CN116570291B - Wearing state judging method and device of wearing equipment, electronic equipment and medium - Google Patents
Wearing state judging method and device of wearing equipment, electronic equipment and medium Download PDFInfo
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Classifications
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
- G16H10/65—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records stored on portable record carriers, e.g. on smartcards, RFID tags or CD
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- A—HUMAN NECESSITIES
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
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- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/14542—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring blood gases
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- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
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Abstract
The invention discloses a wearing state judging method and device of wearable equipment, electronic equipment and a medium, wherein the method comprises the following steps: sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period; if the sensor of the type is detected to exist, acquiring detection data of the sensor, and judging whether the detection data meets a preset condition or not; and judging whether the wearing equipment is in a wearing state according to a judging result of whether the preset condition is met. The scheme is suitable for judging whether a certain type of sensor in the wearing equipment has an ambiguous condition or not, and whether the wearing equipment is in a wearing state or not can be judged through the operation of a plurality of detection or judgment steps, so that the universality of wearing state judgment and the judgment accuracy rate are improved.
Description
Technical Field
The invention relates to the technical field of electronic equipment, in particular to a wearing state judging method and device of wearable equipment, the electronic equipment and a medium.
Background
Wearable devices represented by smart bracelets, watches, and the like have been put into life for tracking daily activities, sleeping conditions, eating habits, and the like of people. In general, the wearing device detects real-time or time data of the user to learn the physical state of the user from the health, exercise and other data of the user. In order to save electricity consumption, part of functions of the wearable device which is not in a wearing state are closed, so that whether the device is in the wearing state or not is correctly judged to directly influence the experience of a user.
In some situations, the wearing state of the wearing equipment with unknown internal configuration needs to be detected, and the types of sensors carried by different wearing equipment are different, so that the technical problem that how to detect the wearing state of the wearing equipment and how to improve the accuracy of detection needs to be solved is solved.
Disclosure of Invention
The present invention has been made in view of the above-mentioned problems, and has as its object to provide a wearing state determination method, apparatus, electronic device, and medium of a wearable device that overcome or at least partially solve the above-mentioned problems.
According to an aspect of the present invention, there is provided a wearing state determination method of a wearable device, the method including:
sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period;
if the sensor of the type is detected to exist, acquiring detection data of the sensor, and judging whether the detection data meets a preset condition or not;
and judging whether the wearing equipment is in a wearing state according to a judging result of whether the preset condition is met.
In some embodiments, the sensor comprises any one or more of a skin electrical activity sensor, an blood oxygen sensor, an acceleration sensor, a gyroscope sensor, a PPG signal sensor, or a temperature sensor.
In some embodiments, the determining whether the detection data meets a preset condition includes:
judging whether the detected data and the detected data are within a preset threshold range or not;
if the detection data is within the threshold range, the preset condition is met, otherwise, the preset condition is not met;
or, the judging whether the detection data meets the preset condition comprises:
determining the quality grade of the detection data according to a preset algorithm model;
if the quality grade is above the preset grade, the preset condition is met, otherwise, the preset condition is not met.
In some embodiments, when the sensor is a PPG signal sensor, if it is detected that the type of sensor exists, acquiring detection data of the sensor, and determining whether the detection data meets a preset condition specifically includes:
collecting PPG signal data of the PPG signal sensor in a single detection period;
inputting PPG signal data into a pre-trained grade classification model, and obtaining the quality grade of the PPG signal from the grade classification model;
judging whether the quality grade is larger than a preset grade, and if so, meeting preset conditions.
In some embodiments, determining whether the wearable device is in a wearing state according to a determination result of whether a preset condition is satisfied includes:
determining whether the wearable device is in a wearing state based on detection data of a single sensor;
or, whether the wearing device is in a wearing state is determined based on detection data of more than two sensors.
