Medical data acquisition method and system based on wireless wristwatch

Abstract

The invention relates to the technical field of medical data acquisition, and relates to a medical data acquisition method and system based on a wireless wristwatch, and the method comprises the steps: enabling the wireless wristwatch to be worn on a wrist, and enabling the wireless wristwatch to be worn tightly; physiological information of a user is detected through a pose detection unit so as to determine the wearing state of the wristwatch; acquiring physiological information of a user by using a data acquisition module of the wireless wristwatch; preprocessing the physiological information and extracting features; communicating with a cloud computing module based on a wireless communication module, and sending the feature vector to the cloud computing module; calculating by using the feature vector to obtain the current emotional state of the user, and evaluating the mental health of the user based on the emotional state data of a period of time; according to the method, the accuracy of the medical original data needing to be collected is improved by judging the mode that the user wears the wristwatch; and subsequent processing and calculation are carried out based on the high-confidence data, so that the accuracy of the calculation result is further improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical data collection, and more specifically, to a medical data collection method and system based on a wireless wristwatch. BACKGROUND

[0002] An intelligent wristwatch is a wearable device that combines the functions of a smart phone and a wristwatch, with core functions such as positioning and anti-lost, audio and video calls, and various functions such as electronic payment, health monitoring, social chatting, photographing, and intelligent object recognition.

[0003] With the improvement of people's living standards, more and more people begin to pay attention to health problems, including not only physical health but also mental health. For people who are in a high-pressure working environment for a long time, mental problems are often more serious than physical problems. Therefore, timely attention to mental health has become a trend.

[0004] In the prior art, the behavior data of a user can be detected in real time by wearing an intelligent wristwatch. Based on historical data analysis, the physiological and mental health data of the user can be obtained. Specifically, the photoplethysmography (PPG) technology is a non-invasive physiological parameter detection technology based on optical principles. The core of the technology is to measure the blood volume change to obtain the dynamic information of the cardiovascular system. PPG uses a light source (such as an LED) to irradiate the skin, and detects the intensity change of the transmitted or reflected light through a photoelectric sensor. Since hemoglobin in the blood has an absorption effect on light of a certain wavelength, and the periodic change in blood volume caused by heartbeats will change the amount of light absorption, eventually forming a PPG signal containing AC (pulsatile component) and DC (static component). Based on this technology, the intelligent wristwatch can measure the physiological data of the human body more accurately.

[0005] Most wearable devices currently use photoplethysmography to carry optical sensors for heart rate detection. By capturing the PPG signal, the heart rate variability value (HRV) is extracted from the PPG signal to realize HRV detection. By calculating the HRV parameters in the frequency domain, the tension of the sympathetic and vagus nerves, the balance of the two, and the influence on cardiovascular activity of an individual can be evaluated non-invasively. The sympathetic and vagus nerves are usually related to emotions and stress, such as emotional anxiety or excessive tension or excitement. Long-term stress can make the sympathetic and vagus nerves more active, so monitoring HRV can also detect changes in the stress and emotion of the subject.

[0006] Therefore, obtaining accurate data is the first step to improve the detection accuracy of the data. However, most wearable devices are worn on the wrist. Due to the inaccuracy of the wearing posture, the tightness, and the variability of the radial artery itself, there are many inaccuracies in measuring medical physiological data by wearing a wristwatch.

[0007] Therefore, how to adjust the correct wearing posture of the watch to collect accurate medical data has become an urgent technical problem to be solved. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention is proposed; this invention provides a medical data acquisition method and system based on a wireless wristwatch.

[0009] According to one aspect of the present invention, a method for medical data acquisition based on a wireless wristwatch is provided, comprising the following specific steps: S1: Wear the wireless watch on your wrist and make sure it fits snugly; S2: The posture detection unit detects the user's physiological information to determine the wearing status of the watch and prompts the user to adjust it to the specified position and appropriate tightness; S3: Collects the user's physiological information using the data acquisition module built into the wireless watch; S4: Preprocess physiological information and extract features; S5: Based on the wireless communication module, communicate with the cloud computing module and send feature vectors to the cloud computing module; S6: Use feature vectors to calculate and obtain the user's current emotional state, and assess the user's mental health based on emotional state data over a period of time; the assessment results are output to the display module of the wireless wristwatch.

