A physiological signal acquisition method and device, a wearable device, and a storage medium

Abstract

The application discloses a physiological signal collection method and device, a wearable device and a storage medium, which can dynamically adjust the physiological signal collection mode, and significantly improve the balance between data quality and device energy efficiency. The application comprises: monitoring the motion state of a user; if the motion state of the user meets a preset static condition, and the current time is within a preset night time period, then the first type of physiological signal and the second type of physiological signal of the user are collected in a continuous sampling mode, the first type of physiological signal is a sensing signal for monitoring cardiovascular function, and the second type of physiological signal is a sensing signal for calculating core body temperature; if the motion state of the user does not meet the preset static condition, and / or the current time is not within the preset night time period, then the first type of physiological signal and the second type of physiological signal of the user are collected in a periodic intermittent sampling mode.

Description

Technical Field

[0001] This application relates to the field of wearable device technology, and in particular to a method, apparatus, wearable device and storage medium for acquiring physiological signals. Background Technology

[0002] With the increasing demand for health monitoring and the continuous miniaturization of sensor technology, wearable devices, especially smartwatches, smart bracelets, and smart rings, are gradually evolving from simple activity tracking tools into personal health management tools that integrate continuous monitoring of physiological parameters. Achieving long-term, continuous monitoring of vital signs such as heart rate, blood oxygen saturation, and core body temperature on these devices has significant reference value for scenarios such as health early warning, chronic disease management, and exercise recovery assessment.

[0003] However, achieving the aforementioned continuous monitoring function in miniaturized wearable devices faces a challenge: the conflict between the device's limited battery capacity and the high power consumption resulting from continuous monitoring. Miniaturized wearable devices have very small battery capacities. Powered by such batteries, if the wearable device is required to continuously sample signals such as PPG and body temperature around the clock, its average operating current will far exceed the battery's capacity. This leads to a significant reduction in the wearable device's battery life, severely impacting user experience and monitoring performance. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method, apparatus, wearable device, and storage medium for acquiring physiological signals.

[0005] The technical solution provided in this application is described below:

[0006] The first aspect of this application provides a method for acquiring physiological signals, the method being applied to a wearable device, the method comprising: Monitor the user's movement status; If the user's movement state meets the preset static condition and the current time is within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a continuous sampling manner. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. If the user's movement state does not meet the preset static condition, and / or the current time is not within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a periodic intermittent sampling manner.

[0007] Optionally, monitoring the user's motion status includes: The user's motion status is monitored using an accelerometer; After monitoring the user's motion state via the accelerometer, the method further includes: Acquire the acceleration changes of the accelerometer during the monitoring process; Within a continuous first preset time period, it is determined whether there is an acceleration change exceeding a preset threshold. If not, it is determined that the user's motion state meets the preset static condition.

[0008] Optionally, the step of continuously sampling the user's first and second type of physiological signals includes: During a preset time period of continuous sampling, photoplethysmography (PPG) signals are continuously acquired at a first sampling frequency. The PPG signals are used to monitor cardiovascular function. During a preset time period of continuous sampling, skin temperature signals and skin heat flux signals are continuously acquired at a second sampling frequency. The skin temperature signals and skin heat flux signals are used to calculate core body temperature, wherein the first frequency is higher than the second frequency.

[0009] Optionally, after continuously acquiring the photoplethysmography (PPG) signal at the first sampling frequency, the method further includes: The photoplethysmography signal is buffered to obtain buffered data; When the cached data meets the preset trigger conditions, a wake-up signal is generated, and the microcontroller is controlled to read the cached data in batches according to the wake-up signal.

[0010] Optionally, the acquisition of the user's first and second type of physiological signals using a periodic intermittent sampling method includes: Determine a first fixed period for collecting the first type of physiological signals and a second fixed period for collecting the second type of physiological signals; Within a preset time period of intermittent sampling, at each time the first fixed cycle is reached, a photoplethysmography (PPG) signal of a second preset duration is collected, and the PPG signal is used to monitor cardiovascular function. During a preset time period of intermittent sampling, skin temperature signal and skin heat flux signal are collected each time the second fixed cycle is reached. The skin temperature signal and the skin heat flux signal are used to calculate core body temperature.

[0011] Optionally, after acquiring the skin temperature signal and skin heat flux signal, the method further includes: Calculate core body temperature according to the first target formula; The first target formula is: CBT = T_skin + R_thermal×HeatFlux, where CBT is the core body temperature, T_skin is the skin temperature represented by the skin temperature signal, HeatFlux is the skin heat flux represented by the skin heat flux signal, and R_thermal is the equivalent thermal resistance.

