CN116633975A - Smart watch health data monitoring system and method based on cloud computing - Google Patents
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02438—Detecting, measuring or recording pulse rate or heart rate with portable devices, e.g. worn by the patient
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- 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
- A61B5/6802—Sensor mounted on worn items
- A61B5/681—Wristwatch-type devices
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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
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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- H—ELECTRICITY
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- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/72409—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality by interfacing with external accessories
- H04M1/724094—Interfacing with a device worn on the user's body to provide access to telephonic functionalities, e.g. accepting a call, reading or composing a message
- H04M1/724095—Worn on the wrist, hand or arm
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- H04M—TELEPHONIC COMMUNICATION
- H04M1/00—Substation equipment, e.g. for use by subscribers
- H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
- H04M1/724—User interfaces specially adapted for cordless or mobile telephones
- H04M1/72403—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality
- H04M1/72418—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality for supporting emergency services
- H04M1/72421—User interfaces specially adapted for cordless or mobile telephones with means for local support of applications that increase the functionality for supporting emergency services with automatic activation of emergency service functions, e.g. upon sensing an alarm
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application discloses a cloud computing-based intelligent watch health data monitoring system and a cloud computing-based intelligent watch health data monitoring method, which relate to the technical field of intelligent watches and comprise the following steps: the performance data of the intelligent watch sensor is periodically collected through the monitoring system, the performance condition of the intelligent watch sensor is evaluated after the performance data is comprehensively analyzed, whether the intelligent watch sensor supports the use is judged, the evaluation result is fed back to a user through a display module of the intelligent watch, and when the intelligent watch sensor is judged not to support the use, a warning signal is sent out and fed back to the user through the display module of the intelligent watch. The application can timely feed back to the user when the comprehensive performance of the sensor of the intelligent watch is reduced, so that the user can timely know the performance of the sensor of the intelligent watch, and can judge whether the acquired health data is wrong, thereby facilitating the user to perform corresponding treatment when the performance of the sensor is reduced.
Description
Technical Field
The application relates to the technical field of intelligent watches, in particular to an intelligent watch health data monitoring system and method based on cloud computing.
Background
Smart watches are wearable devices that integrate smart functionality, the design of which was initially designed to provide more convenient interaction, personal health management and information presentation, the concept of smart watches was originally traced back to the 80 s of the 20 th century, some science fiction movies have now demonstrated similar concepts, however, until recently, with the rapid development of mobile technology and miniature electronic devices, smart watches have not become a reality, smart watch health data monitoring systems are a health management and monitoring technology based on smart watches, which incorporate sensor technology, data analysis and cloud computing technologies, and are aimed at helping users monitor and manage their health status in real time.
The prior art has the following defects:
the existing monitoring system only collects and analyzes data related to the health of a user through a sensor arranged on the intelligent watch, so that the health condition of the user is judged, however, as the service time of the intelligent watch is prolonged and the environment is influenced, the performance of the sensor is reduced, the monitoring system cannot evaluate the performance of the sensor and feed back the performance to the user, the user cannot know the specific performance of the sensor, and when the performance of the sensor is reduced, the accuracy of the sensor for collecting the health data is reduced, so that the monitoring system can not only alarm by mistake, but also can not alarm timely due to monitoring errors, and the use experience of the user to the intelligent watch is reduced.
Disclosure of Invention
The application aims to provide a cloud computing-based intelligent watch health data monitoring system and method, which are used for solving the defects in the background technology.
