Detection method for wearing tightness of wearable device, wearable device and detection system

Abstract

The invention provides a method for detecting the wearing tightness of a wearable device, the wearable device and a detection system. The wearing tightness detection method of the wearable device is used for detecting the wearing tightness of the wearable device, the wearable device comprises an air pump and an air bag, the air pump is connected with the air bag, and the detection method comprises the steps of controlling the air pump to inflate the air bag, recording a PPG signal in the inflation process, obtaining a tightness quantitative index based on the recorded PPG signal, and determining the wearing tightness of the wearable device according to the tightness quantitative index. The tightness quantitative index indicates the wearing tightness of the wearable device. Through the integrated technology of PPG signal measurement and air pump regulation and control, the method for detecting the wearing tightness of the wearable device is provided, a user can know the current wearing tightness according to the finally obtained tightness quantitative index, and the user is guided to adjust the proper wearing tightness through the tightness quantitative index, so that the user experience is improved. The fitting degree and the comfort degree of the wearable device are ensured, the problem of inaccurate data caused by improper wearing is solved, and the credibility of monitoring data is improved.

Description

Technical Field

[0001] This application relates to the field of wearable device technology, and in particular to a method, wearable device and detection system for detecting the tightness of a wearable device. Background Technology

[0002] With the rapid development of technology and people's increasing awareness of health management, wearable devices have become an indispensable part of daily life. From basic heart rate monitoring to complex physiological parameter analysis, these devices play a vital role in improving the accuracy and convenience of personal health monitoring. However, the tightness of wearable devices directly affects the accuracy of measurement data, which has become a significant factor limiting the effectiveness of their application. Wearing a device that is too tight or too loose can lead to measurement data deviations, which not only reduces the reliability of the data but may also mislead users' judgments about their own health, thus affecting their health management decisions. Furthermore, discomfort from wearing wearable devices directly affects users' willingness to use them long-term. Wearing a device that is too tight may cause local pressure, while wearing a device that is too loose may cause it to slip off frequently. Both wearing a device that is too tight and too loose reduce its practicality and user satisfaction. Summary of the Invention

[0003] This application provides a method, a wearable device, and a detection system for detecting the tightness of a wearable device, which can solve at least some of the above-mentioned technical problems.

[0004] In a first aspect, this application provides a method for detecting the tightness of a wearable device, the wearable device including an air pump and an airbag, the air pump being connected to the airbag, the detection method comprising:

[0005] Control the air pump to inflate the airbag;

[0006] Record PPG signals during inflation;

[0007] A tightness quantification index is derived based on the recorded PPG signal, which indicates the tightness of the wearable device.

[0008] Secondly, this application provides a wearable device for monitoring and analyzing a user's physiological characteristic data, including:

[0009] air pump;

[0010] An airbag, wherein the air pump is connected to the airbag;

[0011] Controller;

[0012] PPG sensor;

[0013] The controller is used to control the air pump to inflate the airbag, the PPG sensor is used to detect the PPG signal during the inflation process, the controller is used to acquire the PPG signal detected by the PPG sensor during the inflation process and record the PPG signal during the inflation process, and derive a tightness measurement index based on the recorded PPG signal, the tightness measurement index indicating the wearing tightness of the wearable device.

[0014] Thirdly, this application provides a wearable device, including: a processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to perform the steps of the above-described method for detecting the tightness of the wearable device.

[0015] Fourthly, this application provides a system for detecting the tightness of a wearable device, comprising:

[0016] Such as the wearable devices mentioned above.

[0017] Fifthly, this application provides a computer-readable storage medium storing a computer program, which, when called by a processor, executes the steps of the above-described method for detecting the tightness of a wearable device.

[0018] This application provides a method, a wearable device, and a detection system for detecting the wearing tightness of a wearable device. The method for detecting the wearing tightness of the wearable device includes an air pump and an airbag. The air pump is connected to the airbag. The detection method includes controlling the air pump to inflate the airbag, recording the PPG signal during the inflation process, and deriving a quantified tightness index based on the recorded PPG signal. The quantified tightness index indicates the wearing tightness of the wearable device. The wearable device is used to monitor and analyze the user's physiological characteristic data. The wearable device includes an air pump, an airbag, a controller, and a PPG sensor. The air pump is connected to the airbag. The controller controls the air pump to inflate the airbag. The PPG sensor detects the PPG signal during the inflation process. The controller acquires and records the PPG signal detected by the PPG sensor during the inflation process and derives a quantified tightness index based on the recorded PPG signal. The quantified tightness index indicates the wearing tightness of the wearable device. This application provides a method for detecting the wearing tightness of a wearable device by integrating PPG signal measurement with air pump control technology. Users can determine the current wearing tightness based on the final obtained tightness quantification index. This index guides users to adjust the wearing tightness appropriately, ensuring the fit and comfort of the wearable device. It also solves the problem of inaccurate data caused by improper wearing, improves the reliability of monitoring data, avoids misleading users' judgment of their own health, enables users to make correct health management decisions, and improves the practicality of the wearable device and user satisfaction. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the 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.

[0020] Figure 1 This is a flowchart of a method for detecting the tightness of a wearable device in one embodiment of this application.

[0021] Figure 2 This is a waveform diagram of the PPG signal during the airbag inflation process in one embodiment of this application.

[0022] Figure 3 As shown in one embodiment of this application Figure 1 A further sub-flowchart for step S3.

[0023] Figure 4As shown in one embodiment of this application Figure 3 A further sub-flowchart for step S31.

[0024] Figure 5 As shown in one embodiment of this application Figure 4 A further sub-flowchart for step S312.

[0025] Figure 6 As shown in one embodiment of this application Figure 5 A further sub-flowchart for step S43.

[0026] Figure 7 This is a trend line of baseline drift and a waveform diagram of pulse AC signal in one embodiment of this application.

[0027] Figure 8 As shown in one embodiment of this application Figure 5 A further sub-flowchart for step S42.

[0028] Figure 9 As shown in one embodiment of this application Figure 6 A further sub-flowchart of step S431.

[0029] Figure 10 In another embodiment of this application Figure 1 A further sub-flowchart for step S3.