[0012] Optionally, the method further includes: Continuously collect the user's historical physiological and behavioral data; A model of personal physiological baseline parameters and personal daily routine patterns is established based on the historical physiological data and behavioral data. Based on the individual's physiological baseline parameters and personal daily routine model, adjust at least one of the preset static conditions' judgment thresholds or preset parameters.

[0013] A second aspect of this application provides a physiological signal acquisition device, the device comprising: The monitoring unit is used to monitor the user's movement status; The first execution unit is used to continuously sample the user's first type of physiological signal and second type of physiological signal if the user's movement state meets the preset static condition and the current time is within the preset nighttime period. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. The second execution unit is used to collect the user's first type of physiological signal and second type of physiological signal in a periodic intermittent sampling manner if the user's motion state does not meet the preset static condition and / or the current time is not within the preset nighttime period.

[0014] A third aspect of this application provides a wearable device, the device comprising: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to execute the first aspect and any one of the optional methods in the first aspect.

[0015] A fourth aspect of this application provides a computer-readable storage medium on which a program is stored, which, when executed on a computer, performs the methods of the first aspect and any one of the first aspects.

[0016] As can be seen from the above technical solutions, this application has the following beneficial effects: When the user is at rest and during nighttime, continuous sampling is used to collect cardiovascular function and core body temperature signals, capturing key physiological changes during sleep (such as nocturnal blood pressure fluctuations and body temperature rhythms), providing high-value continuous data for health assessment. During exercise or non-nighttime periods, periodic intermittent sampling is switched to effectively reduce power consumption and extend the wearable device's usage time. By dynamically adjusting the physiological signal acquisition mode, a significant balance between data quality and device energy efficiency is achieved. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of an embodiment of the physiological signal acquisition method of this application; Figure 2 This is a schematic diagram of another embodiment of the physiological signal acquisition method of this application; Figure 3 This is a schematic diagram of another embodiment of the physiological signal acquisition method of this application; Figure 4 This is a schematic diagram of another embodiment of the physiological signal acquisition method of this application; Figure 5 This is a schematic diagram of another embodiment of the physiological signal acquisition method of this application; Figure 6 This is a schematic diagram of another embodiment of the physiological signal acquisition method of this application; Figure 7 This is a schematic diagram of one embodiment of the physiological signal acquisition device of this application; Figure 8 This is a schematic diagram of one embodiment of the wearable device of this application. Detailed Implementation

[0019] Achieving continuous monitoring in miniaturized wearable devices presents a challenge: balancing the limited battery capacity with the high power consumption resulting from continuous monitoring. Miniaturized wearable devices have very small battery capacities. Powered by such batteries, if the wearable device is required to continuously sample signals such as PPG and body temperature around the clock, its average operating current will far exceed the battery's capacity. This significantly shortens the device's battery life, severely impacting user experience and monitoring performance.

[0020] Based on this, this application provides a method, apparatus, wearable device, and storage medium for acquiring physiological signals. When the user is at rest and it is nighttime, continuous sampling is used to acquire cardiovascular function and core body temperature signals, capturing key physiological changes during sleep (such as nocturnal blood pressure fluctuations and body temperature rhythms), providing high-value continuous data for health assessment. During exercise or non-nighttime periods, periodic intermittent sampling is switched, effectively reducing power consumption and extending the wearable device's usage time. By dynamically adjusting the physiological signal acquisition mode, a significant balance between data quality and device energy efficiency is achieved.

[0021] Please see Figure 1 This application provides a method for acquiring physiological signals, the method comprising: S101. Monitor the user's movement status; S102. If the user's movement state meets the preset static condition and the current time is within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a continuous sampling manner. The first type of physiological signal is the sensor signal for monitoring cardiovascular function, and the second type of physiological signal is the sensor signal for calculating core body temperature. S103. If the user's motion state does not meet the preset static condition, and / or the current time is not within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a periodic intermittent sampling manner.

[0022] In this embodiment, the user's movement state is first monitored. If the user's movement state meets the preset static condition and the current time is within the preset nighttime period, the user's first type of physiological signal and second type of physiological signal are collected in a continuous sampling manner. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. If the user's movement state does not meet the preset static condition, and / or the current time is not within the preset nighttime period, the user's first type of physiological signal and second type of physiological signal are collected in a periodic intermittent sampling manner.

[0023] In step S101, the user's motion state is first monitored. Specifically, the wearable device continuously collects the user's three-axis acceleration sensing signals through the built-in accelerometer, and the microcontroller performs real-time amplitude analysis and steady-state detection on the collected three-axis acceleration signals. The microcontroller continuously accumulates the user's continuous static duration in a fixed time window and updates it in real time to determine the user's current stable motion state information.