In order to achieve the above object, the present application provides the following technical solutions: a smart watch health data monitoring method based on cloud computing, the monitoring method comprising the steps of:
s1: the monitoring system periodically collects performance data of the intelligent watch sensor, and evaluates the performance condition of the intelligent watch sensor after comprehensively analyzing the performance data;
s2: judging whether the intelligent watch sensor supports use or not, and feeding back an evaluation result to a user through a display module of the intelligent watch;
s3: when the intelligent watch sensor is judged to not support the use, sending out a warning signal, and feeding the warning signal back to a user through a display module of the intelligent watch;
s4: when the smart watch sensor is judged to be capable of supporting the use, the sensor collects health data of a user and stores the health data in a memory of the smart watch;
s5: the intelligent watch is connected with the intelligent mobile phone to transmit the acquired health data to the cloud;
s6: the cloud computing platform receives and stores health data from the smart watch, and preprocesses the health data collected from the smart watch;
s7: carrying out various analyses and calculations on the health data through a cloud computing platform analysis tool, and sending an abnormal condition to a user when the abnormal condition is detected;
s8: and displaying the analysis result to a user in a visual mode through the cloud computing platform.
In a preferred embodiment, the monitoring system monitors parameters of the heart rate sensor including the light source power fluctuation rate, the sensor element data acquisition standard deviation and the operating environment temperature, and establishes the heart rate sensor index after removing dimensions from the light source power fluctuation rate, the sensor element data acquisition standard deviation and the operating environment temperatureThe computational expression is:in (1) the->For the light source power fluctuation rate, < >>Standard deviation, < > -for sensing element data acquisition>For the running environmentTemperature. In a preferred embodiment, the heart rate sensor index +.>After that, the heart rate sensor index +.>And comparing the first threshold value with the heart rate sensor index, judging that the heart rate sensor is not supported for use if the heart rate sensor index is smaller than the first threshold value, and judging that the heart rate sensor is supported for use if the heart rate sensor index is larger than or equal to the first threshold value. In a preferred embodiment, the light source power fluctuation rate is calculated as:
in the method, in the process of the application,for real-time acquisition of the light source power, +.>Is a stable operating range of the light source power.
In a preferred embodiment, the calculation expression of the standard deviation of the data acquisition of the sensing element is:
in the formula, i=，/>Representing the number of data acquisitions of the sensor element, +.>Is a positive integer>Representing different data measurements, +.>Mean of the data measurements is shown.
In a preferred embodiment, the monitoring system monitors the accelerometer parameters including the scale factor error, the signal and interference correlation coefficient, and the operating environment temperature, and creates an accelerometer index after removing the scale factor error, the signal and interference correlation coefficient, and the operating environment temperature from dimensionsThe calculation expression is:
in the method, in the process of the application,for signal and interference correlation coefficients, < > and->Is a scale factor error>Is the operating ambient temperature.
In a preferred embodiment, the accelerometer index is obtainedAfter that, accelerometer index ++>And comparing the first threshold value with the second threshold value, judging that the accelerometer index is not supported for use if the accelerometer index is smaller than the second threshold value, and judging that the accelerometer index is supported for use if the accelerometer index is larger than or equal to the second threshold value.
In a preferred embodiment, the signal and interference correlation coefficients are calculated as:
in the method, in the process of the application,and->Observations representing signal and interference, respectively, +.>And->Representing the average of the signal and the interference, respectively.