[0030] Figure 11 In another embodiment of this application Figure 10 A further sub-flowchart for step S63.

[0031] Figure 12 The image shows the baseline drift trend line and pulse AC signal waveform of the PPG signal under three different wearing tightness conditions in one embodiment of this application.

[0032] Figure 13 This is a schematic block diagram of a wearable device in one embodiment of this application.

[0033] Figure 14 This is a further schematic block diagram of a wearable device according to an embodiment of this application.

[0034] Figure 15 This is a schematic block diagram of a wearable device according to another embodiment of this application.

[0035] Figure 16 This is a schematic block diagram of a system for detecting the tightness of a wearable device according to an embodiment of this application.

[0036] Figure 17 This is a further schematic block diagram of a system for detecting the tightness of a wearable device according to an embodiment of this application.

[0037] Icon labels:

[0038] Wearable device-100; Air pump-1; Airbag-2; Controller-3; PPG sensor-4; Display unit-5;

[0039] Memory - 200; Processor - 300; Wearable device tightness detection system - 400; Cloud service platform - 500. Detailed Implementation

[0040] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0041] In the description of the embodiments of this application, it should be understood that the terms "upper," "lower," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. The term "connection" in this application, unless otherwise specified, primarily refers to a physical structural connection; however, if specified, it may also include direct or indirect connections. The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive inclusion.

[0042] Please see Figure 1 , Figure 1 This is a flowchart of a method for detecting the wearing tightness of a wearable device according to an embodiment of this application. The method for detecting the wearing tightness of the wearable device is used to detect the wearing tightness of the wearable device. The wearable device includes an air pump and an airbag, the air pump being connected to the airbag. The detection method includes:

[0043] S1: Control the air pump to inflate the airbag;

[0044] S2: Records the PPG signal during the inflation process;

[0045] S3: A tightness quantification index is derived based on the recorded PPG signal, which indicates the tightness of the wearable device.

[0046] Therefore, by integrating PPG signal measurement with air pump control technology to detect the tightness of the wearable device, the user can determine the current tightness of the wearable device based on the final obtained tightness quantification index. This quantification index guides the user to adjust the tightness appropriately, ensuring the fit and comfort of the wearable device. It also solves the problem of inaccurate data caused by improper wearing, improves the reliability of monitoring data, avoids misleading users' judgment of their own health, enables users to make correct health management decisions, and improves the practicality of the wearable device and user satisfaction.

[0047] Specifically, when a user wears the wearable device on a part of their body, the air pump inside the device inflates the airbag. During this process, the airbag gradually inflates from a deflated state. The PPG (Photoplethysmography) signal changes accordingly, and this PPG signal is recorded. Based on the recorded PPG signal, a tightness quantification index is derived. The user can clearly understand the tightness of the wearable device using this index and adjust it accordingly to dynamically control the contact between the wearable device and the skin. After the air pump inflates the airbag for a certain period, the airbag deflates to return to its deflated state.

[0048] Therefore, as mentioned above, this invention effectively solves the problem of data measurement errors caused by improper wearing of the wearable device, improves the reliability and continuity of data monitoring, and greatly enhances the convenience and effectiveness of personal health management. Furthermore, with the improvement of monitoring data accuracy and the optimization of user experience, the wearable device will further penetrate into multiple fields such as telemedicine, occupational health monitoring, and elderly care, thus becoming a key component of personalized health management solutions. Moreover, this invention not only solves the problem of the wearability of the device affecting the accuracy of measurement data, but also promotes the iterative upgrading of the wearable device technology, thereby improving the user experience and laying a solid foundation for achieving more efficient and personalized health management goals.

[0049] It is understood that the method for detecting the tightness of wearable devices is applicable to various types of wearable devices, including but not limited to health monitoring bracelets, smartwatches, medical-grade monitoring devices, etc., which are not limited here.

[0050] Please see Figure 2 , Figure 2 This is a waveform diagram of the PPG signal during the airbag inflation process in one embodiment of this application. Figure 2 As shown, Figure 2 The horizontal axis represents time in seconds, and the vertical axis represents the PPG signal reading. From... Figure 2 It can be seen that the air pump continuously inflated the airbag for 3 seconds and recorded the PPG signal during the 3-second inflation process. At this time, the recorded PPG signal is a continuous PPG signal in the time domain.

[0051] Please see Figure 3 , Figure 3 As shown in one embodiment of this application Figure 1 A further sub-flowchart of step S3. The process of deriving a quantifiable index of tightness based on the recorded PPG signal includes:

[0052] S31: Filter the recorded PPG signal to obtain the baseline drift trend line and pulse AC signal in the PPG signal;

[0053] S32: Analyze the amplitude of the baseline drift trend line changing over time, and analyze the energy intensity of the pulse communication signal;

[0054] S33: Based on the energy intensity of the obtained pulse communication signal and the amplitude of the trend line of the baseline drift changing over time, calculate the parameters corresponding to the tightness of the clothing.

[0055] S34: Calculate the tightness measurement index based on the parameters corresponding to the tightness state of the garment.

[0056] Therefore, by using a filtering algorithm to separate the pulse AC signal and the baseline drift trend line in the PPG signal, the energy intensity of the pulse AC signal and the amplitude of the baseline drift trend line changing over time are accurately calculated. This not only improves the accuracy of signal processing but also ensures the validity of the parameters corresponding to the wearing tightness. Furthermore, based on these parameters, the wearing tightness is converted into a quantifiable tightness metric, providing users with an intuitive basis for adjustment and improving the convenience and scientific nature of using the wearable device.

[0057] Specifically, in conjunction with the above, the PPG signal during the airbag inflation process is recorded, and the recorded PPG signal is filtered to separate the baseline drift trend line and the pulse exchange signal through filtering technology. Based on the amplitude of the baseline drift trend line changing over time and the energy intensity of the pulse exchange signal, a parameter corresponding to the wearing tightness is calculated using a preset formula. At this time, this parameter only corresponds to the wearing tightness, but it cannot quantify the wearing tightness of the wearable device. That is to say, this parameter cannot intuitively display the wearing tightness of the wearable device. Therefore, it is necessary to calculate the tightness quantification index through this parameter so as to intuitively quantify the wearing tightness of the wearable device.