In some embodiments, the determining whether the wearing state is based on the detection data of the two or more sensors includes:
setting a weight value of each detection data according to the data characteristics of each detection data of the more than two sensors;
calculating a comprehensive value according to each detection data and the corresponding weight value;
judging whether the integrated value is larger than a judging threshold value, and if the integrated value is larger than the judging threshold value, determining that the wearing equipment is in a wearing state;
wherein the two or more sensors comprise PPG signal sensors, the magnitude of the weight value of the PPG signal corresponds to the quality level of the PPG signal, the higher the quality level the greater the weight value.
In some embodiments, the training step of the hierarchical classification model of the PPG signal sensor comprises:
extracting wave crests and wave troughs from PPG signal data, and carrying out segmentation processing on the PPG signal based on the wave crests to obtain a plurality of data segments;
performing cross-correlation operation on each data segment and a plurality of data segments of the signal template in sequence, and determining the number of the data segments meeting the condition;
the number of the data segments meeting the condition is counted to obtain the template quality grade of the PPG signal;
judging the network quality level of the PPG signal by using a trained neural network level classification model;
and determining the quality level of the PPG signal according to the weighted result of the template quality level and the network quality level.
According to another aspect of the present invention, there is provided a wearing state determination device of a wearable apparatus, the device including:
the sensor detection module is suitable for sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period;
the data judging module is suitable for acquiring detection data of the sensor if the sensor of the type exists, and judging whether the detection data meets the preset condition or not;
the state judging module is suitable for judging whether the wearing equipment is in a wearing state according to judging results of whether preset conditions are met or not.
According to still another aspect of the present invention, there is provided an electronic apparatus including: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform a wearing state determination method according to any one of the wearable devices described above.
According to still another aspect of the present invention, there is provided a computer-readable storage medium storing one or more programs that, when executed by a processor, implement a wearing state determination method according to any one of the above-described wearing devices.
According to the technical scheme disclosed by the invention, the general judgment method for detecting the wearing equipment with different configurations is provided, wherein whether a certain type of sensor exists or not is detected, when the sensor exists, the detection data of the sensor is obtained, the detection data is processed and judged, and the wearing equipment is judged to be in a wearing state after comprehensive analysis and calculation, so that the universality of the wearing state judgment and the judgment accuracy are improved.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flowchart of a wearing state determination method of a wearable device according to an embodiment of the present invention;
FIG. 2 illustrates a flow diagram for sequentially determining wear status from a single sensor according to one embodiment of the invention;
fig. 3 shows a flow diagram of PPG signal level classification model training according to an embodiment of the invention;
fig. 4 is a schematic diagram showing a configuration of a wearing state determination device of a wearing apparatus according to an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flowchart of a wearing state determining method of a wearable device according to an embodiment of the present invention, where the method is applied to an electronic device, including being applied to the wearable device itself, and may also be used for other electronic devices other than the wearable device and connected to the wearable device: including a smart terminal device, a computer device, and/or a cloud, in which a computer program is installed, including but not limited to a smart phone, PAD; the computer device includes, but is not limited to, a personal computer, a notebook computer, an industrial computer, a network host, a single network server, a plurality of network server sets; the Cloud is composed of a large number of computers or network servers based on Cloud Computing (Cloud Computing), which is one of distributed Computing, a virtual supercomputer composed of a group of loosely coupled computer sets.
As shown in fig. 1, the wearing state determining method of the wearable device in the embodiment of the present invention specifically includes the following steps:
step S110, sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period;
step S120, if the existence of the sensor is detected, acquiring detection data of the sensor, and judging whether the detection data meets a preset condition;
step S130, judging whether the wearing equipment is in a wearing state according to a judging result of whether the preset condition is met.
In the specific determination, first, whether a certain type of sensor exists is detected, for example, whether a skin electric activity sensor or an oxygen blood sensor exists is detected, if so, whether the detection data obtained from the sensor is in a threshold range is further determined, and if so, the sensor is in a wearing state.
In other embodiments, after the detection data of the plurality of sensors are explicitly obtained, each detection data is recorded, whether each detection data meets a condition is primarily determined, a comprehensive analysis is performed on a result of the primary determination, and then whether the wearing device is in a wearing state is determined according to a result of the comprehensive analysis.