In a preferred embodiment, the heart rate sensor index is obtainedThe accelerometer indexAverage over a period of time, establishing an evaluation coefficient +.>The computational expression is:
in the method, in the process of the application,represents heart rate sensor index mean, +.>Represents an accelerometer exponential average, j is +.>Heart rate sensor index number acquired over a period of time, and j= {1, 2, 3,..r }, r is a positive integer,representation ofSum of the j-th heart rate sensor indices, < ->Is->Number of accelerometer indices acquired during a time period, and +.>= {1, 2, 3,..once., s }, s is a positive integer,/-a->Represents the sum of the kth accelerometer indices, < +.>In order to start the recording of the time point,for ending the recording time point +.>、/>Proportional coefficients of heart rate sensor index average and accelerometer index average, respectively, and +.>、/>Are all greater than 0; obtaining an evaluation coefficient->After that, the evaluation coefficient->Comparing the evaluation value with an evaluation threshold, if the evaluation coefficient is greater than or equal to the evaluation threshold, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is optimal, and if the evaluation coefficient is less than the evaluation threshold, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is poor。
The application also provides a cloud computing-based intelligent watch health data monitoring system, which comprises an evaluation module, a display module, a warning module, an acquisition module, a transmission module, a processing module, an analysis module and a visualization module: the evaluation module periodically collects performance data of the intelligent watch sensor, after comprehensive analysis is carried out on the performance data, the performance condition of the intelligent watch sensor is evaluated, whether the intelligent watch sensor supports use is judged, the evaluation result is fed back to a user through a display module of the intelligent watch, when the intelligent watch sensor is judged not to support use, the warning module sends out warning signals, the warning signals are fed back to the user through the display module of the intelligent watch, when the intelligent watch sensor is judged to support use, the collection module collects health data of the user and stores the health data in a memory of the intelligent watch, the transmission module is connected with a smart phone, the collected health data is transmitted to a cloud end through the transmission module, the processing module receives and stores the health data from the intelligent watch, the health data collected from the intelligent watch is preprocessed, various analyses and calculation are carried out through the analysis module, when abnormal conditions are detected, the abnormal conditions are sent to the user, and the visual module displays the analysis results to the user in a visual mode. In the technical scheme, the application has the technical effects and advantages that:
1. according to the intelligent watch sensor performance monitoring system, performance data of the intelligent watch sensor are collected regularly through the monitoring system, performance conditions of the intelligent watch sensor are evaluated after comprehensive analysis is carried out on the performance data, whether the intelligent watch sensor supports use or not is judged, an evaluation result is fed back to a user through a display module of the intelligent watch, when the intelligent watch sensor does not support use, a warning signal is sent out, the warning signal is fed back to the user through the display module of the intelligent watch, when the intelligent watch sensor is judged to support use, the sensor collects health data of the user, the monitoring system can timely feed back to the user when the comprehensive performance of the sensor of the intelligent watch is reduced, the user can timely know the performance of the intelligent watch sensor, and accordingly whether the collected health data is wrong or not can be judged, and the user can conveniently conduct corresponding processing when the performance of the sensor is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of the method of the present application.
Description of the embodiments
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments of the present application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Example 1: referring to fig. 1, the method for monitoring health data of a smart watch based on cloud computing according to the present embodiment includes the following steps:
the monitoring system periodically collects performance data of the smart watch sensor, after comprehensively analyzing the performance data, the performance condition of the smart watch sensor is evaluated, whether the smart watch sensor supports use or not is judged, an evaluation result is fed back to a user through a display module of the smart watch, when the smart watch sensor is judged not to support use, an alarm signal is sent out, the alarm signal is fed back to the user through the display module of the smart watch, when the smart watch sensor is judged to support use, the sensor collects health data of the user, such as heart rate, step number and the like, the sensor periodically collects the data and stores the data in a memory in the watch, the smart watch is connected with a smart phone or other devices, the collected health data is transmitted to a cloud end through Bluetooth, wi-Fi or a mobile network and the like, a cloud computing platform receives and stores the health data from the smart watch, the cloud end is organized and managed in a memory system so that subsequent analysis and application are very common steps including data cleaning, noise removal, calibration and the like, and accuracy and consistency of the data collected from the smart watch are ensured. 1) Data screening: firstly, screening the collected original data to remove possible abnormal values or error data, which can be realized by setting a reasonable data range or using an abnormal detection algorithm;
2) Noise removal: for heart rate and step number data, noise is a common interference factor, and filtering techniques (e.g., moving average filtering, median filtering) or signal processing algorithms may be used to remove or reduce the effect of noise on the data;
3) Interpolation of data: in some cases, data loss or loss may occur, for example, due to sensor loss or measurement interruption, in which case interpolation techniques may be used to fill in the missing data to maintain data continuity and integrity;
4) Data calibration: the accuracy and precision of the sensor may deviate, and therefore, data calibration is required to adjust the offset and scaling of the data to match it with standard or accurate reference values;
5) Abnormal data processing: during the data cleaning process, some obvious abnormal data points may be found, which may be caused by sensor faults or other abnormal conditions, abnormal data processing is required, and the abnormal data processing may be deleted or repaired selectively to ensure the quality and accuracy of the data.