[0058] It is understood that the quantification index of tightness can be, but is not limited to, numerical values, grades, etc., as long as it can clearly and intuitively quantify the tightness of the wearable device. There is no limitation here.

[0059] Please see Figure 4 , Figure 4 As shown in one embodiment of this application Figure 3 A further sub-flowchart of step S31. The filtering of the recorded PPG signal to derive the baseline drift trend line and pulse-alarm signal in the PPG signal includes:

[0060] S311: Sample the recorded PPG signal to obtain a discrete PPG signal sequence based on the continuous PPG signal;

[0061] S312: Perform median filtering on the sampled discrete PPG signal sequence to obtain the baseline drift trend line and pulse AC signal in the PPG signal.

[0062] Therefore, by using median filtering to separate the pulse AC signal and the baseline drift trend line in the PPG signal, the energy intensity of the pulse AC signal and the change amplitude of the trend line can be accurately calculated. This not only improves the accuracy of signal processing, but also ensures the effectiveness of the parameters and tightness measurement indicators corresponding to the wearing tightness.

[0063] In some embodiments, the recorded continuous PPG signal is sampled at a sampling frequency of 100Hz, and a discrete PPG signal sequence is obtained based on the continuous PPG signal. The discrete PPG signal sequence includes 300 sampling points, wherein the discrete PPG signal sequence can be represented as PPG[n], where n represents the (n+1)th sampling point in the PPG signal sequence. n takes any value from 0 to 299, so that PPG[n] represents the reading value of the PPG signal corresponding to one of the 300 sampling points included in the discrete PPG signal sequence.

[0064] It is understood that the sampling frequency may be, but is not limited to, 100Hz, or may be 150Hz, 300Hz, etc., and is not limited here.

[0065] Please see Figure 5 , Figure 5 As shown in one embodiment of this application Figure 4 A further sub-flowchart of step S312. The step of performing median filtering on the sampled discrete PPG signal sequence to derive the baseline drift trend line and pulse AC signal in the PPG signal includes:

[0066] S41: Determine the width of the median filtering window, wherein the width of the filtering window is related to the number of sampling points covered by the filtering window;

[0067] S42: Process the sampled discrete PPG signal sequence based on the width of the filtering window;

[0068] S43: The processed discrete PPG signal sequence is subjected to median filtering through the filtering window to obtain the baseline drift trend line and pulse AC signal in the PPG signal.

[0069] Therefore, by using median filtering to separate the pulse AC signal and the baseline drift trend line from the PPG signal, the accuracy of signal processing is improved, and the effectiveness of the parameters corresponding to the tightness of the garment and the quantification index of tightness is also ensured.

[0070] Please see Figure 6 , Figure 6 As shown in one embodiment of this application Figure 5 A further sub-flowchart of step S43. The step of performing median filtering on the processed discrete PPG signal sequence through the filtering window to derive the baseline drift trend line and pulse-alternating current signal in the PPG signal includes:

[0071] S431: Based on the width of the filtering window, select multiple subsequences from the discrete PPG signal sequence obtained after processing, wherein the number of the multiple subsequences is equal to the number of values ​​in the discrete PPG signal sequence obtained after sampling, and the number of values ​​included in each subsequence corresponds to the width of the filtering window.

[0072] S432: Sort the multiple values ​​in each of the multiple subsequences, and select the median value from the multiple values ​​in each sorted subsequence to obtain multiple median values ​​corresponding to the multiple subsequences respectively;

[0073] S433: Obtain the trend line of baseline drift based on the multiple median values ​​corresponding to the multiple subsequences respectively;

[0074] S434: Calculate the pulse AC signal based on the acquired baseline drift trend line and the sampled discrete PPG signal sequence.

[0075] Thus, the trend line of the baseline drift is obtained by median filtering, and the pulse AC signal is calculated based on the trend line of the baseline drift and the discrete PPG signal sequence, thereby improving the accuracy of signal processing.

[0076] In some embodiments, the period of the pulse exchange signal is 0.68 seconds, and when the sampling frequency is 100 Hz, the pulse exchange signal in one period includes 68 sampling points.

[0077] Specifically, based on the above, the width of the median filtering window is determined to be 75% of the period of the PPG signal. Therefore, 68 × 0.75 indicates that the filtering window includes 51 sampling points, meaning the width (data width) of the filtering window corresponds to 51 values. This means that at any given time point, the median filter considers the values ​​corresponding to the first 25 sampling points and the last 25 sampling points of a selected sampling point, and together with the value corresponding to the sampling point itself, forms a subsequence containing 51 values. The values ​​in the obtained subsequence are sorted in ascending or descending order, and the median value is found, which is the 26th value in the subsequence. This process is repeated for each sampling point in the PPG signal sequence to obtain multiple median values ​​corresponding to multiple subsequences. Based on the obtained median values, a baseline drift trend line is obtained, and the PPG signal is calculated based on the obtained baseline drift trend line and the discrete PPG signal sequence obtained after sampling.

[0078] It is understood that the filter window can also be 70% or 80% of the period, but the width of the filter window must be less than the width of one period of the pulse AC signal.

[0079] In some embodiments, the formula for calculating the pulse AC signal sequence is as follows:

[0080] Pulse[n] = PPG[n] - Baseline[n]

[0081] Wherein, Pulse[n] is a discrete pulse AC signal sequence, PPG[n] is a discrete PPG signal sequence, and Baseline[n] is a discrete baseline drift trendline sequence, where n = 0-299. Finally, the 300 sampling points contained in the obtained pulse AC signal sequence and baseline drift trendline sequence are connected to obtain the waveform diagram of the pulse AC signal and the baseline drift trendline.

[0082] As mentioned earlier, the baseline in the trend line of the baseline drift is the DC component signal in the PPG signal, and the pulse AC signal is the AC component signal obtained by separating the DC signal from the PPG signal.