In summary, the judging methods disclosed in the above two embodiments in this embodiment can judge the wearing state without knowing whether a certain sensor exists, thereby improving the universality and accuracy of the judgment.
In some embodiments, the sensor comprises the following types: any one or more of an EDA sensor, an blood oxygen sensor, an acceleration ACC sensor, a gyroscope GYRO sensor, a PPG signal sensor or a temperature sensor can be used for detecting the body temperature, wherein the temperature sensor can be a sensor for detecting the body temperature only or a double-side sensor for detecting the body temperature and the ambient temperature respectively.
In some embodiments, the determining in step S120 whether the detected data meets a preset condition includes:
judging whether the detection data collected from each sensor and the detection data collected from each sensor are within a preset threshold range or not;
if the detection data is within the threshold range, the preset condition is met, otherwise, the preset condition is not met;
in another embodiment, particularly for PPG signal sensors, gyroscopic GYRO sensors, etc., the determining whether the detection data meets a preset condition comprises:
determining the quality grade of the detection data according to a preset algorithm model;
and if the quality grade is above a preset grade, a preset condition is met.
Of course, only the quality level may be obtained in the above steps, and then in step S130, it may be determined whether or not the wearing state is being performed according to the level of the quality level.
In some embodiments, when the sensor is a PPG signal sensor, step S120 includes, if it is detected that the sensor of the type exists, acquiring detection data of the sensor, and determining whether the detection data meets a preset condition specifically includes:
collecting PPG signal data of the PPG signal sensor in a single detection period;
inputting the PPG signal data into a pre-trained grade classification model, and obtaining the quality grade of the PPG signal from the grade classification model;
judging whether the quality grade is larger than a preset grade, if so, meeting preset conditions, or setting the conditions larger than or equal to the preset conditions for judgment.
In some specific embodiments, the quality level may be divided into three or more levels, and when the quality level is two or more levels, the preset condition is satisfied, which indicates that the quality of the PPG signal is better, and the possibility of being in a wearing state is high. Experiments prove that the accuracy of the result obtained by analyzing the quality of the PPG signal is obviously higher than that of the result obtained by analyzing the heart rate, namely the judgment result according to the quality of the PPG signal is obviously better than that of the heart rate, so that erroneous judgment is avoided.
In some embodiments, determining whether the wearable device is in the wearing state in step S130 according to the determination result of whether the preset condition is satisfied includes:
whether the wearing device is in a wearing state is determined based on detection data of the single sensor.
In a specific embodiment, taking as an example whether the smart bracelet/watch is in a wearing state, firstly, setting a detection period as T, wherein the range of the value of T is 5-15 seconds, then sequentially judging whether an electrodermal activity EDA sensor, an oximetry sensor, an acceleration ACC sensor, a gyroscope GYRO sensor, a PPG signal sensor or a temperature sensor exists, and if one of the sensors exists, judging whether the wearing device is in the wearing state according to the detection data of the one type.
In another embodiment, whether the wearing state is in the wearing state is comprehensively determined according to detection data of more than two sensors, namely, whether the wearing device is in the wearing state is comprehensively determined after the plurality of sensor data are obtained.
Specifically, determining whether or not in the wearing state based on the detection data of the two or more sensors includes:
according to the data characteristics of the detection data of the more than two sensors, particularly the data accuracy of the sensors, the importance of the sensors to wearing equipment and the like, weight values of the detection data are set; preferably, when a PPG signal sensor is present, the weight value of the PPG signal sensor is set to be larger than that of the other sensors, so that the weight of the PPG signal sensor is tilted.
Calculating a comprehensive value according to each detection data and the corresponding weight value;
judging whether the integrated value is larger than a judging threshold value, and if the integrated value is larger than the judging threshold value, determining that the wearing equipment is in a wearing state.
For example, when there are a skin electrical activity sensor, an blood oxygen sensor, a temperature sensor, and an acceleration sensor, the weight values thereof are respectively: 0.2, 0.4, and the sum of the weights is 1, and if the integrated value of each sensor is 0.7 or more, it can be determined that the wearing state is present.