Using analysis tools and algorithms on cloud computing platforms to perform various analyses and calculations on health data, the analysis results can help users understand their health status, and when an abnormal situation is detected, the system can send notifications to users so that they take appropriate action, including the steps of:
1) Analysis algorithm selection: selecting an appropriate analysis algorithm and model according to the analyzed target and demand, for example, the health data such as heart rate, step number and the like can be analyzed by using the technologies such as machine learning algorithm, statistical analysis, pattern recognition and the like;
2) Data analysis and calculation: running the selected analysis algorithm and model to perform various analyses and calculations on the health data, for example, indexes such as heart rate variability index, movement track and the like can be calculated to evaluate the health condition of the user;
3) Abnormality detection: according to a preset threshold or model, carrying out abnormality detection on an analysis result, and if abnormal conditions such as abnormal heart rate and abnormal activity level are detected, triggering corresponding notification by a system;
4) And (3) notification transmission: when an abnormal condition is detected, the system can generate a notification and send the notification to the user, and the notification can be sent in the form of display of the smart watch, a mobile application program, an email, a short message and the like to remind the user to pay attention to or seek further medical advice.
Providing personalized advice and guidance, displaying analysis results to a user in a visual mode through a cloud computing platform, wherein the analysis results can be in the form of charts, graphs, dashboards and the like, so that the user can intuitively understand and interpret health data of the user, and the method comprises the following steps of: 1) Visualization tool selection: selecting a suitable visualization tool or library, such as a data visualization tool package (e.g., matplotlib, d3.Js, plotly, etc.) or a visualization platform (e.g., tab, power BI, etc.), for creating interactive and visualized charts, graphs, and dashboards;
2) Visual design: according to the characteristics of analysis results and the requirements of users, a visual interface and a layout are designed, the data are shown by considering the use of proper chart types (such as a line graph, a bar graph, a pie chart, a thermodynamic diagram and the like), and proper colors, fonts, labels and the like are selected to enhance the visual effect;
3) Data binding and presentation: binding the analysis result with the visualization tool, mapping the data to each attribute of the chart or the graph, and setting proper coordinate axes, legends, titles and the like to show the association and trend of the data;
4) Interaction and navigation: adding interactive functions, enabling a user to interact with the visual results, for example, adding mouse-over prompt, zoom, pan, filter, select and the like, so that the user can explore and learn about data as required;
5) And (3) response type design: the adaptation of different equipment and screen sizes is considered, so that the visual interface has good user experience on different platforms and equipment;
6) Publishing and sharing: the visual result is published to a cloud computing platform or a mobile application program so that a user can access and view at any time, and a sharing function is provided, so that the user can share analysis results and reports with other people;
7) User feedback and adjustment: and adjusting and improving the visual interface according to feedback and requirements of users so as to provide better user experience and data display effect.
According to the intelligent watch sensor performance monitoring system, performance data of the intelligent watch sensor are collected regularly through the monitoring system, performance conditions of the intelligent watch sensor are evaluated after comprehensive analysis is carried out on the performance data, whether the intelligent watch sensor supports use or not is judged, an evaluation result is fed back to a user through a display module of the intelligent watch, when the intelligent watch sensor does not support use, a warning signal is sent out, the warning signal is fed back to the user through the display module of the intelligent watch, when the intelligent watch sensor is judged to support use, the sensor collects health data of the user, the monitoring system can timely feed back to the user when the comprehensive performance of the sensor of the intelligent watch is reduced, the user can timely know the performance of the intelligent watch sensor, and accordingly whether the collected health data is wrong or not can be judged, and the user can conveniently conduct corresponding processing when the performance of the sensor is reduced.