[0083] Please see Figure 7 , Figure 7This is a trend line of baseline drift and a waveform diagram of the pulse AC signal in one embodiment of this application. Specifically, it can be seen from... Figure 7 The image clearly shows the pulse-alcohol signal and the baseline drift trend line obtained after the PPG signal during the 3-second inflation process is filtered by the median as described above. In fact, the calculation process in this application uses the discrete pulse-alcohol signal sequence and the discrete baseline drift trend line sequence mentioned above; the only difference is that the plotting software connects multiple sampling points when generating the final result, thus obtaining... Figure 7 A trend line of continuous baseline drift and a waveform diagram of continuous pulse AC signal.

[0084] The determination of the width of the filtering window for median filtering includes:

[0085] The width of the filtering window is determined based on the period of the pulse exchange signal, wherein the width of the filtering window is less than the width of one period of the pulse exchange signal.

[0086] Therefore, the width of the filtering window is smaller than the width of one cycle of the pulse AC signal, which can retain the energy of the pulse AC signal to a greater extent, thereby improving the accuracy of the obtained baseline drift trend line and the pulse AC signal.

[0087] Please see Figure 8 , Figure 8 As shown in one embodiment of this application Figure 5 A further sub-flowchart of step S42. The number of values ​​corresponding to the width of the filtering window is odd. The processing of the sampled discrete PPG signal sequence based on the width of the filtering window includes:

[0088] S421: Determine the expansion number based on the width of the filter window, wherein the expansion number is (n-1) / 2, where n is the number of values ​​corresponding to the width of the filter window;

[0089] S422: Add a first set of data before the first value of the sampled discrete PPG signal sequence, and add a second set of data after the last value of the sampled discrete PPG signal sequence, wherein the first set of data is the last extended number of values ​​of the sampled discrete PPG signal sequence, and the second set of data is the first extended number of values ​​of the sampled discrete PPG signal sequence.

[0090] Thus, the discrete PPG signal sequence is extended to ensure that the width of the filtering window can completely cover the entire PPG signal sequence, so that the filtering window can also perform filtering at the boundaries of the PPG signal sequence.

[0091] Specifically, since the beginning and end portions of the discrete PPG signal sequence cannot completely form a window including 51 sampling points, edge processing is required for the discrete PPG signal sequence to ensure continuous and smooth filtering of the entire PPG signal sequence.

[0092] It is understood that the filter window can also be 70% or 80% of the period, but it must be ensured that the width of the filter window is less than the width of one period of the pulse AC signal, and the number of values ​​corresponding to the width of the filter window is odd, which is not limited here.

[0093] Please see Figure 9 , Figure 9 As shown in one embodiment of this application Figure 6 A further sub-flowchart of step S431. The selection of multiple sub-sequences from the processed discrete PPG signal sequence based on the width of the filtering window includes:

[0094] S51: Determine the preceding and following values ​​of each value in the discrete PPG signal sequence obtained after processing, wherein the number of preceding and following values ​​of each value is equal to (n-1) / 2.

[0095] S52: Determine each value, the value of its preceding part, and the value of its following part as the subsequence corresponding to the value, thereby obtaining the multiple subsequences.

[0096] Thus, by expanding the PPG signal sequence, multiple subsequences are obtained for median filtering, enabling the filtering window to perform filtering at the boundaries of the PPG signal sequence, thereby improving the accuracy of signal processing.

[0097] Please see Figure 10 , Figure 10 In another embodiment of this application Figure 1 A further sub-flowchart of step S3. The energy intensity of the pulse AC signal includes one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal; the analysis of the energy intensity of the pulse AC signal includes:

[0098] S61: Filter the recorded PPG signal to obtain the baseline drift trend line and pulse AC signal in the PPG signal;

[0099] S62: Analyze the magnitude of the change in the trend line of the baseline drift over time;

[0100] S63: Calculate one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal, so as to represent the energy intensity of the pulse AC signal through one of the power spectral density, harmonic amplitude, or root mean square value.

[0101] S64: Based on the energy intensity of the obtained pulse communication signal and the amplitude of the baseline drift trend line changing over time, calculate the parameters corresponding to the tightness of the clothing.

[0102] S65: Calculate the tightness measurement index based on the parameters corresponding to the tightness state of the garment.

[0103] Therefore, by calculating one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal, the energy intensity of the pulse AC signal can be represented, providing a selectivity for reflecting the energy intensity of the pulse AC signal.

[0104] Please see Figure 11 , Figure 11 In another embodiment of this application Figure 10 A further sub-flowchart of step S63. When the energy intensity of the pulse AC signal includes the root mean square value of the pulse AC signal, calculating one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal to represent the energy intensity of the pulse AC signal through one of the power spectral density, harmonic amplitude, or root mean square value includes:

[0105] S631: Calculate the square of each of the plurality of signal values ​​in the pulse communication signal;

[0106] S632: Calculate the average of the sums of the multiple squared values;

[0107] S633: The root mean square value of the pulse AC signal is obtained by taking the square root of the average value, so as to represent the energy intensity of the pulse AC signal through the root mean square value.

[0108] Therefore, the root mean square value of the pulse communication signal effectively quantifies the effective amplitude of the pulse communication signal and effectively measures the energy intensity of the pulse communication signal, so as to accurately calculate the energy intensity of the pulse communication signal and improve the reliability of the subsequent tensile quantification index.

[0109] Specifically, based on the above, firstly, each signal value in the pulse-to-pulse (PPG) signal sequence Pulse[n] (n = 0-299) is squared to obtain multiple squared values ​​corresponding to each signal value. Then, these squared values ​​are summed to obtain a sum of squares. Next, the sum of squares is averaged to obtain an average value. Finally, the square root of this average value is calculated to obtain the root-mean-square (RMS) value of the PPG signal. Calculating the RMS value of the PPG signal helps in assessing the energy intensity of the AC signal in the PPG signal.

[0110] Specifically, the formula for calculating the root mean square value of the pulse AC signal is as follows:

[0111]

[0112] Wherein, RMS (Root Mean Square) is the root mean square value of the pulse AC signal, and N is the number of sampling points in the pulse AC signal sequence Pulse[n] (n = 0-299).

[0113] In some embodiments, analyzing the magnitude of the change in the trend line of the baseline drift over time includes:

[0114] The magnitude of the change in the trend line of the baseline drift over time is calculated using either the first or the second formula.