[0024] In step S102, after obtaining the user's movement state, if the user's movement state meets the preset static condition and the current time is within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a continuous sampling manner. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature.

[0025] Specifically, the device has a preset fixed nighttime window of 8 hours. The microcontroller retrieves the current time information and compares it with the preset nighttime window. At the same time, it retrieves the user's continuous static duration data and determines whether the user's continuous static duration has reached a preset switching threshold of 30 minutes. If the current time falls within the preset nighttime window and the user's continuous static duration reaches the preset threshold of 30 minutes, then the user's first and second physiological signals are collected in a continuous sampling manner.

[0026] Upon receiving the mode switching command, the microcontroller immediately sends continuous sampling control signals to each sensor, activating the continuous working mode of the sensors. The first type of physiological signal is the sensor signal for monitoring cardiovascular function, specifically the PPG (Photoplethysmography) signal. The microcontroller controls the photoelectric sensor analog front end to maintain a continuous power supply, driving the light source to emit light stably and continuously receive the PPG signal. The PPG signal is continuously acquired and transmitted at a fixed sampling frequency. The second type of physiological signal is the sensor signal used to calculate core body temperature, specifically including skin temperature and heat flux signals. The microcontroller controls the temperature sensor and heat flux sensor to maintain a powered-on working state, continuously acquiring skin temperature data and heat flux data at a fixed sampling frequency. Both types of physiological signals are uploaded to the microcontroller in batches for parsing and processing via FIFO (First In, First Out) buffering and DMA (Direct Memory Access) transmission. Continuous sampling is maintained throughout the process without interruption, ensuring that the physiological signal data during the nighttime sleep stage is complete and without loss.

[0027] In step S103, after obtaining the user's motion state, if the user's motion state does not meet the preset static condition, and / or the current time is not within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a periodic intermittent sampling manner. Specifically, the microcontroller maintains low-power control logic in the daytime monitoring mode. According to the preset intermittent sampling period and sampling duration, it controls the sensors corresponding to the first and second types of physiological signals to complete the cycle of power-on acquisition and power-off sleep according to the preset intermittent sampling period and sampling duration. For the first type of physiological signal, the microcontroller controls the photoelectric sensor analog front end to power on every 10 minutes, continuously collects PPG signals for 30 seconds, and then immediately shuts off the sensor power supply to reduce the average LED current to less than 1 / 10 of the normal operating current. For the second type of physiological signal, the microcontroller controls the temperature sensor and heat flux sensor to power on briefly every 5 minutes to collect data, and then power off to sleep after the data is collected. The periodic intermittent start-stop operation mode of the sensors can significantly reduce the average power consumption during the day. While meeting the basic physiological monitoring needs during the day, it adapts to the low power consumption constraints of ultra-small capacity batteries, thereby effectively improving the battery life of small capacity batteries.

[0028] Therefore, when the user is at rest and during nighttime, continuous sampling of cardiovascular function and core body temperature signals can capture key physiological changes during sleep (such as nocturnal blood pressure fluctuations and body temperature rhythms), providing high-value continuous data for health assessment. During exercise or non-nighttime periods, switching to periodic intermittent sampling effectively reduces power consumption and extends the wearable device's lifespan. By dynamically adjusting the physiological signal acquisition mode, a significant balance between data quality and device energy efficiency is achieved.

[0029] Please refer to Figure 2 According to some embodiments of this application, after step S101: monitoring the user's motion state through the accelerometer, the specific actions may include, but are not limited to, the following: S201. Acquire the acceleration changes of the accelerometer during the monitoring process; S202. Within a continuous first preset time period, determine whether there is an acceleration change exceeding a preset threshold. If not, determine that the user's motion state meets the preset static condition.

[0030] In this embodiment, the user's acceleration changes are monitored by an accelerometer. It should be noted that the device uses a low-power triaxial accelerometer, model MC3672. This sensor is controlled by a microcontroller through a timer and interrupt mechanism to continuously collect raw X, Y, and Z axis acceleration signals in real time while the user is wearing the device. The microcontroller performs real-time digital filtering and amplitude synthesis processing on the collected raw triaxial acceleration signals to remove interference signals caused by environmental vibration and slight device shaking, and extracts effective acceleration change data that can truly reflect the user's limb activity state. The microcontroller continuously acquires and updates the user's acceleration change information at a fixed sampling interval.