Example 2: the monitoring system periodically collects performance data of the intelligent watch sensor, after comprehensive analysis is carried out on the performance data, the performance condition of the intelligent watch sensor is evaluated, whether the intelligent watch sensor supports use is judged, an evaluation result is fed back to a user through a display module of the intelligent watch, when the intelligent watch sensor does not support use is judged, a warning signal is sent out, and the warning signal is fed back to the user through the display module of the intelligent watch;
smartwatches typically are configured with the following sensors to collect health data of the human body:
1) Heart rate sensor: the intelligent watch is provided with an optical heart rate sensor and is used for measuring the heart rate of a user, and the watch can monitor heart rate changes in real time and provide heart rate data by irradiating skin and detecting the reflection condition of light;
2) An accelerometer: accelerometers are one of the common sensors in smart watches for measuring the acceleration and movement of the watch in three axes, which can monitor the user's athletic activities such as walking, running, climbing stairs, etc.
Therefore, the application mainly carries out performance evaluation on the heart rate sensor and the accelerometer of the intelligent watch.
For heart rate sensors, parameters to be monitored include light source power fluctuation rate, sensor element data acquisition standard deviation and operating environment temperature, and heart rate sensor index is established after the light source power fluctuation rate, the sensor element data acquisition standard deviation and the operating environment temperature are removed from dimensionsThe computational expression is: />In (1) the->For the light source power fluctuation rate, < >>Standard deviation, < > -for sensing element data acquisition>Is the operating environment temperature;
acquiring heart rate sensor indexAfter that, the heart rate sensor index +.>And comparing the first threshold value with the heart rate sensor index, judging that the heart rate sensor is not supported for use if the heart rate sensor index is smaller than the first threshold value, and judging that the heart rate sensor is supported for use if the heart rate sensor index is larger than or equal to the first threshold value.
The calculation expression of the light source power fluctuation rate is as follows:in (1) the->For real-time acquisition of the light source power, +.>For the stable operation range of the light source power, the larger the fluctuation rate of the light source power is, the more the light source power acquired in real time deviates from the stable operation range of the light source power, and the following problems are presented:
1) Excessive light source power may cause excessive light to penetrate the skin, so that the reflected light signal is too strong, and the heart rate signal is distorted or saturated, thereby affecting the accuracy of heart rate monitoring;
2) Too little light source power may result in signal weakness such that the heart rate sensor does not obtain enough reflected light signal for accurate heart rate monitoring.
The calculation expression of the standard deviation of the data acquisition of the sensing element is as follows:in the formula, i=，/>Representing the number of data acquisitions of the sensor element, +.>Is a positive integer>Representing different data measurements, +.>Mean of the data measurements is shown.
1) The smaller the standard deviation of the data acquisition of the sensing element is, the more the numerical value measured by the sensing element is relatively concentrated near the mean value, the degree of dispersion is small, the higher the consistency is, the distribution is relatively stable and the fluctuation is small; 2) The larger the standard deviation of the data acquisition of the sensing element is, the larger the dispersion degree of the numerical values measured by the sensing element is, the larger the difference between the data points is, the lower the consistency is, the distribution is relatively unstable and the fluctuation is large.
The greater the operating environment temperature, the more likely it is that:
1) Signal noise increase: the high temperature environment may cause increased noise in the electronic components, including the sensor itself and other components in the circuit, which may introduce additional signal noise, interfering with the acquisition and processing of the heart rate signal, thereby reducing the accuracy of the sensor;
2) The light source stability is reduced: the high temperature environment may cause the stability of a light source (such as an LED) in the heart rate sensor to be reduced, and the light intensity and wavelength of the light source may be affected by temperature variation, thereby affecting the quality and stability of the heart rate signal;
3) Sensor stability decreases: the high temperature environment may adversely affect the stability of the sensor, including the performance of the sensing element and thermal expansion of the material, etc., which may cause the sensor output to change or shift, thereby affecting the accuracy of heart rate monitoring.