Optionally, in the case that the two or more sensors include PPG signal sensors, the magnitude of the weight value of the PPG signal corresponds to a quality level of the PPG signal, the higher the quality level, the greater the weight value.
In one embodiment, there are a skin electric activity sensor, an blood oxygen sensor, an acceleration sensor and a PPG signal sensor on the wearable device, wherein the weight values of the skin electric activity sensor, the blood oxygen sensor, the acceleration sensor and the PPG signal sensor are respectively 0.1, 0.2 and 0.6, the quality grade of the PPG signal is three-grade, the grade weight of the first grade is 0.2, the grade weight of the second grade is 0.6, and the grade weight of the third grade is 1. The actual weight of the PPG signal quality level in each sensor is 0.6x0.2, i.e. 0.12, when the PPG signal quality level is two levels, 0.6x0.6, i.e. 0.36, and when the PPG signal quality level is three levels, 0.6x1, i.e. 0.6. Of course, the above arrangement is merely exemplary, and other alternatives are within the scope of the embodiments of the present invention.
In some embodiments, referring to fig. 3, the training step of the hierarchical classification model of the PPG signal sensor includes:
step S310, extracting wave crests and wave troughs from PPG signal data, and carrying out segmentation processing on the PPG signal based on the wave crests to obtain a plurality of data segments; optionally, the filtered PPG signal is segmented, and each segment of data takes 1 second before to 1 second after the peak index point.
Step S320, performing cross-correlation operation on each data segment and a plurality of data segments of the signal template in sequence, and determining the number of the data segments meeting the condition.
Specifically, for a certain data segment of the plurality of processed data segments, this is denoted here as the current data segment. And carrying out cross-correlation operation on the current data segment and a plurality of data segments of the signal template (assuming that the signal template has 6 data segments in total) to respectively obtain a plurality of cross-correlation values (assuming that the obtained plurality of cross-correlation values are 0.8,0.9,0.5 and 0.9,0.9,0.9 respectively), wherein the cross-correlation condition can comprise that the cross-correlation value is > 0.8. Therefore, it is known that the number of data segments satisfying the cross-correlation condition among the plurality of data segments of the template is determined to be 4 for the current data segment.
Step S330, obtaining the template quality level of the PPG signal according to the number of the data segments satisfying the condition. For example, when the number of data segments satisfying the condition is 1 to 3, the quality level thereof is one level, when the number of data segments satisfying the condition is 4 to 5, the quality level thereof is two levels, and when the number of data segments satisfying the condition is more than 6, the quality level thereof is three levels.
Step S340, determining the network quality level of the PPG signal by using the trained neural network level classification model. The neural network may be a convolutional neural network, and the training steps of the neural network include data labeling, data preprocessing, network model building, data training, data verification and the like, which are not repeated here.
Step S350, determining a quality level of the PPG signal according to the weighted result of the template quality level and the network quality level.
According to this embodiment, the PPG signal quality level is weighted by the result of integrating both the template quality level and the network quality level, for example, the template quality level is weighted by 0.4 and the network quality level is weighted by 0.6, but of course, the weight may change with the change of the neural network learning time, and the longer the time, the more depending on the result of the deep learning.
The trained grade classification model is obtained through the steps, and it is pointed out that the model is optimized continuously along with the increase of the judgment data, and the accuracy of the evaluation of the PPG signal quality grade is improved.
According to another aspect of the present invention, referring to fig. 4, there is provided a wearing state determining apparatus of a wearable device, the apparatus 400 including:
the sensor detection module 410 is adapted to sequentially detect whether a certain type of sensor exists in the wearable device in a detection period;
the data judging module 420 is adapted to acquire detection data of the sensor if the sensor of the type is detected, and judge whether the detection data meets a preset condition;
the state determining module 430 is adapted to determine whether the wearable device is in a wearing state according to a determination result of whether a preset condition is satisfied.
The device 400 can judge the wearing state under the condition that whether a certain sensor exists or not is unclear, so that the universality and the accuracy of judgment are improved.