[0031] After collecting user acceleration change data, the system determines whether there is an acceleration change exceeding a preset threshold within a continuous first preset time period. It should be noted that in this embodiment, the continuous first preset time period is set to 30 minutes, and the preset threshold is set to 50mg. The microcontroller uses a sliding time window determination method to compare the effective acceleration change data within the continuous 30-minute time period in real time. It detects whether the acceleration change amplitude at each sampling moment exceeds the preset threshold of 50mg. If the user's acceleration change amplitude does not exceed the preset threshold of 50mg within the entire continuous 30-minute sliding window, that is, no limb activity exceeding the threshold occurs, then the user's current motion state is finally determined to meet the preset static condition.

[0032] Therefore, it can be seen that continuous comparison over a long period of time can ensure the stability of motion state determination, avoid misjudgment caused by brief and slight shaking, and improve the accuracy of mode switching.

[0033] Please refer to Figure 3 According to some embodiments of this application, step S102: collecting the user's first type of physiological signal and second type of physiological signal in a continuous sampling manner, which may specifically include, but is not limited to, the following: S301. During a preset time period of continuous sampling, photoplethysmography (PPG) signals are continuously acquired at a sampling frequency of the first frequency. The PPG signals are used as sensing signals to monitor cardiovascular function. S302. During a preset time period of continuous sampling, skin temperature signal and skin heat flux signal are continuously acquired at a second sampling frequency. The skin temperature signal and skin heat flux signal are used to calculate core body temperature, wherein the first frequency is higher than the second frequency.

[0034] In this embodiment, during the continuous sampling workflow of the nighttime monitoring mode, when the microcontroller triggers the continuous sampling command and enters the preset sampling time period, it first performs the acquisition operation of the photoplethysmography (PPG) signal. This PPG signal serves as the core sensing signal for monitoring the user's cardiovascular function. The microcontroller enables the photoelectric sensor analog front-end through the GPIO interface, configures the sampling parameters of the PPG signal acquisition channel to 25Hz, and maintains continuous power supply to the photoelectric sensor analog front-end throughout the entire preset continuous sampling time period. This drives the light source to emit light stably and continuously receive the PPG signal from the user's fingertip or skin surface. After the acquired PPG signal is amplified, filtered, and conditioned by the analog front-end, it is stored in the hardware FIFO buffer in real time and uploaded to the microcontroller in batches via DMA transmission for parsing. The entire process does not require the microcontroller to continuously poll and read, minimizing processor power consumption and ensuring that the cardiovascular function monitoring signal acquired within the preset time period has continuous, complete, and high-fidelity characteristics.

[0035] During the same preset continuous sampling period, which involves continuous acquisition of photoplethysmography (PPG) signals at 25Hz, the acquisition processes for skin temperature and skin heat flux signals used to calculate core body temperature are simultaneously initiated. The microcontroller controls the power-on of the temperature and heat flux sensors, configuring the sampling frequency of both skin temperature and heat flux signals to 1Hz. It continuously acquires the absolute temperature data of the user's skin surface and the heat flux values ​​between skin layers. The skin temperature data is detected and acquired in real time by a dedicated temperature sensor, while the skin heat flux signal is transmitted to the microcontroller via a dedicated acquisition channel. Both types of signals are acquired and transmitted synchronously. Based on the preset core body temperature calculation model and the calibrated equivalent thermal resistance parameters from skin to core, the microcontroller processes the real-time acquired skin temperature and heat flux data to calculate the user's current core body temperature value. The 1Hz continuous sampling frequency is maintained throughout the preset continuous sampling period to ensure the real-time performance and accuracy of the core body temperature calculation. When the preset continuous sampling period ends, the microcontroller immediately shuts off the power supply to the photoelectric sensor analog front end, temperature sensor, and heat flux sensor, allowing each sensor to quickly enter a low-power sleep state, completing the entire data acquisition process for this continuous sampling.

[0036] Therefore, it can be seen that in the night monitoring mode, PPG signals are continuously acquired at 25Hz and skin temperature and heat flux signals are simultaneously acquired at 1Hz. Through FIFO caching and DMA batch transmission, processor power consumption can be reduced, ensuring the continuous and complete cardiovascular and core body temperature monitoring data. Furthermore, the sensor power supply is promptly turned off after sampling to achieve low-power sleep mode, balancing the accuracy of physiological signal monitoring, data integrity, and device power consumption control.

[0037] Please refer to Figure 4According to some embodiments of this application, after step S301: continuously acquiring photoplethysmography (PPG) signals at a sampling frequency of a first frequency, the process may specifically include, but is not limited to, the following: S401. Buffer the photoplethysmography signal to obtain buffered data; S402. When the cached data meets the preset trigger conditions, a wake-up signal is generated, and the microcontroller is controlled to read the cached data in batches according to the wake-up signal.

