Smart bracelet detection method and system based on adaptive regression model, and medium

Abstract

The invention discloses a smart bracelet detection method and system based on an adaptive regression model, and a medium, and mainly relates to the technical field of smart bracelets. The method and the device are used for solving the problems that existing early warning schemes mostly depend on servers and network communication, early warning models are mostly static or fixed parameter models, and accurate early warning cannot be provided for health conditions and operation characteristics of different users. Comprising the following steps: collecting various physiological sign information under the health condition of a user in a preset operation time period, and further obtaining an average value and a standard deviation corresponding to the various physiological sign information; according to the relation between the current physiological sign information and the average value and the standard deviation, determining a weight value corresponding to the current physiological sign information, and further obtaining a calculation weight sum to determine whether the current sign is normal or not; when the current physical sign is abnormal, starting a preset first early warning mechanism; and when the current physical sign is normal, whether mutation exists or not is determined, and when mutation exists, a preset second early warning mechanism is started.

Description

Technical Field

[0001] The present application relates to the technical field of smart bracelet detection, and in particular to a smart bracelet detection method, system and medium based on an adaptive regression model. Background Art

[0002] A smart bracelet is a lightweight and portable wearable electronic device that integrates sensors, microprocessors and wireless communication technologies to achieve real-time monitoring and management of users' health status, exercise data, message reminders and other aspects.

[0003] The existing detection scheme of smart bracelets is: smart bracelets can monitor users' real-time health data in real time and continuously through built-in photoelectric sensors, blood pressure and blood oxygen monitoring sensors, and acceleration sensors related to sleep monitoring. By transmitting users' real-time health data to the server, and then through the preset early warning model built into the server, real-time monitoring and early warning of users' health data in different states such as exercise and rest, and then timely discover abnormal health data, and then provide users with timely health warnings.

[0004] Although existing technologies can achieve a certain degree of health monitoring, they have the following shortcomings: First, most health warning solutions rely on servers and network communications, uploading collected data to servers and relying on servers to process and analyze health data; second, warning models are mostly static or fixed parameter models, which are difficult to adapt to the dynamic changes of the physiological state and environmental factors of power workers; third, the warning system lacks personalization and intelligence, and cannot provide accurate warnings based on the health status and work characteristics of different workers. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present application provides a smart bracelet detection method, system and medium based on an adaptive regression model to solve the problem that most existing health warning solutions rely on servers and network communications, and the warning models are mostly static or fixed parameter models, which are difficult to adapt to the dynamic changes in the physiological state and environmental factors of power workers, and cannot provide accurate warnings based on the health conditions and operating characteristics of different users.

[0006] In the first aspect, the present application provides a smart bracelet detection method based on an adaptive regression model, the method comprising: using the built-in sensor of the smart bracelet, collecting various physiological sign information of the user under the health status within a preset working time period, and then obtaining the average value and standard deviation corresponding to each physiological sign information; using the built-in sensor of the smart bracelet, collecting the user's current physiological sign information; according to the relationship between the current physiological sign information and the average value and standard deviation, determining the weight value corresponding to the current physiological sign information, and then obtaining the calculated weight sum; comparing the preset sign weight sum corresponding to the current user with the calculated weight sum to determine whether the current sign is normal; when the current sign is abnormal, starting the preset first warning mechanism; when the current sign is normal, determining whether there is a mutation based on the current physiological sign information, the average value, and the preset mutation threshold, and when there is a mutation, starting the preset second warning mechanism.

[0007] Furthermore, the user's various physiological signs information within the preset working time period is collected, and then the average value and standard deviation corresponding to each physiological sign information are obtained, specifically including: through the formula:

[0008] Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Represents the specific value of each physiological sign information of the ith part; through the formula:

[0009] Represents standard deviation.

[0010] Furthermore, according to the relationship between the current physiological sign information and the average value and the standard deviation, the weight value corresponding to the current physiological sign information is determined, and then the total weight is calculated, specifically including: by the formula:

[0011] Calculate the weight value corresponding to the current physiological sign information );in, 、 、 、 is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, xRepresents the current physiological sign information; the total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information; wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

[0012] Furthermore, when the current vital signs are abnormal, a preset first warning mechanism is activated, specifically including: when the current vital signs are abnormal, the built-in vibration and ringing components of the smart bracelet are activated; when the built-in rescue trigger device of the smart bracelet is triggered within the preset waiting time period after the built-in vibration and ringing components of the smart bracelet are activated, the smart bracelet sends a call for help information to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help information to the bound terminals; when the built-in rescue trigger device of the smart bracelet is not triggered within the preset waiting time period, the current physiological sign information is added to the various physiological sign information under the user's health status, and then the corresponding average value and standard deviation of each physiological sign information are updated.