[0010] Preferably, in step S1, the wristwatch is worn on the designated position on the wrist and secured according to the prompts from the wristwatch display module.

[0011] Preferably, in step S2, the posture detection unit is activated; first, the pressure sensor detects whether the watch is worn tightly, then the optical sensor detects whether the watch is close to a blood vessel; then, the wrist is shaken, and the six-axis sensor detects the movement characteristics of the watch as it follows the hand's movement, to help determine the wearing status of the watch.

[0012] Preferably, in step S3, the data acquisition module built into the wristwatch collects heart rate and blood oxygen data; Among them, the heart rate sensor uses optical principles to be placed close to blood vessels to detect the heart's beating frequency; Blood oxygen saturation is measured by utilizing the blood's ability to absorb light. Real-time physiological information is determined by detecting and recording the user's heart rate and blood oxygen data.

[0013] Preferably, in step S4, the collected raw data is first filtered, denoised, and signal enhanced; then, the data collected by different sensors are spatiotemporally calibrated, and interpolation is used to align the data from sensors with different sampling rates; finally, feature extraction is performed, and the extracted features are represented in vector form.

[0014] Preferably, in step S5, the feature extraction is performed locally and then sent to the cloud computing module; the emotion state data is obtained based on the cloud computing module to reduce the calculation frequency and power consumption of the local end.

[0015] Preferably, in step S6, the proportion of each type of emotion in the total monitoring time in a certain period is calculated to determine whether the user has a mental health problem, and a corresponding prompt is given on the display module according to the severity of the mental health problem.

[0016] The second aspect of the present application provides a wireless wristwatch-based medical data acquisition system using the above method for acquisition, including an interactive display module, a data acquisition module, a communication module and a calculation processing module integrated on the wristwatch. The interactive display module is used to display the state of the wristwatch and the recorded physiological data, and to display the emotion state data of the user and the prompt for the mental health problem. The data acquisition module is used to acquire the physiological data of the user. The communication module is used for intercommunication with the cloud computing module. The calculation processing module is used to pre-process and extract features of the data acquired by the data acquisition module on the local end.

[0017] Preferably, it further includes a pose detection unit. The pose detection unit uses part of the sensors in the data acquisition module to obtain information related to the pose, and after processing, it assists in determining whether the wristwatch is worn in place and the tightness of the wearing.

[0018] The third aspect of the present application provides a computer device including a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above wireless wristwatch-based medical data acquisition method when executing the computer program.

[0019] Compared with the prior art, the accuracy of the medical raw data to be acquired is improved by determining the way the user wears the wristwatch; the accuracy of the calculation result is further improved by subsequent processing and calculation based on high-confidence data, which is beneficial to better reflect the physiological and psychological health data of the user. At the same time, a distributed calculation method is adopted, only simple preprocessing and feature extraction are performed on the local end to reduce power consumption and improve the endurance of the wearable device; complex calculations are performed by the cloud computing module, and the local end and the cloud computing module only communicate information; the feature data and the result information are sent first, which also reduces the requirements of the local end on the calculation module, and a lower-power MCU can be selected to meet the actual needs of the wearable device. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and the other drawings can be obtained by those skilled in the art without any creative effort based on these drawings. In the drawings: Figure 1 The flow chart of the wireless wristwatch-based medical data acquisition method according to the embodiment of the present application.

[0021] Figure 2 The system block diagram of the wireless wristwatch-based medical data acquisition system according to the embodiment of the present application.

[0022] Figure 3 The structural schematic diagram of the computer system of the electronic device suitable for implementing the embodiment of the present application is shown. DETAILED DESCRIPTION

[0023] In the following, the example embodiments according to the present application will be described in detail with reference to the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.