[0038] In this embodiment, during the continuous acquisition of photoplethysmography (PPG) signals, signal sampling and data buffering operations are performed. This allows the microcontroller to avoid real-time responses to individual sampled data. When the sampled data in the FIFO buffer reaches a preset data volume or the buffering time reaches a preset threshold, a wake-up signal is sent to the microcontroller to trigger it to exit sleep mode. After being woken up, the microcontroller performs batch reading and transmission of all sampled data in the buffer area, and immediately returns to sleep mode after completing the reading. During the remaining time periods outside of data reading, the microcontroller remains in sleep mode to avoid increased system power consumption caused by frequent single-time reading of sampled data. This significantly reduces the overall power consumption of the device while ensuring the complete acquisition of continuous physiological signals.

[0039] Therefore, by sampling data from the cache and reading it in batches after meeting preset conditions, the number of times the microcontroller is woken up can be greatly reduced. During non-reading periods, the microcontroller remains in sleep mode, effectively reducing system power consumption. At the same time, it ensures that the photoplethysmography (PPG) signal is continuously acquired without loss, thus meeting the low power consumption requirements of wearable devices with ultra-small capacity batteries.

[0040] Please refer to Figure 5 According to some embodiments of this application, step S103: collecting the user's first type of physiological signal and second type of physiological signal in a periodic intermittent sampling manner, which may specifically include, but is not limited to, the following: S501. Determine the first fixed period for collecting the first type of physiological signal and the second fixed period for collecting the second type of physiological signal; S502. During the preset time period of intermittent sampling, at each time the first fixed cycle is reached, a photoplethysmography (PPG) signal of a second preset duration is collected. The PPG signal is used to monitor cardiovascular function. S503. During the preset time period of intermittent sampling, skin temperature signal and skin heat flux signal are collected each time the second fixed cycle is reached. The skin temperature signal and skin heat flux signal are used to calculate the core body temperature.

[0041] In this embodiment, a first fixed period for collecting the first type of physiological signal and a second fixed period for collecting the second type of physiological signal are first determined. The first type of physiological signal is a sensor signal used to monitor cardiovascular function. To balance the effectiveness of daytime cardiovascular status monitoring with the overall power consumption of the device, the microcontroller determines that the first fixed period corresponding to this signal is 10 minutes. The second type of physiological signal is a sensor signal used to calculate core body temperature. Considering the physiological characteristics of the gradual fluctuation of core body temperature during the day and the basic monitoring accuracy requirements, the microcontroller determines that the second fixed period corresponding to this type of signal is 5 minutes. The two fixed period parameters are stored in the microcontroller's dedicated register as the core timing reference for controlling the start and stop of the sensor during the daytime intermittent sampling phase, providing stable timing support for the subsequent accurate execution of intermittent sampling operations.

[0042] After determining the two types of fixed cycles, the device enters a preset time period for intermittent sampling. This preset time period corresponds to the system's preset daytime monitoring period, with a total duration of sixteen hours. During this time period, the microcontroller continuously counts using its internal timer, polling in real time to determine whether the time node of the first fixed cycle has been reached. Each time the first fixed cycle is determined to have been reached, the microcontroller immediately sends a power-on enable command to the photoelectric sensor analog front end via a pin, starting the photoplethysmography (PPG) acquisition module. The PPG signal is continuously acquired for the preset second preset duration. It should be noted that in this embodiment, the second preset duration is set to 30 seconds. During the acquisition process, the photoelectric sensor analog front end maintains a stable working state, and the acquired signals are uploaded to the microcontroller in batches through FIFO buffering and DMA transmission. After the signal acquisition for the second preset duration is completed, the microcontroller immediately shuts off the power supply to the photoelectric sensor analog front end, causing the PPG acquisition module to enter a deep sleep state, reducing the average LED current to less than one-tenth of that in continuous working mode, thus minimizing the power consumption of daytime cardiovascular signal acquisition.

[0043] Within a preset time period of intermittent sampling during the same day, the microcontroller synchronously uses an independent timer to time and poll the second fixed cycle. Each time the second fixed cycle is determined, the microcontroller synchronously activates the temperature sensor and the heat flux sensor. The temperature sensor collects the absolute temperature signal of the user's skin surface in real time, and the heat flux sensor collects the user's skin heat flux signal in real time. After each collection of the two types of signals, the microcontroller immediately cuts off the power supply to the corresponding sensor, switching it to a low-power sleep mode. The collected skin temperature signal and skin heat flux signal are transmitted to the microcontroller's processing unit in real time. Based on the preset core body temperature calculation model and combined with the calibrated equivalent thermal resistance parameters from the skin to the core, the microcontroller calculates the user's true core body temperature data. The core body temperature is calculated according to the first target formula: CBT = T_skin + R_thermal × HeatFlux, where CBT is the core body temperature, T_skin is the skin temperature represented by the signal, HeatFlux is the skin heat flux represented by the skin heat flux signal, and R_thermal is the equivalent thermal resistance. While meeting the basic needs of daytime core body temperature monitoring, power consumption is strictly controlled by periodically starting and stopping the sensor for short periods, which fully meets the low power consumption design goals of wearable devices with ultra-small capacity batteries.

