Wearable device wearing environment detection method and system

Abstract

The present invention discloses a wearable device wearing environment detection method and system, belonging to the field of wearable device technology, comprising: S1, periodically collecting PPG signals through the wearable device's built-in sensor, and performing morphological analysis to extract morphological features; S2, analyzing information theory indicators based on the collected PPG signals; S3, substituting the morphological features and information theory indicators into a constructed human wearing discrimination model; S4, comparing the output value of the human discrimination model with a set discrimination threshold to determine the wearing environment of the wearable device. The method provided by the present invention can effectively improve the ability to distinguish between living and non-living subjects without increasing the complexity of peripheral hardware, providing a better prerequisite for accurately measuring heart rate and blood oxygen; the calculation method provided by the present invention is universal and does not rely on hardware differences.

Description

Technical Field

[0001] The present invention belongs to the technical field of wearable devices, and in particular relates to a wearable device wearing environment detection method and system combining PPG morphology and information theory. Background Art

[0002] In smartwatches and other wearable devices, physiological measurement technology based on photoplethysmography (PPG) signals is widely used to monitor health data such as heart rate and blood oxygen levels. However, when a smart device is not properly worn on the body but placed on other surfaces, the collected PPG signals can differ significantly, leading to inaccurate calculations of physiological parameters. Therefore, determining whether the device is properly worn on the body and ensuring data accuracy is crucial.

[0003] Traditional smartwatches and wearable devices typically determine whether a device is being worn or removed by checking whether the PS value exceeds a certain threshold. However, this approach alone is inaccurate in practical applications and can easily misidentify non-human surfaces as worn. Furthermore, some methods use proximity capacitive sensors for detection, which requires hardware materials such as copper foil to be laid inside the device, increasing manufacturing cost and complexity. Summary of the Invention

[0004] In response to the above-mentioned deficiencies in the prior art, the wearable device wearing environment detection method and system provided by the present invention solve the problems of low detection accuracy and increased equipment manufacturing cost and complexity of the existing related methods.

[0005] In order to achieve the above-mentioned purpose of the invention, the technical solution adopted by the present invention is: a method for detecting the wearing environment of a wearable device, comprising the following steps:

[0006] S1, periodically collect PPG signals through the built-in sensor of the wearable device, and perform morphological analysis on them to extract morphological features;

[0007] S2. Analyze information theory indicators based on the collected PPG signals;

[0008] S3, substituting the morphological features and information theory indicators into the constructed human wearing discrimination model;

[0009] S4. Compare the output value of the human body discrimination model with the set discrimination threshold to determine the wearing environment of the wearable device.

[0010] Furthermore, the morphological features extracted in step S1 include the rise time and fall time of the pulse wave and the waveform amplitude.

[0011] Furthermore, in step S2, the information theory indicators include entropy, mutual information and redundancy.

[0012] Furthermore, for information theory indicators:

[0013] The entropy is used to characterize the randomness of the PPG signal, and the analysis method is:

[0014] The collected PPG signal is divided into several intervals according to the maximum to minimum signal value within the segment, and the probability of the signal appearing in each interval is calculated to obtain the entropy, which is expressed as:

[0015]

[0016] Where, represents entropy, Indicates PPG signal In the intensity range i The probability of the interval, n is the number of intervals divided by the PPG signal;

[0017] The mutual information is used to characterize the correlation between the current collected PPG signal and the previous PPG signal, and the analysis method is as follows:

[0018] The collected PPG signal is divided into the first half and the second half, and the PPG signal of the first half and the second half is divided into several intervals according to the maximum to minimum value of the signal in the segment, and the corresponding signal occurrence probability is calculated. and ;

[0019] According to the probability of signal occurrence in the first half and the second half and , forming the space of all possible values ​​of the signal Table, statistics the joint probability distribution of the signal in each interval grid, and then calculate the mutual information, which is expressed as:

[0020]

[0021] Where, represents mutual information, represents the joint probability distribution of each interval grid, X and Y are the signal sets for the first half and the second half respectively;

[0022] The redundancy is used to characterize whether the PPG signal contains redundant information, which is expressed as:

[0023]

[0024] Where, Indicates the number of collected PPG signals.