[0115] The first formula is:

[0116] M=max(Baseline[n])-min(Baseline[n])

[0117] Where M is the value of the amplitude of the baseline drift trend line changing over time, Baseline[n] is the discrete sequence of n+1 points in the baseline drift trend line, max(Baseline[n]) is the maximum value in the discrete sequence Baseline[n], and min(Baseline[n]) is the minimum value in the discrete sequence Baseline[n].

[0118] The second formula is:

[0119]

[0120] in, The sum of the last 10 values ​​in the discrete sequence Baseline[n] This is the sum of the first 10 values ​​in the discrete sequence Baseline[n].

[0121] Therefore, calculating the magnitude of the change of the trend line of the baseline drift over time using the first formula or the second formula provides an alternative method for calculating the value of the magnitude of the change of the trend line of the baseline drift over time.

[0122] In some embodiments, the difference between the value corresponding to the trend line of baseline drift at the start time and the value corresponding to the end time can also be used to represent the magnitude of the change of the trend line of baseline drift over time.

[0123] In other embodiments, two percentages may be set, and two sampling points corresponding to the two percentages within the trend line of the baseline drift may be calculated respectively. The difference between the values ​​corresponding to the two sampling points is then obtained, and the difference is represented as the magnitude of the change of the trend line of the baseline drift over time.

[0124] In some embodiments, the calculation of parameters corresponding to the tightness of the garment based on the energy intensity of the obtained pulse-electrode signal and the amplitude of the baseline drift trend line changing over time includes:

[0125] Based on the energy intensity of the obtained pulse communication signal and the amplitude of the baseline drift trend line changing over time, the parameters corresponding to the tightness of the garment are calculated using the third or fourth formula.

[0126] The third formula is as follows:

[0127]

[0128] The fourth formula is:

[0129]

[0130] Wherein, λ is the parameter corresponding to the tightness of the garment, RMS is the energy intensity of the pulse communication signal, and M is the amplitude of the baseline drift trend line changing over time.

[0131] Therefore, the parameter λ corresponding to the tightness of the garment is calculated using the third formula or the fourth formula, providing an alternative method for calculating the parameter corresponding to the tightness of the garment.

[0132] Please see Figure 12 , Figure 12 This document shows the trend line of baseline drift of the PPG signal and the waveform of the pulse AC signal under three different wearing tightnesses of wearable devices, according to one embodiment of this application. Specifically, based on... Figure 12The amplitude values ​​of the baseline drift trend lines and the pulse AC signal waveforms of the three different baseline drift trend lines over time and the energy intensity values ​​of the pulse AC signal are calculated, and then the fourth formula is used to calculate... Figure 12 The tightness measurement index of the three different wearing tightness of the aforementioned wearable devices.

[0133] Specifically, combining the above formulas and calculations, the value of the amplitude of the baseline drift trend line changing with time is calculated as 336151 based on the baseline drift trend line in the first row, 67448 based on the baseline drift trend line in the second row, and 29240 based on the baseline drift trend line in the third row. The energy intensity of the pulse exchange signal is calculated as 515772 based on the pulse exchange signal in the first row, 174778 based on the pulse exchange signal in the second row, and 161318 based on the pulse exchange signal in the third row. The calculated values ​​of the parameter λ corresponding to the wearing tightness state are 1.53, 2.59, and 5.51, respectively. Through preset mathematical functions or table lookup operations, the corresponding tightness measurement indicators are obtained based on the parameter λ corresponding to the wearing tightness state.

[0134] In some embodiments, the detection method further includes:

[0135] The system displays the obtained tightness measurement index and prompts the user to adjust the tightness of the wearable device based on the tightness measurement index.

[0136] Therefore, the calculated tightness measurement index is displayed so that users can intuitively observe the current tightness of the wear, better guide users to adjust the tightness of the wear, solve the problem of inaccurate data caused by improper wearing, improve the reliability of monitoring data, ensure the fit and comfort of the wearable device, and also improve user satisfaction and the practicality of the wearable device.

[0137] In some embodiments, the tightness of the wear can be displayed based on the calculated tightness quantification index, such as too loose or too tight, and wearing suggestions for the wearable device can also be provided to help the user adjust the tightness of the wearable device.

[0138] In some embodiments, controlling the air pump to inflate the airbag includes:

[0139] The air pump is controlled to inflate the airbag at a target airflow rate within a target inflation time.

[0140] Thus, the precise, short-term inflation of the airbag via the air pump provides a dynamic and accurate process for quantitatively assessing the tightness of the fit. By controlling the air pump to inflate the airbag at a target airflow rate within a target inflation time, the inflation strategy can be dynamically adjusted, allowing for more accurate calculation of the quantifiable tightness index based on the PPG signal.

[0141] In some embodiments, the target inflation time falls within the range of 0-8 seconds during the airbag inflation process. The specific value of the target inflation time can be set as needed and is not limited here. The target inflation time will not be 0 seconds.

[0142] It is understood that the target airflow velocity may be, but is not limited to, greater than or equal to 150 ml / min, and the target airflow velocity can be set according to actual needs, without limitation here.

[0143] In some embodiments, the detection method further includes:

[0144] The monitored and analyzed user physiological characteristic data is uploaded to a cloud service platform and stored in the cloud service platform.

[0145] Thus, the detected and analyzed user's physiological characteristic data is sent and stored in the cloud service platform, and the progress of the detection method can be monitored in real time.

[0146] Please see Figure 13 , Figure 13 This is a schematic block diagram of a wearable device 100 according to an embodiment of this application. The wearable device 100 is used to monitor and analyze the user's physiological characteristic data. The wearable device 100 includes an air pump 1, an airbag 2, a controller 3, and a PPG sensor 4. The air pump 1 is connected to the airbag 2. The controller 3 controls the air pump 1 to inflate the airbag 2. The PPG sensor 4 detects PPG signals during the inflation process. The controller 3 acquires and records the PPG signals detected by the PPG sensor 4 during the inflation process, and derives a quantified tightness index based on the recorded PPG signals. This quantified tightness index indicates the wearing tightness of the wearable device 100. The physiological characteristic data includes heart rate, blood oxygen saturation, etc.