[0024] As described in the above background, most of the wearable devices are worn on the wrist, and due to the inaccurate wearing posture, the insufficient tightness, and the variability of the radial artery itself, there is a big problem of inaccuracy in measuring medical physiological data by wearing a wristwatch. A brand new technical solution is proposed again to help users to independently adjust the wearing manner of the wristwatch and to collect accurate medical information.

[0025] Embodiment 1

[0026] As shown in the above Figure 1 The embodiment provides a wireless wristwatch-based medical data acquisition method, which comprises the following specific steps: S1: wearing the wireless wristwatch on the wrist and wearing it tightly; then wearing the wristwatch at the specified position of the wrist according to the prompt of the wristwatch display module and wearing it tightly.

[0027] Firstly, during the wearing process of the wristwatch, the user can be prompted in advance through the display module of the wristwatch; for example, the wristwatch needs to be worn at a position at least one finger distance away from the carpal bone, and the corresponding diagram and text are prompted in the display module; the user can perform the next step after wearing it for the first time, and the pose is confirmed by the pose detection unit to determine whether it is worn properly.

[0028] S2: detecting the physiological information of the user by the posture detection unit to determine the wearing state of the wristwatch and prompting the user to adjust to the specified position and appropriate tightness; starting the posture detection unit; first detecting whether the wristwatch is worn tightly by the pressure sensor, and then detecting whether the wristwatch is close to the blood vessel by the optical sensor; then shaking the wrist to detect the motion characteristics of the wristwatch when following the hand shaking by the six-axis sensor to assist in determining the wearing state of the wristwatch.

[0029] In the embodiment, the posture detection unit detects the physiological information of the user by using part of the sensors of the data acquisition module to determine whether the user wears the wristwatch in the appropriate position and appropriate tightness; thereby improving the accuracy of subsequent data acquisition; Specifically: first detecting the pressure between the wristwatch and the skin by the pressure sensor on the back of the wristwatch; determining the pressure threshold of the current wristwatch by experiment to set the maximum and minimum values; below the minimum value, it is determined that the wristwatch is worn too loosely, and the corresponding text and pattern are displayed on the display module to prompt the user to wear the wristwatch tightly; above the maximum value, it is determined that the wristwatch is worn too tightly, and the corresponding text and pattern are displayed on the display module to prompt the user to wear the wristwatch loosely, so that the wristwatch is in the appropriate interval; for example, the minimum value is set to 10 kPa, and the maximum value is set to 30 kPa; if the pressure value is lower than 10 kPa, it means that the wristwatch is worn too loosely, and the sensor does not contact the skin well; if the pressure value is higher than 30 kPa, it means that the wristwatch is worn too tightly, affecting the normal work of the sensor and blood circulation; prompting the user to adjust the wristwatch to the appropriate tightness.

[0030] Then detecting the blood pulse at this time by the optical sensor; since the hemoglobin in the blood has the absorption effect on the light of a specific wavelength, and the periodic change of blood volume caused by the heart beat will change the amount of light absorption; therefore, the data of the wristwatch during detection is obtained by experiment to determine the threshold value; if the intensity of the reflected light detected is within the threshold value, it is determined that the sensor is close to the blood vessel; then processing the detected periodic signal to analyze whether its frequency is consistent with the heart rate frequency, if consistent, it is determined that the sensor is close to the blood vessel.

[0031] Specifically, experimental data analysis determined that the intensity of reflected light changes between 0.1 and 0.5V when using green light for detection. Therefore, when the optical sensor detects that the actual reflected light intensity of the user is within this range, it can be preliminarily determined that the sensor is close to the blood vessel. If the detected actual reflected light intensity is not within this range, text and graphic prompts are displayed on the display module, reminding the user to adjust the position of the watch left or right and test again. When it is preliminarily determined that the sensor is close to the blood vessel, the collected periodic reflected light intensity data is analyzed in the frequency domain using algorithms such as Fast Fourier Transform (FFT) to calculate the main frequency components of the signal. The normal human heart rate frequency is usually between 0.6 and 3 Hz. Therefore, if the detected frequency components are mainly concentrated within this range, it can be confirmed that the sensor is close to the blood vessel and the wearing position is appropriate. If the frequency is not within this range, it may be that the user is not wearing the watch on their wrist, in which case text and graphic prompts are displayed on the display module.