[0025] Furthermore, in step S3, the human body wearing discrimination model constructed is:

[0026]

[0027] Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of .

[0028] Furthermore, in step S3, the weight coefficient Adjust according to the hardware settings of different wearable devices and use the noise intensity measured by the wearable device Afterwards, As The weight adjustment coefficient.

[0029] Furthermore, in step S4, when the output value of the human body discrimination model is greater than a set discrimination threshold, the wearable device is worn on the human body, otherwise it is placed on the surface of an object.

[0030] A wearable device wearing environment detection system, comprising:

[0031] PPG signal acquisition module: used to periodically collect PPG signals;

[0032] Morphological feature extraction module: used to perform morphological analysis on the collected PPG signals and extract morphological features;

[0033] Information theory index analysis module: used to perform information theory analysis on the collected PPG signals and extract information theory indicators;

[0034] Wearing discrimination module: used to substitute the extracted morphological features and information theory indicators into the constructed human wearing discrimination model, and determine the wearing environment of the wearable device based on its output values.

[0035] Furthermore, the morphological characteristics include the rise time and fall time of the pulse wave and the waveform amplitude.

[0036] The information theory indicators include entropy, mutual information and redundancy.

[0037] Furthermore, in the wearing discrimination module, the output value of the human body discrimination model is compared with a set discrimination threshold. When the output value is greater than the discrimination threshold, the wearable device is worn on the human body; otherwise, it is placed on the surface of an object.

[0038] The human body wearing discrimination model is expressed as:

[0039]

[0040] Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of .

[0041] The beneficial effects of the present invention are:

[0042] 1. High-precision wearing status judgment:

[0043] The proposed method, based on morphological analysis and information-theoretic metrics of photoplethysmography (PPG) signals, can provide more accurate wear status determination. Compared to traditional methods that rely on skin resistance (PS), PPG signals capture more physiological characteristics, such as the heart pulse waveform, significantly improving the accuracy of determining whether a device is worn on the body. This method effectively avoids false positives caused by PS alone, especially in situations such as sweating or poor contact.

[0044] 2. No need to increase hardware costs:

[0045] Compared to some methods that rely on proximity capacitance sensors to determine whether a device is worn on the body, this invention does not require additional hardware, such as copper foil or contact electrodes. High accuracy is achieved simply by collecting data from existing PPG sensors and performing algorithmic processing. This significantly reduces manufacturing costs, avoids major changes to existing device designs, and enhances market adaptability.

[0046] 3. Flexibility to adapt to different hardware devices:

[0047] Experiments have shown that most of the weight parameters in the human wear discrimination model of the present invention are stable. Only the α2 parameter related to amplitude fluctuation needs to be adjusted according to different hardware devices. A method is further provided to equivalently control this parameter without being affected by the device. This flexible parameter adjustment design can adapt to the differences in light intensity and unit measurement between different devices, making the method applicable to smart watch devices of different brands and models. This flexibility of the system ensures cross-device portability and adapts to the needs of different hardware platforms.

[0048] 4. Stable threshold judgment:

[0049] The discrimination threshold θ in this invention has been determined through experiments, ensuring consistent judgment standards across different devices and scenarios. The stable threshold setting reduces the complexity of the system in practical applications, avoids frequent adjustments, and lowers the barrier to use.

[0050] 5. Reduce false positives and improve user experience:

[0051] By combining the physiological characteristics of PPG signals with information theory analysis, this invention effectively reduces false positives, ensuring reliable device operation while worn. Even when the device is placed on a surface, the discriminant model can promptly identify it, preventing unnecessary data recording or erroneous health monitoring data due to incorrect wearing. This high accuracy and reduced false positives enhance user trust and provide a better user experience.

[0052] 6. Applicable to various application scenarios:

[0053] This invention is not only applicable to smart watches, but also to other wearable health monitoring devices, such as smart bracelets and heart rate monitors. This technology can provide high-precision wear status judgment for a wide range of wearable devices, thereby ensuring the reliability of health monitoring, sports tracking, and medical data collection. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of the wearable device wearing environment detection method provided by the present invention. DETAILED DESCRIPTION

[0055] The specific embodiments of the present invention are described below to facilitate understanding of the present invention by those skilled in the art. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the appended claims, these changes are obvious, and all inventions and creations utilizing the concepts of the present invention are protected.