[0147] Therefore, the wearable device 100, which monitors and analyzes users' physiological characteristic data, integrates PPG signal measurement and air pump 1 control technology. This allows users to determine the current tightness of the device based on the final calculated tightness quantification index. The tightness quantification index guides users to adjust the tightness appropriately, ensuring the fit and comfort of the wearable device 100. It also solves the problem of inaccurate data caused by improper wearing, improves the reliability of monitoring data, and enhances both comfort and accuracy. This dual guarantee improves user satisfaction and the practicality of the wearable device 100, and is expected to attract more users and broaden the application scope of this type of wearable device 100 in the market.

[0148] The operations performed by the wearable device 100 correspond to the steps in the methods of the foregoing embodiments. More specific operations performed by the wearable device 100 can be found in the steps in the methods of the foregoing embodiments.

[0149] In some embodiments, the wearable device 100 can also analyze the monitored physiological parameters and then remind the user based on the analysis results, so as to play an important auxiliary tool for preventing diseases and promoting a healthy life.

[0150] In some embodiments, the wearable device 100 is a smartwatch, the air pump 1, the controller 3 and the PPG sensor 4 are disposed inside the dial of the smartwatch, the airbag 2 is disposed in at least part of the strap of the smartwatch, and the air pump 1 is connected to the airbag 2.

[0151] In some embodiments, the tightness measurement index is the pressure exerted by the PPG sensor 4 on the skin of a certain part of the user's body before the airbag 2 is inflated, that is, the surface pressure of the PPG sensor. When the tightness of the wearable device 100 is in a reasonable state, the surface pressure of the PPG sensor is in the pressure range of 10-20 mmHg.

[0152] Specifically, such as Figure 12 As shown, the two waveforms in the first row are the baseline drift trend line and pulse AC signal when the PPG sensor surface pressure is 0.5 mmHg; the two waveforms in the second row are the baseline drift trend line and pulse AC signal when the PPG sensor surface pressure is 5.3 mmHg; and the two waveforms in the third row are the baseline drift trend line and pulse AC signal when the PPG sensor surface pressure is 15.3 mmHg.

[0153] More specifically, combining the above, we take the parameter λ, which relates to the tightness of the garment, as the X-axis and the surface pressure of the PPG sensor as the Y-axis. We then perform linear regression on multiple datasets obtained from numerous experiments to obtain the linear regression formula: y = 3.656x - 4.7023, and the formula for the statistical value R, which represents the perfect fit of the linear regression: R... 2 =0.996, which indicates that the surface pressure of the PPG sensor calculated by the above linear regression formula has high accuracy. In the linear regression formula, x represents the value of parameter λ related to the tightness of the garment, and y represents the value of the surface pressure of the PPG sensor.

[0154] It can be seen that, through the linear regression formula, the surface pressure of the PPG sensor can be calculated based on the parameter λ of the wearing tightness state, and the surface pressure of the PPG sensor can be used as the quantitative index of tightness, which can effectively reflect the force that the PPG sensor 4 is subjected to when in contact with the skin, thereby assessing whether the wearing tightness of the wearable device 100 is in a reasonable state, so as to ensure the reliability of the physiological characteristic parameters monitored by the wearable device 100.

[0155] It is understood that the linear regression formulas for different types and models of wearable devices 100 are different, and multiple experiments in the laboratory are required to obtain a matching linear regression formula.

[0156] Please see Figure 14 , Figure 14 This is a further schematic block diagram of a wearable device 100 according to one embodiment of this application. In some embodiments, the wearable device 100 further includes a display unit 5, which is used to display the obtained tightness measurement index and prompt the user to adjust the wearing tightness of the wearable device 100 based on the tightness measurement index.

[0157] Therefore, the display unit 5 displays the calculated tightness measurement index, so that the user can intuitively observe the current tightness of the wear, better guide the user to adjust the appropriate tightness of the wear, solve the problem of inaccurate data caused by improper wearing, improve the reliability of monitoring data, and also ensure the fit and comfort of the wearable device 100, thereby improving user satisfaction and the practicality of the wearable device 100.

[0158] In some embodiments, the display unit 5 may display the tightness of the wear based on the calculated tightness measurement index, such as prompts for being too loose or too tight, and may also provide wearing suggestions for the wearable device 100, such as tightening or loosening the strap by one notch, so that the user can adjust the tightness of the wearable device 100.

[0159] It is understood that the display unit 5 may be a touch screen, a display screen, or other component capable of displaying parameters.

[0160] In some embodiments, the controller 3 is also used to control the air pump 1 to inflate the airbag 2 at a target airflow rate within a target inflation time.

[0161] Thus, the precise control of the airbag 2 by the air pump 1 during brief inflation provides a dynamic and accurate process for quantitatively evaluating the tightness of the wearable device 100. By controlling the air pump 1 to inflate the airbag 2 at a target airflow rate within a target inflation time, the inflation strategy is dynamically adjusted to more accurately calculate the quantifiable index of tightness based on the PPG signal.

[0162] In some embodiments, the display unit 5 may also display the target inflation time and the airflow speed, and the user may adjust the target inflation time and the airflow speed on the display unit 5 according to the recommendations of the wearable device 100.

[0163] It is understood that the display unit 5 can be controlled by voice commands, button commands, touch commands, etc., and this is not limited here.

[0164] Please see Figure 15 , Figure 15 This is a schematic block diagram of a wearable device 100 according to another embodiment of this application. The wearable device 100 includes a processor 300 and a memory 200. The memory 200 stores a computer program, and the processor 300 runs the computer program to perform the method for detecting the tightness of the wearable device as described above.

[0165] For example, the method for detecting the tightness of the wearable device includes:

[0166] Control the air pump to inflate the airbag;

[0167] Record PPG signals during inflation;

[0168] A tightness quantification index is derived based on the recorded PPG signal, which indicates the tightness of the wearable device.

[0169] Therefore, the wearable device 100 executes the method for detecting the tightness of the wearable device, so as to guide the user to adjust the tightness appropriately through the quantified tightness index. This solves the problem of inaccurate data caused by improper wearing, improves the reliability and continuity of monitoring data, ensures that each measurement reaches the optimal state, and ensures the fit and comfort of the wearable device 100. The dual guarantee of comfort and accuracy improves user satisfaction and the practicality of the wearable device 100, and is expected to attract more users and broaden the application scope of this type of wearable device 100 in the market.