[0044] Therefore, it can be seen that by setting the daytime sampling cycle according to the differences in physiological signal characteristics, collecting PPG signals for 30 seconds every 10 minutes, and collecting temperature and heat flux signals every 5 minutes, using timer independent control, FIFO and DMA batch transmission, and immediately powering off and going into sleep mode after acquisition, the power consumption of the sensor can be significantly reduced. At the same time, the core body temperature is calculated based on the formulas of skin temperature, heat flux and equivalent thermal resistance. While meeting the basic monitoring accuracy of cardiovascular and core body temperature during the day, the power consumption is significantly reduced to meet the low power consumption design requirements of wearable devices with small capacity batteries.

[0045] Please refer to Figure 6 According to some embodiments of this application, this application may further include, but is not limited to, the following: S601. Continuously collect users' historical physiological and behavioral data; S602. Establish a model of personal physiological baseline parameters and personal daily routine based on historical physiological and behavioral data; S603. Adjust at least one judgment threshold or preset parameter in the preset static conditions according to the individual's physiological baseline parameters and the individual's work and rest pattern model.

[0046] In this embodiment, the user's historical physiological and behavioral data are first continuously collected. Specifically, the wearable smart ring, through its built-in microcontroller, accelerometer, photoelectric sensor analog front-end, temperature sensor, and heat flux sensor, continuously collects and stores the user's historical physiological and behavioral data locally during daily operation. The historical physiological data includes continuously collected PPG (photoplethysmography) pulse wave signals, skin temperature signals, heat flux signals, core body temperature data calculated from heat flux and skin temperature, heart rate data, and heart rate variability data. The historical behavioral data includes the user's daily movement status data, continuous static duration data, and activity intensity data at different times, all collected by the accelerometer. The device uses a collection cycle of at least 7 days and stores the above data in the microcontroller's local storage unit in an orderly manner according to timestamps, accumulating and updating the user's personalized historical data in real time, providing sufficient and authentic raw data support for subsequent model building.

[0047] After collecting historical physiological and behavioral data from users, a personal physiological baseline parameter and personal sleep-wake cycle model are established based on the historical physiological and behavioral data. Specifically, the microcontroller performs statistical analysis and fitting calculations on the accumulated historical physiological data, extracting personal physiological baseline parameters such as the daily baseline value of the user's core body temperature, the slope of the core body temperature drop during the sleep stage, the resting heart rate baseline, and the conventional threshold of heart rate variability. At the same time, it performs time-series analysis on historical behavioral data to identify the user's fixed daily rest start time, continuous stable rest duration, and natural sleep time interval, and fits to form a personal sleep-wake cycle model that adapts to the user's own sleep habits, clarifying the user's personalized nighttime rest period characteristics and rest state judgment characteristics.

[0048] It should be noted that the above models and parameters are all generated and processed locally on the device, and are automatically updated according to a preset cycle to adapt to the dynamic changes in the user's physiological state and daily routine.

[0049] After establishing personal physiological baseline parameters and a personal sleep-wake cycle model, the microcontroller incorporates these parameters into the judgment logic for preset resting conditions. This allows for personalized adjustments to the original fixed judgment thresholds and preset parameters. Specifically, the preset fixed nighttime window can be adjusted to a personalized nighttime window that matches the user's sleep-wake cycle. The original 30-minute continuous resting duration threshold can be adjusted to a personalized resting duration threshold that aligns with the user's pre-sleep behavior. Furthermore, the personal core body temperature drop slope baseline can be combined to assist in optimizing the triggering conditions for mode switching. Through these personalized adjustments, the judgment of monitoring mode switching becomes more aligned with the user's physiological characteristics and behavioral habits, effectively improving the accuracy and sensitivity of mode switching. This avoids erroneous switching or switching lag issues caused by fixed parameters, further optimizing device power consumption control and the completeness of physiological data monitoring, and adapting to the low-power and high-precision monitoring needs of wearable devices with ultra-small capacity batteries.