[0056] The embodiment of the present invention provides a method for detecting the wearing environment of a wearable device, such as Figure 1 As shown, the following steps are included:

[0057] S1, periodically collect PPG signals through the built-in sensor of the wearable device, and perform morphological analysis on them to extract morphological features;

[0058] S2. Analyze information theory indicators based on the collected PPG signals;

[0059] S3, substituting the morphological features and information theory indicators into the constructed human wearing discrimination model;

[0060] S4. Compare the output value of the human body discrimination model with the set discrimination threshold to determine the wearing environment of the wearable device.

[0061] In step S1 of the embodiment of the present invention, the wearable device periodically collects PPG signals through the built-in photoelectric sensor. When the device is worn on the human body, the PPG signal will show an obvious heartbeat pulse waveform, while when the device is placed on the surface of an object, the PPG signal will show lower volatility or random noise.

[0062] In an optional embodiment of the present invention, the wearable device may be a smart watch, a smart bracelet, a heart rate monitor or other wearable health monitoring device.

[0063] In step S1 of the embodiment of the present invention, the extracted morphological features include the rise time and fall time of the pulse wave and the waveform amplitude.

[0064] In a specific embodiment of the present invention, when the device is worn on the human body, the heart pulse wave will form a specific rise time and fall time .

[0065] The waveform amplitude of the PPG signal when worn on the human body It will fluctuate periodically with the pulse, but when it is on the surface of an object, the fluctuation of the amplitude is significantly reduced.

[0066] By analyzing the periodicity of the signal, its autocorrelation coefficient in a given time window is calculated. If a periodic heartbeat signal is detected, it means that the device is worn on the human body.

[0067] In step S2 of the embodiment of the present invention, the information theory indicators include entropy, mutual information and redundancy;

[0068] Specifically, entropy is used to characterize the randomness of the PPG signal. The higher the entropy, the greater the randomness of the surface signal, and the lower the entropy, the stronger the structure and regularity of the surface signal. The entropy analysis method in this embodiment is:

[0069] The collected PPG signal is divided into several intervals according to the maximum to minimum signal value within the segment, and the probability of the signal appearing in each interval is calculated to obtain the entropy, which is expressed as:

[0070]

[0071] Where, represents entropy, Indicates PPG signal In the intensity range i The probability of the interval, n is the number of intervals divided by the PPG signal.

[0072] Mutual information is used to characterize the correlation between the current PPG signal and the previous PPG signal. The greater the mutual information, the more relevant information there is between the current signal and the past signal. The mutual information analysis method in this embodiment is:

[0073] The collected PPG signal is divided into the first half and the second half, and the PPG signal of the first half and the second half is divided into several intervals according to the maximum to minimum value of the signal in the segment, and the corresponding signal occurrence probability is calculated. and ;

[0074] According to the probability of signal occurrence in the first half and the second half and , forming the space of all possible values ​​of the signal Table, statistics the joint probability distribution of the signal in each interval grid, and then calculate the mutual information, which is expressed as:

[0075]

[0076] Where, represents mutual information, represents the joint probability distribution of each interval grid, X and Y They are the signal sets of the first half and the second half respectively.

[0077] Redundancy is used to characterize whether the PPG signal contains redundant information. It measures the signal redundancy in different time windows. The redundancy in this embodiment is expressed as:

[0078]

[0079] Where, Indicates the number of collected PPG signals.

[0080] In an optional embodiment of the present invention, l The value is 250 points.

[0081] In step S3 of the embodiment of the present invention, a human wearing discrimination model based on morphological and information theory metrics is constructed, and its output value is used to determine whether the device is worn on the human body. In this embodiment, the constructed human wearing discrimination model is:

[0082]

[0083] Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of .

[0084] Furthermore, experimental verification shows that since the parameter is the human body or the statistical measurement characteristics of signal information, it is different from the representation of signal strength without equipment and does not change with different equipment. Therefore, the weight coefficient in the above model is: 、 、 and Relatively stable, only weight coefficient Needs adjustment.

[0085] Specifically, in this embodiment, since different devices have different light intensity measurement units, the same signal amplitude may be represented differently. Therefore, in this embodiment, adjustments are made according to the hardware settings of different wearable devices, and the noise intensity measured by the wearable device is used. Afterwards, As The weight adjustment coefficient is used to cancel the differences between devices.