For the accelerometer, parameters to be monitored include a scale factor error, a signal and interference correlation coefficient and an operating environment temperature, and an accelerometer index is established after the scale factor error, the signal and interference correlation coefficient and the operating environment temperature are removed from dimensionsThe calculation expression is:
in the method, in the process of the application,for signal and interference correlation coefficients, < > and->Is a scale factor error>Is the operating ambient temperature. Acquisition of accelerometer index->After that, accelerometer index ++>And comparing the first threshold value with the second threshold value, judging that the accelerometer index is not supported for use if the accelerometer index is smaller than the second threshold value, and judging that the accelerometer index is supported for use if the accelerometer index is larger than or equal to the second threshold value.
The calculation expression of the scale factor error is:
in the method, in the process of the application,
in order to measure the acceleration,
for the true acceleration, the measured acceleration is the output value of the accelerometer under the known acceleration condition, and the true acceleration is the corresponding true acceleration value.
When the scale factor error is larger, the proportional relation between the output value of the accelerometer and the actual acceleration has larger deviation, which may be caused by factors such as errors in the manufacturing process, instability of the sensor element, temperature change and the like, and the larger scale factor error can cause larger deviation of the measurement result of the accelerometer, thereby affecting the measurement precision and accuracy of the accelerometer.
The computational expression of the signal and interference correlation coefficients is:
in (1) the->And->Observations representing signal and interference, respectively, +.>And->Respectively representing average values of signals and interference, wherein the value range of the correlation coefficient of the signals and the interference is between-1 and 1, and when r is close to 1, the strong positive correlation relationship exists between the signals and the interference; when r is close to-1, a strong negative correlation exists between the signal and the interference; when r is close to 0, no linear correlation exists between the signal and the interference;
1) When the correlation coefficient of the signal and the interference is close to 1, a strong positive correlation relationship exists between the signal and the interference, which means that the interference also changes in a similar way along with the change of the signal, and the two show a consistent trend, and in this case, the interference may have a great influence on the reliability of the signal;
2) When the signal and interference correlation coefficient is close to 0 or negative value, it means that the correlation between the signal and the interference is weak or negative, which means that the variation of the interference is small or opposite to the variation of the signal, in which case the influence of the interference on the signal is small and the reliability of the signal may be high;
thus, the greater the signal and interference correlation coefficient between the signal and the interference, the greater the impact of the interference on the signal, the less reliable the signal may be, and the measurement accuracy of the accelerometer may be reduced with it, whereas the lesser or near zero the signal and interference correlation coefficient, the less the impact of the interference on the signal, and the greater the reliability of the signal may be.
The greater the operating environment temperature, the more likely it is that:
1) Sensitivity variation: the sensitivity of the accelerometer refers to output voltage or output signal variation corresponding to unit acceleration variation, and under a high-temperature environment, the sensitivity of the accelerometer can be changed, so that deviation exists between the variation of the output signal and the variation of the actual acceleration;
2) Offset drift: during long-time operation of the accelerometer, offset drift phenomenon can occur, under the high-temperature environment, zero offset and scale factors of the accelerometer can change due to the influence of thermal expansion of materials and other factors, so that a deviation exists between a measurement result and a true value, and the deviation can gradually increase along with the time; 3) Noise increase: in a high temperature environment, the accelerometer may be interfered by more thermal noise, which may cause the measurement result of the accelerometer to be affected by the noise, and reduce the measurement precision and accuracy.