[0032] Finally, a shaking test was conducted on the watch. The display module provided prompts in the form of text and images: first, extend your arm and remain stationary in the horizontal direction, then shake your wrist according to a specific gesture. If the watch fits properly and the sensor is close to a blood vessel, the Z-axis acceleration should be close to gravity when stationary in the horizontal direction; while the acceleration on the X and Y axes should be 0. Under these conditions, during subsequent movement, the acceleration change should be consistent with the arm movement, with acceleration in each axis not exceeding ±2g (gravitational acceleration). Simultaneously, during movement, the reflected light intensity should remain within the threshold range, and the calculated frequency should conform to the normal human heart rate range, slightly increasing compared to the stationary state.

[0033] Based on the above three prerequisites, it can be determined that the position and tightness of the watch worn by the user are appropriate; at this point, the accuracy of the collected data is relatively high.

[0034] S3: Use the data acquisition module built into the wireless watch to collect the user's physiological information; in step S3, the data acquisition module built into the watch collects heart rate and blood oxygen data; Among them, the heart rate sensor uses optical principles to be placed close to blood vessels to detect the heart's beating frequency; Blood oxygen saturation is measured by utilizing the blood's ability to absorb light. Real-time physiological information is determined by detecting and recording the user's heart rate and blood oxygen data.

[0035] In this embodiment, when collecting the user's physiological information, such as heart rate and blood oxygen, the PPG algorithm commonly used in the prior art is used for measurement; since this part has been disclosed in many articles and patents, this embodiment will not describe this part in detail.

[0036] S4: Preprocess physiological information and extract features; In step S4, the collected raw data is first filtered, denoised and signal enhanced; Spatiotemporal calibration is performed on the data collected by different sensors, and interpolation is used to align the data of sensors with different sampling rates; Then feature extraction is performed, and the extracted features are represented in the form of vectors.

[0037] In this embodiment, accelerometer data is combined with the acquired raw PPG data for processing to eliminate motion artifacts. Specifically, the PPG signal is bandpass filtered from 0.5 to 5 Hz to retain the frequency band relevant to heartbeats. Accelerometer data is used as a reference signal, and normalized least mean square (NLMS) adaptive filtering is employed to eliminate motion artifacts.

[0038] Then, multi-sensor synchronization was performed. The heart rate (128Hz), blood oxygen (1Hz), and accelerometer (50Hz) data were aligned using linear interpolation. The frequency of the aligned data was then uniformly set to 50Hz.

[0039] Next, feature extraction is performed based on the preprocessed PPG signal; Temporal characteristics: a. Rise Time (RT): The time from the trough to the peak; typically 0.1-0.3 seconds. b. Systolic area / diastolic area ratio (S / D ratio): reflects vascular elasticity; its value is 1.2-1.8. c.SDNN (Standard deviation of all RR intervals):

[0040] In the above formula: the RR interval refers to the time interval between two adjacent heartbeats in a pulse wave signal (such as photoplethysmography, PPG), representing the time difference between two consecutive R waves; RRi identifies the i-th RR interval value; is the average of all RR intervals, and N is the total number of RR intervals.

[0041] Frequency domain characteristics: Power spectral density (PSD): Frequency band energy decomposed by FFT. LF (0.04-0.15Hz): Sympathetic nerve activity; HF (0.15-0.4Hz): Parasympathetic nerve activity; LF / HF ratio: a pressure level indicator; normal range is 0.5-2.0. Nonlinear characteristics: SpO2 fluctuation index: Calculates the standard deviation of blood oxygen saturation within 10 minutes (normal <2%). Finally, a 3-dimensional feature vector was established: F F = [SDNN, LF / HF, SpO2] S5: Based on the communication between the wireless communication module and the cloud computing module, the feature vector is sent to the cloud computing module; in step S5, the feature is extracted locally and then sent to the cloud computing module; the emotional state data is obtained by calculation processing based on the cloud computing module, so as to reduce the calculation frequency and power consumption of the local end.