[0086] In an optional embodiment of the present invention, the values ​​of the above parameters are: [0.28~0.31], 0.5, 0.2, [0.19~0.22], [0.85~0.1], The value of 0.1 and the discrimination threshold value of 14.5 should be noted that for all the above parameters, if a range is given, the median value is the default value. It can be adjusted according to actual performance, but it is not recommended to exceed the range.

[0087] In step S4 of the embodiment of the present invention, based on the above model, when the output value of the human body discrimination model is greater than the set discrimination threshold, the wearable device is worn on the human body, otherwise it is placed on the surface of an object.

[0088] An embodiment of the present invention further provides a wearable device wearing environment detection system based on the above-mentioned wearable device wearing environment detection method, comprising:

[0089] PPG signal acquisition module: used to periodically collect PPG signals;

[0090] Morphological feature extraction module: used to perform morphological analysis on the collected PPG signals and extract morphological features;

[0091] Information theory index analysis module: used to perform information theory analysis on the collected PPG signals and extract information theory indicators;

[0092] Wearing discrimination module: used to substitute the extracted morphological features and information theory indicators into the constructed human wearing discrimination model, and determine the wearing environment of the wearable device based on its output values.

[0093] In the embodiment of the present invention, the morphological characteristics include the rise time and fall time of the pulse wave and the waveform amplitude; specifically, when the device is worn on the human body, the heart pulse wave will form a specific rise time and fall time ; Under the human body wearing state, the waveform amplitude of the PPG signal It will fluctuate periodically with the pulse, but when it is on the surface of an object, the fluctuation of the amplitude is significantly reduced; by analyzing the periodicity of the signal, its autocorrelation coefficient in a given time window is calculated. If a periodic heartbeat signal is detected, it means that the device is worn on the human body.

[0094] In this embodiment, information theory indicators include entropy, mutual information and redundancy; specifically, entropy is used to characterize the randomness of the PPG signal. The higher the entropy, the greater the randomness of the surface signal, and the lower the entropy, the stronger the structure and regularity of the surface signal. Mutual information is used to characterize the correlation between the current collected PPG signal and the previous PPG signal. The larger the mutual information, the more relevant information there is between the current signal and the past signal. Redundancy is used to characterize whether the PPG signal contains redundant information, which measures the signal redundancy in different time windows.

[0095] In an embodiment of the present invention, in the wearing discrimination module, the output value of the human body discrimination model is compared with a set discrimination threshold. If it is greater than the discrimination threshold, the wearable device is worn on the human body; otherwise, it is placed on the surface of an object.

[0096] The human body wearing discrimination model is expressed as:

[0097]

[0098] Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of .

[0099] Furthermore, experimental verification shows that since the parameter is the human body or the statistical measurement characteristics of signal information, it is different from the representation of signal strength without equipment and does not change with different equipment. Therefore, the weight coefficient in the above model is: 、 、 and Relatively stable, only weight coefficient Needs adjustment.

[0100] Specifically, in this embodiment, since different devices have different light intensity measurement units, the same signal amplitude may be represented differently. Therefore, in this embodiment, adjustments are made according to the hardware settings of different wearable devices, and the noise intensity measured by the wearable device is used. Afterwards, As The weight adjustment coefficient is used to cancel the differences between devices.

[0101] In the embodiment of the present invention, a specific application example of the above method is provided:

[0102] In this embodiment, the above method is applied to a smartwatch product. For the PPG signal, the acquisition time is set to 10 seconds, the sampling rate is 25 Hz, and the signal is band-pass filtered from 0.4 Hz to 4 Hz. According to the above method, the discrimination results obtained are shown in Table 1:

[0103] Table 1: Wearable device wearing environment test results in different scenarios

[0104]

[0105] Among the 34 scenarios mentioned above, the method of the present invention has significantly improved the recognition probability of 22 scenarios from being completely unrecognizable, effectively distinguishing between living and non-living objects.

[0106] The above method provided by the present invention can effectively improve the ability to distinguish between living and non-living objects without increasing the complexity of peripheral hardware, providing a better prerequisite for accurately measuring heart rate and blood oxygen; the calculation method provided by the present invention is universal and does not rely on hardware differences.