[0013] Further, based on the current physiological sign information, the average value, and the preset mutation threshold, it is determined whether there is a mutation, specifically including: using the formula:

[0014] Calculate the absolute difference between the current physiological sign information and the average value; where, Indicates k Current physiological sign information, Indicates the average value of current physiological signs information. k Belongs to [1, K], where K represents the total number of current physiological sign information items; when the absolute difference of any physiological sign information item is greater than the preset mutation threshold, it is determined that a mutation exists.

[0015] Furthermore, when there is a mutation, a preset second warning mechanism is activated, specifically including: when there is a mutation, the built-in vibration and ringing components of the smart bracelet are activated; when the built-in rescue trigger device of the smart bracelet is triggered within the preset trigger time period after the built-in vibration and ringing components of the smart bracelet are activated, the smart bracelet sends a call for help message to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help message to the bound terminals; when the built-in rescue trigger device of the smart bracelet is not triggered within the preset trigger time period, the number of non-triggered times is recorded, and when the number of non-triggered times is greater than the preset number, the preset mutation threshold corresponding to the physiological sign information that triggers the mutation is increased by a preset adjustment value.

[0016] In the second aspect, the present application provides a smart bracelet detection system based on an adaptive regression model, and the system includes: an acquisition module, which is used to collect various physiological signs information of the user under the health status of a preset working time period through the built-in sensor of the smart bracelet, and then obtain the average value and standard deviation corresponding to each physiological sign information; an acquisition module, which is used to collect the user's current physiological sign information through the built-in sensor of the smart bracelet; a weight module, which is used to determine the weight value corresponding to the current physiological sign information according to the relationship between the current physiological sign information and the average value and standard deviation, and then obtain the calculated weight sum; a determination module, which is used to compare the preset sign weight sum corresponding to the current user with the calculated weight sum to determine whether the current sign is normal; an early warning module, which is used to start the preset first early warning mechanism when the current sign is abnormal; when the current sign is normal, it is determined whether there is a mutation based on the current physiological sign information, the average value, and the preset mutation threshold, and when there is a mutation, the preset second early warning mechanism is started.

[0017] Furthermore, the obtaining module includes a calculation unit for obtaining the following equation:

[0018] Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Indicates i The specific value of each physiological sign information; through the formula:

[0019] in, Represents standard deviation.

[0020] Furthermore, the weight module includes a weight calculation unit for calculating the weight by the formula:

[0021] Calculate the weight value corresponding to the current physiological sign information );in, , , , is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, x Represents the current physiological sign information; the total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information; wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

[0022] In a third aspect, the present application provides a non-volatile computer storage medium on which computer instructions are stored. When the computer instructions are executed, a smart bracelet detection method based on an adaptive regression model as described above is implemented.

[0023] Those skilled in the art can understand that the present application has at least the following beneficial effects: The calculation scheme involved in this application is implemented in a smart bracelet, which solves the problem that most existing health warning solutions rely on servers and network communications. In addition, the user's current physiological sign information is collected; based on the relationship between the current physiological sign information and the average value and standard deviation, the weight value corresponding to the current physiological sign information is determined, so as to provide accurate warnings for the health conditions and operating characteristics of different users. In addition, the weight data involved in this application is determined by the specific value of the current physiological sign information, which solves the problem that the warning models are mostly static or fixed parameter models, which are difficult to adapt to the dynamic changes of the physiological state and environmental factors of power workers. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Some embodiments of the present disclosure are described below with reference to the accompanying drawings, in which: Figure 1 It is a flow chart of a smart bracelet detection method based on an adaptive regression model provided in an embodiment of the present application.