[0042] In this embodiment, after the feature vector is sent to the cloud computing module, the computation process begins. The trained algorithm model is pre-written into the cloud computing module. Specifically, a hybrid model of support vector machine (SVM) + random forest is used. The training dataset is labeled data from a laboratory-induced stress task with a sample size of ≥1000. The performance metrics of the model are set as follows: accuracy ≥90% and AUC ≥0.95.

[0043] By manually labeling data of different emotional states in advance to obtain a training set, and by training and iterating the model to make it converge, the user's emotional state is evaluated by inputting a feature vector and outputting a stress index.

[0044] For example, emotional states can be categorized into four types: irritable, excited, calm, and stressed. The specific emotional state is determined based on the stress index value output by the model.

[0045] 1. Anger Sympathetic nerve activity: Significant enhancement: Triggers the "fight or flight" response, releasing adrenaline and noradrenaline.

[0046] Physiological manifestations: increased heart rate (>100 BPM), elevated blood pressure, muscle tension, and dilated pupils.

[0047] Parasympathetic activity: Severely suppressed: Digestive function slows down, saliva secretion decreases, and the body's resources are concentrated on dealing with threats.

[0048] Typical indicators: HRV (Heart Rate Variability): Low-frequency (LF) power increases, high-frequency (HF) power decreases, and the LF / HF ratio increases significantly.

[0049] 2. Excitement Sympathetic nerve activity: Moderately enhanced: Associated with positive emotions (such as pleasure and anticipation), with a lower activation level than the irritable state.

[0050] Physiological manifestations: slightly increased heart rate (80-100 BPM), accelerated breathing, and increased dopamine secretion.

[0051] Parasympathetic activity: Partial inhibition: It still retains a certain level of activity to maintain basic physiological functions, such as normal digestion.

[0052] Typical indicators: HRV: LF power increases, HF power decreases slightly, and the LF / HF ratio is slightly higher than the baseline.

[0053] 3. Calm Sympathetic nerve activity: Significantly reduced: In "rest and digestion" mode, energy consumption is reduced.

[0054] Physiological manifestations: stable heart rate (60-80 BPM), normal blood pressure, and deep and slow breathing.

[0055] Parasympathetic activity: Highly active: Promotes digestion, repair, and immune system function.

[0056] Typical indicators: HRV: HF power is significantly increased (reflecting parasympathetic activity), and the LF / HF ratio is close to 1 or lower.

[0057] Vagal tone: When the parasympathetic nervous system is dominant, vagal activity is enhanced.

[0058] 4. High Pressure (Chronic Stress / High Pressure) Sympathetic nerve activity: Sustained hyperactivity: Long-term stress leads to overactivation of the sympathetic nervous system, resulting in sustained activity of the HPA axis (hypothalamic-pituitary-adrenal axis).

[0059] Physiological manifestations: elevated resting heart rate (>90 BPM), sleep disturbances, and decreased immunity.

[0060] Parasympathetic activity: Long-term inhibition: Vagal tone decreases, and the body cannot recover effectively.

[0061] Typical indicators: HRV: LF power remains high, HF power decreases significantly, and the LF / HF ratio is much higher than the normal value (>3).

[0062] The specific value of the stress index output by the model is determined by analyzing the training data. For example, a stress index below 20 is considered excitement; a stress index between 20 and 60 is considered calm; a stress index between 61 and 85 is considered high stress; and a stress index above 85 is considered irritability.