In some embodiments, the sensor comprises any one or more of a skin electrical activity sensor, a blood oxygen sensor, an acceleration sensor, a gyroscope sensor, a PPG signal sensor, or a temperature sensor.
In some embodiments, the determining in the data determining module 420 whether the detected data meets the preset condition includes:
judging whether the detected data and the detected data are within a preset threshold range or not;
if the detection data is within the threshold range, the preset condition is met, otherwise, the preset condition is not met;
or, the judging whether the detection data meets the preset condition comprises:
determining the quality grade of the detection data according to a preset algorithm model;
if the quality grade is above the preset grade, the preset condition is met, otherwise, the preset condition is not met.
In some embodiments, when the sensor is a PPG signal sensor, then the data determination module 420 is further adapted to:
collecting PPG signal data of the PPG signal sensor in a single detection period;
inputting PPG signal data into a pre-trained grade classification model, and obtaining the quality grade of the PPG signal from the grade classification model;
judging whether the quality grade is larger than a preset grade, and if so, meeting preset conditions.
In some embodiments, the status determination module 430 is further adapted to:
determining whether the wearable device is in a wearing state based on detection data of a single sensor;
or, whether the wearing device is in a wearing state is determined based on detection data of more than two sensors.
In some embodiments, the determining in the state determination module 430 whether the wearing state is based on the detection data of the more than two sensors comprises:
setting a weight value of each detection data according to the data characteristics of each detection data of the more than two sensors;
calculating a comprehensive value according to each detection data and the corresponding weight value;
judging whether the integrated value is larger than a judging threshold value, and if the integrated value is larger than the judging threshold value, determining that the wearing equipment is in a wearing state;
wherein the two or more sensors comprise PPG signal sensors, the magnitude of the weight value of the PPG signal corresponds to the quality level of the PPG signal, the higher the quality level the greater the weight value.
In some optional embodiments, the training step of the hierarchical classification model of the PPG signal sensor comprises:
extracting wave crests and wave troughs from PPG signal data, and carrying out segmentation processing on the PPG signal based on the wave crests to obtain a plurality of data segments;
performing cross-correlation operation on each data segment and a plurality of data segments of the signal template in sequence, and determining the number of the data segments meeting the condition;
the number of the data segments meeting the condition is counted to obtain the template quality grade of the PPG signal;
judging the network quality level of the PPG signal by using a trained neural network level classification model;
and determining the quality level of the PPG signal according to the weighted result of the template quality level and the network quality level.
It should be noted that, the specific implementation manner of each embodiment of the apparatus may be performed with reference to the specific implementation manner of the corresponding embodiment of the method, which is not described herein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may also be used with the teachings herein. The required structure for the construction of such devices is apparent from the description above. In addition, the present invention is not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functions of some or all of the components in the wearing state determination device of the wearable apparatus according to the embodiment of the present invention may be implemented in practice using a microprocessor or a Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the wearing state judging method of the wearing equipment in any of the method embodiments.
Fig. 5 shows a schematic structural diagram of an embodiment of the electronic device according to the present invention, and the embodiment of the present invention is not limited to the specific implementation of the electronic device.
As shown in fig. 5, the electronic device may include: a processor 502, a communication interface (Communications Interface) 504, a memory 506, and a communication bus 508.
Wherein: processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508. A communication interface 504 for communicating with network elements of other devices, such as clients or other servers. The processor 502 is configured to execute the program 510, and may specifically perform relevant steps in the wearing state determining method embodiment of the wearable device for an electronic device.
In particular, program 510 may include program code including computer-operating instructions.
The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included in the electronic device may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
A memory 506 for storing a program 510. Memory 506 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may be specifically configured to cause the processor 502 to perform operations corresponding to the wearing state determining method embodiment of the wearable device.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names.