[0050] Therefore, by continuously collecting user physiological and behavioral historical data for no less than 7 days and establishing a personal physiological baseline parameter and personalized work-rest pattern model, lightweight calculations are performed locally on the device and automatically iterated and updated. Based on this model, the fixed time window for mode switching and the threshold for static duration are personalized, and combined with core body temperature changes to assist in judgment, the accuracy and sensitivity of monitoring mode switching can be significantly improved, avoiding false switching and lag. At the same time, power consumption control and data integrity are optimized, adapting to the low power consumption and high precision monitoring needs of wearable devices with small-capacity batteries.

[0051] According to some embodiments of this application, this application may further include, but is not limited to, the following: Before entering daytime or nighttime monitoring mode, calculate the available energy budget for the current cycle; The available energy budget is calculated based on the second objective formula; the second objective formula is: P_budget = (SOC_current - SOC_reserve) × C_bat / T_est, where P_budget is the available energy budget, SOC_current is the current remaining battery percentage, SOC_reserve is the preset safe reserve percentage, C_bat is the battery nominal capacity, and T_est is the estimated time until the next charge based on the user's historical charging behavior. The sampling frequency and sampling duration of wearable devices are dynamically adjusted based on the available energy budget.

[0052] In this embodiment, before configuring and sampling in daytime or nighttime monitoring mode, the device microcontroller, in conjunction with the built-in battery management unit, first calculates the available energy budget for the current cycle. Based on the remaining battery energy, safety power redundancy, and the user's personalized charging habits, it provides quantitative power consumption constraints for the dynamic allocation of subsequent sensor sampling parameters. This avoids the problem of premature power failure and interruption of key physiological data acquisition due to disordered energy consumption of ultra-small capacity batteries, ensuring that the device can stably complete the monitoring task throughout the entire usage cycle.

[0053] The device then calculates the value according to the preset second target formula, which is P_budget = (SOC_current - SOC_reserve) × C_bat / T_est. Here, P_budget is the available energy budget that the device can use in the current cycle, which is the maximum average power consumption threshold allowed in this period. SOC_current is the current battery remaining percentage obtained by the battery management unit in real time after analog-to-digital conversion. This parameter is updated in real time with the battery power consumption status to reflect the battery's remaining energy level. SOC_reserve is the device's preset safety reserve percentage, which is used to reserve basic power consumption to prevent battery damage from over-discharge and ensure the operation of the minimum necessary functions such as the device clock and accelerometer wake-up. C_bat is the nominal capacity of the ultra-small capacity battery carried by the wearable device. It should be noted that in this embodiment, the nominal capacity of the battery does not exceed 15mAh, and the typical application capacity is 10mAh. T_est is the estimated time until the next charge, which is predicted by the microcontroller after constructing a user's work and rest and charging pattern model through a personalized learning algorithm based on the user's historical charging behavior data of no less than 7 days. The microcontroller simultaneously substitutes the above parameters into the second objective formula to complete the calculation and outputs the available energy budget result for the current cycle.

[0054] After obtaining the available energy budget value, the microcontroller uses this budget as the upper limit constraint for power consumption. Combined with the basic sampling strategies of daytime monitoring mode and nighttime monitoring mode, it dynamically adjusts the sampling frequency and sampling duration of each physiological sensor in the wearable device. The adjustment process follows the principle of prioritizing health monitoring value, giving priority to the acquisition of PPG signals used for cardiovascular function monitoring, and temperature and heat flux signals used to calculate core body temperature.

[0055] When the available energy budget meets the power consumption requirements of the standard mode, the standard configuration is maintained: PPG sampling at 25Hz and core body temperature sampling at 1Hz in night mode, and PPG sampling for 30 seconds every 10 minutes and core body temperature sampling for 5 minutes in day mode.

[0056] When the available energy budget is lower than the standard power consumption requirement, it is adjusted in a stepwise manner. In daytime mode, the intermittent sampling period of the sensor is extended and the duration of a single sampling is shortened to further reduce the sensor's duty cycle. In nighttime mode, while ensuring the integrity of sleep monitoring and core body temperature data, the power consumption ratio of non-critical sensors is optimized to always keep the actual operating power consumption of the device within the available energy budget. Under the energy constraint of ultra-small capacity batteries, the effectiveness of physiological monitoring and the device's battery life are balanced.

[0057] Please see Figure 7 This application also provides a physiological signal acquisition device, including: Monitoring unit 701 is used to monitor the user's movement status; The first execution unit 702 is used to continuously sample the user's first type of physiological signal and second type of physiological signal if the user's movement state meets the preset static condition and the current time is within the preset nighttime period. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. The second execution unit 703 is used to collect the user's first type of physiological signal and second type of physiological signal in a periodic intermittent sampling manner if the user's motion state does not meet the preset static condition and / or the current time is not within the preset nighttime period.