[0025] Figure 2 It is a schematic diagram of the internal structure of a smart bracelet detection system based on an adaptive regression model provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] It should be understood by those skilled in the art that the embodiments described below are only preferred embodiments of the present disclosure, and do not mean that the present disclosure can only be implemented through the preferred embodiments. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided by the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work should still fall within the protection scope of the present disclosure.

[0027] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0028] The technical solution proposed in the embodiments of the present application is described in detail below with reference to the accompanying drawings.

[0029] The present application embodiment provides a smart bracelet detection method based on an adaptive regression model, such as Figure 1 As shown, the method provided in the embodiment of the present application mainly includes the following steps: Step 110: The built-in sensor of the smart bracelet is used to collect various physiological sign information of the user's health status during a preset working time period, and then the average value and standard deviation corresponding to each physiological sign information are obtained.

[0030] It should be noted that the physiological sign information at least includes physiological sign information such as heart rate, blood pressure, blood oxygen, and body temperature.

[0031] Among them, the user's various physiological signs information within the preset working time period is collected, and then the average value and standard deviation corresponding to each physiological sign information are obtained, which can be specifically: By formula:

[0032] Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Represents the specific value of each i-th physiological sign information.

[0033] By formula:

[0034] in, Represents standard deviation.

[0035] Step 120: Collect the user's current physiological sign information through the built-in sensor of the smart bracelet.

[0036] Step 130: Determine the weight value corresponding to the current physiological sign information according to the relationship between the current physiological sign information and the mean value and the standard deviation, and then obtain the calculated weight sum.

[0037] This step can be specifically as follows: By formula:

[0038] Calculate the weight value corresponding to the current physiological sign information );in, 、 、 、 is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, x Represents the current physiological sign information; the total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information; wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

[0039] Step 140: Compare the preset vital sign weight sum corresponding to the current user with the calculated weight sum to determine whether the current vital sign is normal.

[0040] It should be noted that the preset sum of the weights of the physical signs is a preset constant, which can be obtained by those skilled in the art through multiple experiments. When the preset sum of the weights of the physical signs is greater than the calculated sum of the weights, it is determined that the current physical signs are normal, otherwise they are abnormal.

[0041] Step 150: When the current vital signs are abnormal, the preset first warning mechanism is activated; when the current vital signs are normal, based on the current physiological vital sign information, the average value, and the preset mutation threshold, it is determined whether there is a mutation. When a mutation exists, the preset second warning mechanism is activated.

[0042] Among them, when the current physical signs are abnormal, the preset first warning mechanism is activated, which can be specifically: When the current vital signs are abnormal, the built-in vibration and ringing components of the smart bracelet are activated; when the built-in rescue trigger device of the smart bracelet is triggered within the preset waiting time period after the built-in vibration and ringing components of the smart bracelet are activated, the smart bracelet sends a call for help information to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help information to the bound terminals; when the built-in rescue trigger device of the smart bracelet is not triggered within the preset waiting time period, the current physiological vital signs information is added to the various physiological vital signs information under the user's health status, and then the corresponding average value and standard deviation of each physiological sign information are updated.

[0043] Wherein, based on the current physiological sign information, the average value, and the preset mutation threshold, it is determined whether there is a mutation, which can be specifically: By formula:

[0044] Calculate the absolute difference between the current physiological sign information and the average value; where, Indicates k Current physiological sign information, Indicates the average value of current physiological signs information. k Belongs to [1, K], where K represents the total number of current physiological sign information items; when the absolute difference of any physiological sign information item is greater than the preset mutation threshold, it is determined that a mutation exists.

[0045] When a mutation occurs, the preset second warning mechanism is activated, which may be: When there is a mutation, the vibration and ringing components built into the smart bracelet are started; when the built-in rescue trigger device of the smart bracelet is triggered within the preset trigger time period after the built-in vibration and ringing components of the smart bracelet are started, the smart bracelet sends a call for help message to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help message to the bound terminals; when the built-in rescue trigger device of the smart bracelet is not triggered within the preset trigger time period, the number of non-triggering times is recorded, and when the number of non-triggering times is greater than the preset number, the preset mutation threshold corresponding to the physiological sign information that triggers the mutation is increased by a preset adjustment value.

[0046] Based on the above description, this application proposes a smart bracelet detection method based on an adaptive regression model, which can collect and analyze the user's health data in real time, and immediately issue abnormal warnings to ensure the safety of staff; without relying on servers, health management is more efficient and convenient, bringing users a full range of health protection experience.