[0063] S6: Use feature vectors to calculate the user's current emotional state, and assess the user's mental health based on emotional state data over a period of time; the assessment results are output to the display module of the wireless wristwatch. In step S6, by calculating the proportion of each type of emotion to the total monitoring time in a certain period, it is determined whether the user has mental health problems, and corresponding prompts are given on the display module according to the severity of mental health issues.

[0064] In this embodiment, four emotional states can be categorized into three types: positive, negative, and calm. Irritability and high stress correspond to the negative type; excitement corresponds to the positive type, and calmness corresponds to the calm type. The three types are distinguished by consulting materials and other methods. Under the premise of wearing the watch in a suitable position, data that meets the requirements over a period of time is collected from the user and divided into calendar days. The proportion of each type in the total time spent each day is calculated to determine the emotional type of the day. The proportion of days with the negative type is calculated to comprehensively determine whether the user has mental health problems.

[0065] In this embodiment, the accuracy of the raw medical data to be collected is improved by first determining whether the user is wearing a wristwatch; subsequent processing and calculation based on high-confidence data further improve the accuracy of the calculation results, which helps to better reflect the user's physiological and mental health data.

[0066] The following is a specific case study to illustrate this solution: A user wore the watch following steps S1-S2 of Example 1 and wore it continuously for one week without ever removing it. PPG data was collected and preprocessed according to steps S3-S4 to obtain the feature vector F. The feature vector F was then sent to the cloud computing module for computation. The computation result at a certain time T is as follows: SDNN=45ms, LF / HF=0.8, SpO2 fluctuation index=1.2%. Based on these results, the stress index at time T is calculated to be 27, indicating a calm state. A comprehensive analysis of the week's data reveals that four days were identified as high-pressure states, one as angry states, one as calm states, and one as excited states. This suggests a risk of the user being in a prolonged high-pressure state. Therefore, a display module should prompt the user to engage in stress-relief exercises to prevent physiological and psychological health problems caused by prolonged high pressure.

[0067] Example 2 like Figure 2As shown, this embodiment provides a medical data acquisition system based on a wireless wristwatch, which uses the method in Embodiment 1 to acquire medical data, including an interactive display module, a data acquisition module, a communication module, and a computing processing module integrated on the wristwatch; The interactive display module is used to display the watch's status and recorded physiological data, as well as the user's emotional state data and prompts for mental health issues; The data acquisition module is used to collect users' physiological data; The communication module is used for interconnection and communication with the cloud computing module; The computation and processing module is used to preprocess and extract features from the data collected by the data acquisition module on the local end.

[0068] In this embodiment, a pose detection unit is also included; The posture detection unit uses some sensors in the data acquisition module to acquire posture-related information, and after processing, it helps to determine whether the watch is worn correctly and how tight it is.

[0069] Example 3 Figure 3 A schematic diagram of a computer system suitable for implementing embodiments of the present invention is shown.

[0070] It should be noted that the computer system of the electronic device in this embodiment is only an example and should not impose any limitations on the function and scope of use of the embodiments of the present invention.

[0071] In this embodiment, the computer system includes a central processing unit 401, which can perform various appropriate actions and processes based on a program stored in the read-only memory 402 or a program loaded from the storage section 408 into the random access memory 403, such as executing the medical data acquisition method and system based on a wireless wristwatch described in the above embodiment. The random access memory 403 also stores various programs and data required for system operation. The central processing unit 401, the read-only memory 402, and the random access memory 403 are interconnected via a bus 404. An input / output interface 405 is also connected to the bus 404.

[0072] The following components are connected to the input / output interface 405: an input section 406 including a keyboard, mouse, etc.; an output section 407 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the input / output interface 405 as needed. A removable medium 411, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 410 as needed so that computer programs read from it can be installed into the storage section 408 as needed.

[0073] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing computer programs for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 409, and / or installed from removable medium 411. When the computer program is executed by central processing unit 401, it performs various functions defined in the system of the present invention.

