the skin tissue of the finger being tested, providing a light source for the acquisition of PPG signals by photoplethysmography. The emitted light signals are received by the optical receiving unit after being reflected by the finger tissue. An optical receiving unit is used to receive light signals reflected or transmitted through human tissue and convert the received light signals into electrical signals for output to the analog front-end circuit. The analog front-end circuit (AFE) is used to amplify, filter, and perform analog-to-digital conversion on the weak electrical signal output from the optical receiving unit, and output the digitized PPG signal to the microcontroller unit. The microcontroller unit (MCU) is used to sample and control the digital PPG signal output by the analog front-end circuit (AFE), perform basic digital signal processing, and manage the communication process of the Bluetooth Low Energy (BLE) communication module. The Bluetooth Low Energy (BLE) communication module is used to send the detection results of heart rate and blood oxygen in the form of data frames to the mobile phone under the control of the microcontroller unit (MCU) via the BLE protocol, and at the same time receive configuration commands from the mobile phone. An independent power supply unit and a power management unit are used to provide independent power to each unit and control the system to enter a sleep state to reduce power consumption when there is no effective detection.

6. The system according to claim 5, characterized in that, The microcontroller unit (MCU) sends blood pressure sensor data frames to the mobile phone via Bluetooth Low Energy (BLE) communication module. Each uploaded data frame includes the unique device identifier of the heart rate and blood oxygen detection module, a timestamp, heart rate value, blood oxygen saturation, and signal quality index.

7. The system according to claim 5, characterized in that, The Bluetooth Low Energy (BLE) communication module employs the following power control strategy for communication: (1) When the detection window on the back or side of the phone case comes into contact with a finger but the signal quality is not up to standard, the Bluetooth Low Energy (BLE) communication module enters the low-power broadcast mode. (2) When there is no effective detection, i.e. when the finger leaves the detection area, the Bluetooth Low Energy (BLE) communication module enters sleep mode and the MCU switches to deep sleep. (3) After each valid detection is completed, the Bluetooth Low Energy (BLE) communication module sends the detection results to the mobile phone in batches in an event-driven manner, and returns to the sleep state immediately after sending. (4) By dynamically adjusting the BLE broadcast interval of the Bluetooth Low Energy communication module, the optimal balance between detection frequency and power consumption is achieved.

8. The system according to claim 7, characterized in that, The Bluetooth Low Energy (BLE) broadcast interval of the Bluetooth Low Energy communication module can be dynamically adjusted using the following formula: in This represents the average power consumption over a complete duty cycle. This indicates the power consumption of the Bluetooth Low Energy (BLE) communication module when it is in transmit mode. This indicates the cumulative time the Bluetooth Low Energy (BLE) communication module has been in the transmitting state. This indicates the power consumption when the Bluetooth Low Energy (BLE) communication module or MCU enters sleep mode; This indicates the cumulative time the system has been in a sleep state; This represents the total time of a complete work cycle.

9. A computer device comprising a memory and a processor, the memory being electrically connected to the processor, the memory storing a computer program, characterized in that: When the computer program is executed by the processor, it causes the processor to implement the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor implements the method as described in any one of claims 1 to 8.

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