[0047] In addition, the mathematical model constructed based on personal vital signs information can accurately reflect each person's unique physiological characteristics and health status, providing individuals with tailored health management and intervention strategies.

[0048] In addition, by determining whether there is a mutation based on current physiological signs information, average values, and preset mutation thresholds, it is able to capture mutations and abnormal fluctuations in physiological indicators, timely assess potential risks and issue alarms. This algorithm ensures that when physiological indicators are abnormal, it can respond quickly and take measures to effectively protect the health and safety of the wearer.

[0049] besides, Figure 2 A smart bracelet detection system based on an adaptive regression model is provided in the embodiment of the present application. Figure 2 As shown, the system provided in the embodiment of the present application mainly includes: The acquisition module 210 is used to collect various physiological sign information of the user's health status within a preset working time period through the built-in sensor of the smart bracelet, and then obtain the average value and standard deviation corresponding to each physiological sign information.

[0050] The obtaining module 210 includes a calculation unit for obtaining the following equation:

[0051] Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Indicates iSpecific values ​​of each physiological sign information.

[0052] By formula:

[0053] Represents standard deviation.

[0054] The collection module 220 is used to collect the user's current physiological sign information through the built-in sensor of the smart bracelet.

[0055] The weight module 230 is used to determine the weight value corresponding to the current physiological sign information according to the relationship between the current physiological sign information and the average value and the standard deviation, and then obtain the calculated weight sum.

[0056] The weight module 230 includes a weight calculation unit for calculating the weight using the formula:

[0057] Calculate the weight value corresponding to the current physiological sign information );in, , , , is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, x Represents the current physiological sign information; the total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information; wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

[0058] The determination module 240 is used to compare the preset vital sign weight sum corresponding to the current user with the calculated weight sum to determine whether the current vital sign is normal.

[0059] The warning module 250 is used to activate the preset first warning mechanism when the current vital signs are abnormal; when the current vital signs are normal, determine whether there is a mutation based on the current physiological sign information, the average value, and the preset mutation threshold, and when there is a mutation, activate the preset second warning mechanism.

[0060] In addition, an embodiment of the present application further provides a non-volatile computer storage medium on which executable instructions are stored. When the executable instructions are executed, a smart bracelet detection method based on an adaptive regression model as described above is implemented.

[0061] So far, the technical solutions of the present disclosure have been described in combination with the above multiple embodiments, but it is easy for those skilled in the art to understand that the protection scope of the present disclosure is not limited to these specific embodiments. Without departing from the technical principles of the present disclosure, those skilled in the art can split and combine the technical solutions in the above-mentioned various embodiments, and can also make equivalent changes or replacements to the relevant technical features. Any changes, equivalent replacements, improvements, etc. made within the technical concept and / or technical principle of the present disclosure will fall within the protection scope of the present disclosure.

Claims

1. A smart bracelet detection method based on an adaptive regression model, characterized in that: The method comprises: Through the built-in sensor of the smart bracelet, various physiological signs information of the user's health status during the preset working time period are collected, and then the average value and standard deviation corresponding to each physiological sign information are obtained; The user's current physiological signs information is collected through the built-in sensors of the smart bracelet; According to the relationship between the current physiological sign information and the average value and the standard deviation, the weight value corresponding to the current physiological sign information is determined, and then the total weight is calculated; Compare the sum of preset vital signs weights corresponding to the current user with the sum of calculated weights to determine whether the current vital signs are normal; When the current vital signs are abnormal, the preset first warning mechanism is activated; when the current vital signs are normal, based on the current physiological vital signs information, the average value, and the preset mutation threshold, it is determined whether there is a mutation. When a mutation exists, the preset second warning mechanism is activated.

2. The smart bracelet detection method based on the adaptive regression model according to claim 1, characterized in that: Collect the user's various physiological signs information within the preset working time period, and then obtain the average value and standard deviation corresponding to each physiological sign information, including: By formula: Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Represents the specific value of each physiological sign information of the ith part; By formula: Represents standard deviation.