[0074] It should be noted that the computer-readable medium shown in the embodiments of the present invention can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In the present invention, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. The transmitted data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0076] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0077] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0078] In another aspect, the present invention also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the medical data acquisition method and system based on a wireless wristwatch as described in the above embodiments.

[0079] It should be noted that although several modules or units of the device for performing actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0080] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, portable hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of the present invention.

[0081] Here, those skilled in the art will understand that the specific operations of each step in the above-described method for medical data acquisition based on a wireless wristwatch have been referenced above. Figures 1 to 2 The description of the wireless wristwatch-based medical data acquisition system is detailed therein, and therefore, its repeated description will be omitted.

[0082] In summary, the medical data acquisition system based on a wireless wristwatch, as described in this invention, improves the accuracy of the raw medical data to be collected by determining how the user wears the wristwatch; further processing and calculation based on high-confidence data improves the accuracy of the calculation results, which is beneficial for better reflecting the user's physiological and psychological health data.

Claims

1. A method for acquiring medical data based on a wireless wristwatch, characterized in that, The specific steps include the following: S1: Wear the wireless watch on your wrist and make sure it fits snugly; S2: The posture detection unit detects the user's physiological information to determine the wearing status of the watch and prompts the user to adjust it to the specified position and appropriate tightness; S3: Collects the user's physiological information using the data acquisition module built into the wireless watch; S4: Preprocess physiological information and extract features; S5: Based on the wireless communication module, communicate with the cloud computing module and send feature vectors to the cloud computing module; S6: Use feature vectors to calculate and obtain the user's current emotional state, and assess the user's mental health based on emotional state data over a period of time; the assessment results are output to the display module of the wireless wristwatch.

2. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S1, according to the prompts on the watch display module, put the watch on the designated position on the wrist and tighten it.

3. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S2, the posture detection unit is activated; first, the pressure sensor detects whether the watch is worn tightly, then the optical sensor detects whether the watch is close to the blood vessels; then, the wrist is shaken, and the six-axis sensor detects the movement characteristics of the watch as it follows the hand's movement, to help determine the wearing status of the watch.

4. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S3, the watch's built-in data acquisition module collects heart rate and blood oxygen data; Among them, the heart rate sensor uses optical principles to be placed close to blood vessels to detect the heart's beating frequency; Blood oxygen saturation is measured by utilizing the blood's ability to absorb light. Real-time physiological information is determined by detecting and recording the user's heart rate and blood oxygen data.

5. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S4, the collected raw data is first filtered, denoised, and signal enhanced; then, spatiotemporal calibration is performed on the data collected by different sensors, and interpolation is used to align the data from sensors with different sampling rates. Then feature extraction is performed, and the extracted features are represented in the form of vectors.

6. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S5, feature extraction is performed locally and then sent to the cloud computing module; the emotional state data is obtained by computational processing based on the cloud computing module, thereby reducing the computational frequency and power consumption on the local end.

7. The medical data acquisition method based on a wireless wristwatch according to claim 1, characterized in that, In step S6, the proportion of each type of emotion in the total monitoring time is calculated to determine whether the user has mental health problems, and corresponding prompts are given on the display module according to the severity of mental health.

8. A medical data acquisition system based on a wireless wristwatch, comprising acquiring data using the method described in any one of claims 1-7, characterized in that, This includes an interactive display module, a data acquisition module, a communication module, and a computing and processing module integrated into the wristwatch; The interactive display module is used to display the watch's status and recorded physiological data, as well as the user's emotional state data and prompts for mental health issues; The data acquisition module is used to collect users' physiological data; The communication module is used for interconnection and communication with the cloud computing module; The computation and processing module is used to preprocess and extract features from the data collected by the data acquisition module on the local end.

9. The medical data acquisition system based on a wireless wristwatch according to claim 8, characterized in that, It also includes a pose detection unit; The posture detection unit uses some sensors in the data acquisition module to acquire posture-related information, and after processing, it helps to determine whether the watch is worn correctly and how tight it is.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the medical data acquisition method based on a wireless wristwatch as described in any one of claims 1-7.

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