Claims (7)
1. A wearing state determination method of a wearable device, the method comprising:
sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period;
if the sensor of the type is detected to exist, acquiring detection data of the sensor, and judging whether the detection data meets a preset condition or not;
judging whether the wearing equipment is in a wearing state according to a judging result of whether a preset condition is met or not;
the sensor comprises any one or more of a skin electric activity sensor, a blood oxygen sensor, an acceleration sensor, a gyroscope sensor, a PPG signal sensor and a temperature sensor;
wherein, the judging whether the detection data meets the preset condition comprises:
judging whether the detected data and the detected data are within a preset threshold range or not;
if the detection data is within the threshold range, the preset condition is met, otherwise, the preset condition is not met; or alternatively, the process may be performed,
determining the quality grade of the detection data according to a preset algorithm model;
if the quality grade is above the preset grade, the preset condition is met, otherwise, the preset condition is not met;
wherein, according to the judging result of whether the preset condition is met, judging whether the wearing equipment is in a wearing state comprises:
determining whether the wearable device is in a wearing state based on detection data of a single sensor; or, whether the wearing device is in a wearing state is determined based on detection data of more than two sensors.
2. The method according to claim 1, wherein when the sensor is a PPG signal sensor, if the presence of the type of sensor is detected, acquiring detection data of the sensor, and determining whether the detection data meets a preset condition specifically includes:
collecting PPG signal data of the PPG signal sensor in a single detection period;
inputting PPG signal data into a pre-trained grade classification model, and obtaining the quality grade of the PPG signal from the grade classification model;
judging whether the quality grade is larger than a preset grade, and if so, meeting preset conditions.
3. The method of claim 2, wherein determining whether the wearing state is based on detection data of the two or more sensors comprises:
setting a weight value of each detection data according to the data characteristics of each detection data of the more than two sensors;
calculating a comprehensive value according to each detection data and the corresponding weight value;
judging whether the integrated value is larger than a judging threshold value, and if the integrated value is larger than the judging threshold value, determining that the wearing equipment is in a wearing state;
wherein the two or more sensors comprise PPG signal sensors, the magnitude of the weight value of the PPG signal corresponds to the quality level of the PPG signal, the higher the quality level the greater the weight value.
4. A method according to claim 3, wherein the training step of the hierarchical classification model of the PPG signal sensor comprises:
extracting wave crests and wave troughs from PPG signal data, and carrying out segmentation processing on the PPG signal based on the wave crests to obtain a plurality of data segments;
performing cross-correlation operation on each data segment and a plurality of data segments of the signal template in sequence, and determining the number of the data segments meeting the condition;
the number of the data segments meeting the condition is counted to obtain the template quality grade of the PPG signal;
judging the network quality level of the PPG signal by using a trained neural network level classification model;
and determining the quality level of the PPG signal according to the weighted result of the template quality level and the network quality level.
5. A wearing state determination device of a wearable apparatus, the device comprising:
the sensor detection module is suitable for sequentially detecting whether a certain type of sensor exists in the wearable equipment or not in a detection period;
the data judging module is suitable for acquiring detection data of the sensor if the sensor of the type exists, and judging whether the detection data meets the preset condition or not;
the state judging module is suitable for judging whether the wearing equipment is in a wearing state according to a judging result of whether the preset condition is met or not;
the sensor comprises any one or more of a skin electric activity sensor, a blood oxygen sensor, an acceleration sensor, a gyroscope sensor, a PPG signal sensor and a temperature sensor;
the data determination module is further adapted to:
judging whether the detected data and the detected data are within a preset threshold range or not; if the detection data is within the threshold range, the preset condition is met, otherwise, the preset condition is not met; or determining the quality grade of the detection data according to a preset algorithm model; if the quality grade is above the preset grade, the preset condition is met, otherwise, the preset condition is not met;
the state determination module is further adapted to:
determining whether the wearable device is in a wearing state based on detection data of a single sensor; or, whether the wearing device is in a wearing state is determined based on detection data of more than two sensors.
6. An electronic device, wherein the electronic device comprises: a processor; and a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the wearing state determination method of a wearable device according to any one of claims 1-4.
7. A computer-readable storage medium storing one or more programs that, when executed by a processor, implement the wearing state determination method of a wearable device according to any one of claims 1-4.
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