3. The smart bracelet detection method based on the adaptive regression model according to claim 1 is characterized in that: According to the relationship between the current physiological sign information and the mean value and standard deviation, the weight value corresponding to the current physiological sign information is determined, and then the total weight is calculated, which specifically includes: By formula: Calculate the weight value corresponding to the current physiological sign information ); in, 、 、 、 is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, x Indicates current physiological sign information; The total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information, wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

4. The smart bracelet detection method based on the adaptive regression model according to claim 1 is characterized in that: When the current physical signs are abnormal, the preset first warning mechanism is activated, including: When the current vital signs are abnormal, the built-in vibration and ringing components of the smart bracelet are activated; When the built-in rescue trigger device of the smart bracelet is triggered within the preset waiting time period after the vibration and ringing components of the smart bracelet are activated, the smart bracelet sends a call for help message to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help message to the bound terminals; When the built-in rescue trigger device of the smart bracelet is not triggered within the preset waiting time period, the current physiological sign information is added to the various physiological sign information under the user's health status, and then the mean value and standard deviation corresponding to each physiological sign information are updated.

5. The smart bracelet detection method based on the adaptive regression model according to claim 1 is characterized in that: Based on the current physiological sign information, average value, and preset mutation threshold, determine whether there is a mutation, including: By formula: Calculate the absolute difference between the current physiological sign information and the average value; in, Indicates k Current physiological sign information, Indicates the average value of current physiological signs information. k Belongs to [1, K], where K represents the total number of items of current physiological sign information; When the absolute difference of any physiological sign information is greater than a preset mutation threshold, it is determined that a mutation exists.

6. The smart bracelet detection method based on the adaptive regression model according to claim 1 is characterized in that: When there is a mutation, the preset second warning mechanism is activated, including: When there is a sudden change, the vibration and ringing components built into the smart bracelet are activated; When the built-in rescue trigger device of the smart bracelet is triggered within the preset trigger time period after the built-in vibration and ringing components of the smart bracelet are activated, the smart bracelet sends a call for help message to the preset health center, obtains several bound terminals corresponding to the current smart bracelet through the preset health center, and sends a preset call for help message to the bound terminals; When the built-in rescue trigger device of the smart bracelet is not triggered within the preset trigger time period, the number of non-triggering times is recorded. When the number of non-triggering times is greater than the preset number, the preset mutation threshold corresponding to the physiological sign information that triggers the mutation is increased by a preset adjustment value.

7. A smart bracelet detection system based on an adaptive regression model, characterized in that: The system comprises: The acquisition module is used to collect various physiological signs information of the user's health status during a preset working time period through the built-in sensor of the smart bracelet, and then obtain the average value and standard deviation corresponding to each physiological sign information; The acquisition module is used to collect the user's current physiological sign information through the built-in sensor of the smart bracelet; A weight module is used to determine the weight value corresponding to the current physiological sign information according to the relationship between the current physiological sign information and the average value and standard deviation, and then obtain the calculated weight sum; A determination module is used to compare the sum of the preset vital signs weights corresponding to the current user with the sum of the calculated weights to determine whether the current vital signs are normal; The early warning module is used to activate the preset first early warning mechanism when the current vital signs are abnormal; when the current vital signs are normal, determine whether there is a mutation based on the current physiological sign information, average value, and preset mutation threshold, and when there is a mutation, activate the preset second early warning mechanism.

8. The smart bracelet detection system based on the adaptive regression model according to claim 7, characterized in that: The acquisition module includes a calculation unit, Used by the formula: Calculate the average value of each physiological sign information , n represents the number of physiological sign information corresponding to the current item within the preset operation time period; Indicates i Specific values ​​of each physiological sign information; By formula: Represents standard deviation.

9. The smart bracelet detection system based on the adaptive regression model according to claim 7, characterized in that: The weight module includes a weight calculation unit, Used by the formula: Calculate the weight value corresponding to the current physiological sign information ); in, , , , is the preset weight value, Indicates the average value of current physiological signs information. Represents the standard deviation of the current physiological sign information, x Indicates current physiological sign information; The total weight is calculated by calculating the sum of the weight values ​​corresponding to each physiological sign information, wherein the physiological sign information is at least divided into heart rate, blood pressure, blood oxygen, and body temperature.

10. A non-volatile computer storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed, the method for detecting a smart bracelet based on an adaptive regression model as described in any one of claims 1 to 6 is implemented.

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