[0170] Other steps in the method for detecting the tightness of a wearable device executed by the processor 300 running the computer program can be found in the steps of the methods in the foregoing embodiments.

[0171] The processor 300 may be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. The memory 200 may be volatile memory or non-volatile memory, or may include both. For example, it may be, but is not limited to, a flash drive, read-only memory (ROM), random access memory (RAM), a magnetic disk, or an optical disk. The processor 300 is connected to the memory 200.

[0172] Please see Figure 16 , Figure 16 This is a schematic block diagram of a wearable device tightness detection system 400 according to an embodiment of this application. The wearable device tightness detection system 400 includes the wearable device 100 as described above.

[0173] The operation performed by the wearable device tightness detection system 400 corresponds to the steps in the methods of the foregoing embodiments. For more specific operations performed by the wearable device tightness detection system 400, please refer to the steps in the methods of the foregoing embodiments.

[0174] Therefore, the wearable device tightness detection system 400 can perform the function of the wearable device 100 guiding the user to adjust the appropriate tightness through the tightness measurement indicators, solving the problem of inaccurate data caused by improper wearing, improving the reliability of the monitoring data of the wearable device 100, and providing dual protection of comfort and accuracy, thereby improving user satisfaction and the practicality of the wearable device 100, which is expected to attract more users and broaden the application scope of this type of wearable device 100 in the market.

[0175] Please see Figure 17 , Figure 17 This is a further schematic block diagram of a wearable device tightness detection system 400 according to one embodiment of this application. In some embodiments, the wearable device tightness detection system 400 further includes a cloud service platform 500, which is used to store the physiological characteristic data of the user monitored and analyzed by the wearable device 100 and to remotely monitor the usage status of the wearable device 100. The wearable device 100 is also used to establish a communication connection with the cloud service platform 500, and to upload the physiological characteristic data of the user monitored and analyzed by the wearable device 100 to the cloud service platform 500 and store it in the cloud service platform 500.

[0176] Thus, the wearable device 100 and the cloud service platform 500 are integrated into one, forming a wearable device tightness detection system 400, which stores the user's physiological characteristic data detected by the wearable device 100 and remotely monitors the usage status of the wearable device 100, making the wearable device tightness detection system 400 more intelligent.

[0177] The cloud service platform 500 may include a server.

[0178] This application also provides a computer-readable storage medium storing a computer program, which is invoked by a processor 300 to execute the method for detecting the tightness of a wearable device as described above.

[0179] For example, the method for detecting the tightness of the wearable device includes:

[0180] Control the air pump to inflate the airbag;

[0181] Record PPG signals during inflation;

[0182] A tightness quantification index is derived based on the recorded PPG signal, which indicates the tightness of the wearable device.

[0183] The other steps of the method for detecting the tightness of the wearable device, which is executed by the processor 300 after the computer program is called, can be found in the steps of the methods in the foregoing embodiments.

[0184] The computer storage medium in this application embodiment can be any combination of one or more computer-readable media. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. 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 computer-readable storage media (a non-exhaustive list) include: 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 or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, 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.

[0185] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0186] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0187] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Where there is no conflict, the embodiments and features in the embodiments of this application can be combined with each other. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting the wearing tightness of a wearable device, used to detect the wearing tightness of the wearable device, the wearable device comprising an air pump and an airbag, the air pump being connected to the airbag, characterized in that, The detection method includes: Control the air pump to inflate the airbag; Record PPG signals during inflation; A tightness quantification index is derived based on the recorded PPG signal, which indicates the tightness of the wearable device.

2. The method for detecting the wearing tightness of a wearable device according to claim 1, characterized in that, The quantitative index of tightness derived from the recorded PPG signal includes: The recorded PPG signal was filtered to obtain the baseline drift trend line and pulse AC signal in the PPG signal; The amplitude of the baseline drift trend line changing over time was analyzed, as well as the energy intensity of the pulse-electrode signal was analyzed. Based on the energy intensity of the obtained pulse communication signal and the amplitude of the baseline drift trend line changing over time, parameters corresponding to the tightness of the clothing are calculated. The tightness measurement index is calculated based on the parameters corresponding to the tightness state of the garment.

3. The method for detecting the tightness of wearable devices according to claim 2, characterized in that, The filtering of the recorded PPG signal to derive the baseline drift trend line and pulse AC signal in the PPG signal includes: The recorded PPG signals are sampled to obtain discrete PPG signal sequences based on continuous PPG signals; Median filtering is performed on the sampled discrete PPG signal sequence to obtain the baseline drift trend line and pulse AC signal in the PPG signal.

4. The method for detecting the tightness of wearable devices according to claim 3, characterized in that, The process of performing median filtering on the sampled discrete PPG signal sequence to derive the baseline drift trend line and pulse AC signal in the PPG signal includes: Determine the width of the filtering window for median filtering, wherein the width of the filtering window is related to the number of sampling points covered by the filtering window; The sampled discrete PPG signal sequence is processed based on the width of the filtering window; The discrete PPG signal sequence obtained after processing is subjected to median filtering through the filtering window to obtain the baseline drift trend line and pulse AC signal in the PPG signal.

5. The method for detecting the wearing tightness of a wearable device according to claim 4, characterized in that, The step of performing median filtering on the processed discrete PPG signal sequence through the filtering window to derive the baseline drift trend line and pulse AC signal in the PPG signal includes: Based on the width of the filtering window, multiple subsequences are selected from the discrete PPG signal sequence obtained after processing. The number of the multiple subsequences is equal to the number of values ​​in the discrete PPG signal sequence obtained after sampling, and the number of values ​​included in each subsequence corresponds to the width of the filtering window. The multiple values ​​in each of the multiple subsequences are sorted, and the median value is selected from the multiple values ​​in each sorted subsequence to obtain multiple median values ​​corresponding to the multiple subsequences respectively. A trend line for baseline drift is obtained based on the median values ​​corresponding to the multiple subsequences; The pulse AC signal is calculated based on the obtained baseline drift trend line and the discrete PPG signal sequence obtained after sampling.