[0107] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

[0108] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and it should be understood that the scope of protection of the present invention is not limited to such specific descriptions and embodiments. Those skilled in the art can make various other specific variations and combinations based on the technical teachings disclosed in the present invention without departing from the essence of the present invention, and such variations and combinations are still within the scope of protection of the present invention.

Claims

1. A method for detecting a wearing environment of a wearable device, characterized in that: The following steps are involved: S1, periodically collect PPG signals through the built-in sensor of the wearable device, and perform morphological analysis on them to extract morphological features; S2. Analyze information theory indicators based on the collected PPG signals; S3, substituting the morphological features and information theory indicators into the constructed human wearing discrimination model; S4. Compare the output value of the human body discrimination model with the set discrimination threshold to determine the wearing environment of the wearable device; The morphological features extracted in step S1 include the rise time and fall time of the pulse wave and the waveform amplitude; In step S2, the information theory indicators include entropy, mutual information and redundancy; In step S3, the human body wearing discrimination model constructed is: Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of In step S3, the weight coefficient Adjust according to the hardware settings of different wearable devices and use the noise intensity measured by the wearable device Afterwards, As The weight adjustment coefficient.

2. The wearable device wearing environment detection method according to claim 1, characterized in that: For information theoretic metrics: The entropy is used to characterize the randomness of the PPG signal, and the analysis method is: The collected PPG signal is divided into several intervals according to the maximum to minimum signal value within the segment, and the probability of the signal appearing in each interval is calculated to obtain the entropy, which is expressed as: Where, represents entropy, Indicates PPG signal In the intensity range i The probability of the interval, n is the number of intervals divided by the PPG signal; The mutual information is used to characterize the correlation between the current collected PPG signal and the previous PPG signal, and the analysis method is as follows: The collected PPG signal is divided into the first half and the second half, and the PPG signal of the first half and the second half is divided into several intervals according to the maximum to minimum value of the signal in the segment, and the corresponding signal occurrence probability is calculated. and ; According to the probability of signal occurrence in the first half and the second half and , forming the space of all possible values ​​of the signal Table, statistics the joint probability distribution of the signal in each interval grid, and then calculate the mutual information, which is expressed as: Where, represents mutual information, represents the joint probability distribution of each interval grid, X and Y are the signal sets for the first half and the second half respectively; The redundancy is used to characterize whether the PPG signal contains redundant information, which is expressed as: Where, Indicates the number of collected PPG signals.

3. The wearable device wearing environment detection method according to claim 1, characterized in that: In step S4, when the output value of the human body discrimination model is greater than the set discrimination threshold, the wearable device is worn on the human body, otherwise it is placed on the surface of the object.

4. A wearable device wearing environment detection system, implemented based on the wearable device wearing environment detection method according to any one of claims 1 to 3, characterized in that: include: PPG signal acquisition module: used to periodically collect PPG signals; Morphological feature extraction module: used to perform morphological analysis on the collected PPG signals and extract morphological features; Information theory index analysis module: used to perform information theory analysis on the collected PPG signals and extract information theory indicators; Wearing discrimination module: used to substitute the extracted morphological features and information theory indicators into the constructed human wearing discrimination model, and determine the wearing environment of the wearable device based on its output values.

5. The wearable device wearing environment detection system according to claim 4, characterized in that: The morphological characteristics include the rise time and fall time of the pulse wave and the waveform amplitude; The information theory indicators include entropy, mutual information and redundancy.

6. The wearable device wearing environment detection system according to claim 4, characterized in that: In the wearing discrimination module, the output value of the human body discrimination model is compared with a set discrimination threshold. If it is greater than the discrimination threshold, the wearable device is worn on the human body; otherwise, it is placed on the surface of an object; The human body wearing discrimination model is expressed as: Where, and They represent the rise time and fall time of the pulse wave in the morphological characteristics, Represents the waveform amplitude in the morphological characteristics, express t PPG signal at all times The entropy of express t PPG signal at all times and t- PPG signal at 1 moment The mutual information of express t PPG signal at all times The redundancy, 、 、 、 and They are 、 、 、 and The weight coefficient of .

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