[0058] In this embodiment, the functions of each unit are the same as those described above. Figures 1 to 6 The steps in the method embodiments shown correspond to those in the examples, and will not be repeated here.

[0059] Please see Figure 8 This application also provides a wearable device, which includes: Processor 801, memory 802, input / output unit 803, bus 804; The processor 801 is connected to the memory 802, the input / output unit 803, and the bus 804; The memory 802 stores a program, and the processor 801 calls the program to execute any of the methods described above.

[0060] This application also relates to a computer-readable storage medium on which a program is stored, which, when run on a computer, causes the computer to perform any of the methods described above.

[0061] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0063] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0064] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0065] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for acquiring physiological signals, characterized in that, The method is applied to wearable devices, and the method includes: Monitor the user's movement status; If the user's movement state meets the preset static condition and the current time is within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a continuous sampling manner. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. If the user's movement state does not meet the preset static condition, and / or the current time is not within the preset nighttime period, then the user's first type of physiological signal and second type of physiological signal are collected in a periodic intermittent sampling manner.

2. The method according to claim 1, characterized in that, The monitoring of the user's movement status includes: The user's motion status is monitored using an accelerometer; After monitoring the user's motion state via the accelerometer, the method further includes: Acquire the acceleration changes of the accelerometer during the monitoring process; Within a continuous first preset time period, it is determined whether there is an acceleration change exceeding a preset threshold. If not, it is determined that the user's motion state meets the preset static condition.

3. The method according to claim 1, characterized in that, The method of continuously sampling the user's first and second type of physiological signals includes: During a preset time period of continuous sampling, photoplethysmography (PPG) signals are continuously acquired at a first sampling frequency. The PPG signals are used to monitor cardiovascular function. During a preset time period of continuous sampling, skin temperature signals and skin heat flux signals are continuously acquired at a second sampling frequency. The skin temperature signals and skin heat flux signals are used to calculate core body temperature, wherein the first frequency is higher than the second frequency.

4. The method according to claim 3, characterized in that, After continuously acquiring the photoplethysmography (PPG) signal at the first sampling frequency, the method further includes: The photoplethysmography signal is buffered to obtain buffered data; When the cached data meets the preset trigger conditions, a wake-up signal is generated, and the microcontroller is controlled to read the cached data in batches according to the wake-up signal.

5. The method according to claim 1, characterized in that, The method of collecting the user's first and second type of physiological signals using a periodic intermittent sampling method includes: Determine a first fixed period for collecting the first type of physiological signals and a second fixed period for collecting the second type of physiological signals; Within a preset time period of intermittent sampling, at each time the first fixed cycle is reached, a photoplethysmography (PPG) signal of a second preset duration is collected, and the PPG signal is used to monitor cardiovascular function. During a preset time period of intermittent sampling, skin temperature signal and skin heat flux signal are collected each time the second fixed cycle is reached. The skin temperature signal and the skin heat flux signal are used to calculate core body temperature.

6. The method according to claim 5, characterized in that, After acquiring the skin temperature signal and skin heat flux signal, the method further includes: Calculate core body temperature according to the first target formula; The first target formula is: CBT = T_skin + R_thermal×HeatFlux, where CBT is the core body temperature, T_skin is the skin temperature represented by the skin temperature signal, HeatFlux is the skin heat flux represented by the skin heat flux signal, and R_thermal is the equivalent thermal resistance.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Continuously collect the user's historical physiological and behavioral data; A model of personal physiological baseline parameters and personal daily routine patterns is established based on the historical physiological data and behavioral data. Based on the individual's physiological baseline parameters and personal daily routine model, adjust at least one of the preset static conditions' judgment thresholds or preset parameters.

8. A physiological signal acquisition device, characterized in that, include: The monitoring unit is used to monitor the user's movement status; The first execution unit is used to continuously sample the user's first type of physiological signal and second type of physiological signal if the user's movement state meets the preset static condition and the current time is within the preset nighttime period. The first type of physiological signal is a sensor signal for monitoring cardiovascular function, and the second type of physiological signal is a sensor signal for calculating core body temperature. The second execution unit is used to collect the user's first type of physiological signal and second type of physiological signal in a periodic intermittent sampling manner if the user's motion state does not meet the preset static condition and / or the current time is not within the preset nighttime period.

9. A wearable device, characterized in that, The device includes: Processor, memory, input / output units, and bus; The processor is connected to the memory, the input / output unit, and the bus; The memory stores a program, which the processor invokes to perform the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium contains a program that, when executed on a computer, performs the method as described in any one of claims 1 to 7.

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