6. The method for detecting the wearing tightness of a wearable device according to claim 4, characterized in that, The determination of the width of the filtering window for median filtering includes: The width of the filtering window is determined based on the period of the pulse exchange signal, wherein the width of the filtering window is less than the width of one period of the pulse exchange signal.

7. The method for detecting the wearing tightness of a wearable device according to claim 5, characterized in that, The number of values ​​corresponding to the width of the filtering window is odd. The processing of the sampled discrete PPG signal sequence based on the width of the filtering window includes: The expansion number is determined based on the width of the filter window, wherein the expansion number is (n-1) / 2, and n is the number of values ​​corresponding to the width of the filter window; A first set of data is added before the first value of the sampled discrete PPG signal sequence, and a second set of data is added after the last value of the sampled discrete PPG signal sequence, wherein the first set of data is the last extended number of values ​​of the sampled discrete PPG signal sequence, and the second set of data is the first extended number of values ​​of the sampled discrete PPG signal sequence.

8. The method for detecting the wearing tightness of a wearable device according to claim 7, characterized in that, The selection of multiple sub-sequences from the discrete PPG signal sequence obtained after processing, based on the width of the filtering window, includes: Determine the preceding and following values ​​of each value in the discrete PPG signal sequence obtained after processing, wherein the number of preceding and following values ​​of each value is equal to (n-1) / 2. Each value, along with the values ​​of its preceding and following parts, is determined as a subsequence corresponding to that value, thereby obtaining the multiple subsequences.

9. The method for detecting the wearing tightness of a wearable device according to claim 2, characterized in that, The energy intensity of the pulse AC signal includes one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal; the analysis of the energy intensity of the pulse AC signal includes: Calculate one of the power spectral density, harmonic amplitude, or root mean square value of the pulse AC signal, so as to represent the energy intensity of the pulse AC signal through one of the power spectral density, harmonic amplitude, or root mean square value.

10. The method for detecting the wearing tightness of a wearable device according to claim 9, characterized in that, When the energy intensity of the pulse-interval signal includes the root-mean-square (RMS) value of the pulse-interval signal, calculating one of the power spectral density, harmonic amplitude, or RMS value of the pulse-interval signal, so as to represent the energy intensity of the pulse-interval signal through one of the power spectral density, harmonic amplitude, or RMS value, includes: Calculate the square of each of the multiple signal values ​​in the pulse-electrode signal; Calculate the average of the sums of the multiple squared values; The root mean square of the average value is obtained to obtain the root mean square value of the pulse AC signal, so as to represent the energy intensity of the pulse AC signal through the root mean square value.

11. The method for detecting the wearing tightness of a wearable device according to claim 2, characterized in that, The analysis of the magnitude of the baseline drift trend line changing over time includes: The magnitude of the change in the trend line of the baseline drift over time is calculated using either the first or the second formula. The first formula is: M=max(Baseline[n])-min(Baseline[n]) Where M is the value of the amplitude of the baseline drift trend line changing over time, Baseline[n] is the discrete sequence of n+1 points in the baseline drift trend line, max(Baseline[n]) is the maximum value in the discrete sequence Baseline[n], and min(Baseline[n]) is the minimum value in the discrete sequence Baseline[n]. The second formula is: in, The sum of the last 10 values ​​in the discrete sequence Baseline[n] This is the sum of the first 10 values ​​in the discrete sequence Baseline[n].

12. The method for detecting the wearing tightness of a wearable device according to claim 2, characterized in that, The parameters corresponding to the tightness of the garment are calculated based on the changes in the energy intensity of the obtained pulse exchange signal and the amplitude of the baseline drift trend line over time, including: Based on the energy intensity of the obtained pulse communication signal and the amplitude of the baseline drift trend line changing over time, the parameters corresponding to the tightness of the garment are calculated using the third or fourth formula. The third formula is as follows: The fourth formula is: Wherein, λ is the parameter corresponding to the tightness of the garment, RMS is the energy intensity of the pulse communication signal, and M is the amplitude of the baseline drift trend line changing over time.

13. The method for detecting the wearing tightness of a wearable device according to claim 1, characterized in that, The detection method further includes: The system displays the obtained tightness measurement index and prompts the user to adjust the tightness of the wearable device based on the tightness measurement index.

14. The method for detecting the wearing tightness of a wearable device according to claim 1, characterized in that, The control of the air pump to inflate the airbag includes: The air pump is controlled to inflate the airbag at a target airflow rate within a target inflation time.

15. A wearable device for monitoring and analyzing a user's physiological characteristic data, characterized in that, include: air pump; An airbag, wherein the air pump is connected to the airbag; Controller; PPG sensor; The controller is used to control the air pump to inflate the airbag, the PPG sensor is used to detect the PPG signal during the inflation process, the controller is used to acquire the PPG signal detected by the PPG sensor during the inflation process and record the PPG signal during the inflation process, and derive a tightness measurement index based on the recorded PPG signal, the tightness measurement index indicating the wearing tightness of the wearable device.

16. The wearable device according to claim 15, characterized in that, The wearable device also includes a display unit, which is used to display the obtained tightness measurement index and prompt the user to adjust the tightness of the wearable device based on the tightness measurement index.

17. The wearable device according to claim 15, characterized in that, The controller is also used to control the air pump to inflate the airbag at a target airflow rate within a target inflation time.

18. A wearable device, characterized in that, include: A processor and a memory, wherein the memory stores a computer program, and the processor runs the computer program to perform the method for detecting the tightness of the wearable device according to any one of claims 1-14.

19. A system for detecting the tightness of a wearable device, characterized in that, include: The wearable device as described in any one of claims 15-18.

20. The system for detecting the tightness of a wearable device according to claim 19, characterized in that, The wearable device tightness detection system also includes a cloud service platform for storing the user's physiological characteristic data monitored and analyzed by the wearable device and for remotely monitoring the usage status of the wearable device.

21. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when called by a processor, executes the method for detecting the tightness of the wearable device according to any one of claims 1-14.

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