When heart rate sensor index is obtainedAccelerometer index->After that, the monitoring system can regularly evaluate the overall state of the health data acquisition sensor of the intelligent watch, and specifically comprises the following steps:
acquiring heart rate sensor indexAccelerometer index->Average over a period of time, then establishing an evaluation coefficient +.>The computational expression is:
in the method, in the process of the application,
represents the heart rate sensor index average value,
represents an accelerometer exponential average, j isHeart rate sensor index number acquired in a time period, and j= {1, 2, 3,..r }, r is a positive integer, +.>Represents the sum of the j-th heart rate sensor indices,/->Is->Number of accelerometer indices acquired during a time period, and +.>= {1, 2, 3,..once., s }, s is a positive integer,/-a->Represents the sum of the kth accelerometer indices, < +.>For starting recording the time point +.>For ending the recording time point +.>、/>Proportional coefficients of heart rate sensor index average and accelerometer index average, respectively, and +.>、/>Are all greater than 0. Obtaining an evaluation coefficient->After that, the evaluation coefficient->And comparing the evaluation threshold value with the evaluation threshold value, if the evaluation coefficient is more than or equal to the evaluation threshold value, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is optimal, and if the evaluation coefficient is less than the evaluation threshold value, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is poor.
When the overall state of the health data acquisition sensor of the intelligent watch is poor, the intelligent watch display module is used for feeding back to a user, and the user selects a corresponding processing method according to the feedback result, wherein the processing method comprises the steps of maintaining or overhauling the health data acquisition sensor of the intelligent watch.
Example 3: the embodiment of the intelligent watch health data monitoring system based on cloud computing comprises an evaluation module, a display module, a warning module, an acquisition module, a transmission module, a processing module, an analysis module and a visualization module:
and an evaluation module: the performance data of the intelligent watch sensor is collected regularly, the performance condition of the intelligent watch sensor is evaluated after the performance data is comprehensively analyzed, whether the intelligent watch sensor supports use is judged, and the evaluation result is fed back to a user through a display module of the intelligent watch;
and the warning module is used for: when the intelligent watch sensor is judged to not support the use, sending out a warning signal, and feeding the warning signal back to a user through a display module of the intelligent watch;
and the acquisition module is used for: when the smart watch sensor is judged to be capable of supporting use, health data of a user are collected and stored in a memory of the smart watch;
and a transmission module: the acquired health data are transmitted to the cloud end through a transmission module, and the transmission module is connected with the smart phone;
the processing module is used for: receiving and storing health data from the smart watch, and preprocessing the health data collected from the smart watch;
and an analysis module: carrying out various analyses and calculations on the health data, and sending an abnormal condition to a user when the abnormal condition is detected;
and a visualization module: and displaying the analysis result to the user in a visual mode.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the application disclosed above are intended only to assist in the explanation of the application. The preferred embodiments are not intended to be exhaustive or to limit the application to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the application and the practical application, to thereby enable others skilled in the art to best understand and utilize the application. The application is limited only by the claims and the full scope and equivalents thereof.
Claims (10)
1. A cloud computing-based intelligent watch health data monitoring method is characterized by comprising the following steps of: the monitoring method comprises the following steps:
s1: the monitoring system periodically collects performance data of the intelligent watch sensor, and evaluates the performance condition of the intelligent watch sensor after comprehensively analyzing the performance data;
s2: judging whether the intelligent watch sensor supports use or not, and feeding back an evaluation result to a user through a display module of the intelligent watch;
s3: when the intelligent watch sensor is judged to not support the use, sending out a warning signal, and feeding the warning signal back to a user through a display module of the intelligent watch;
s4: when the smart watch sensor is judged to be capable of supporting the use, the sensor collects health data of a user and stores the health data in a memory of the smart watch;
s5: the intelligent watch is connected with the intelligent mobile phone to transmit the acquired health data to the cloud;
s6: the cloud computing platform receives and stores health data from the smart watch, and preprocesses the health data collected from the smart watch;
s7: carrying out various analyses and calculations on the health data through a cloud computing platform analysis tool, and sending an abnormal condition to a user when the abnormal condition is detected;
s8: and displaying the analysis result to a user in a visual mode through the cloud computing platform.
2. The smart watch health data monitoring method based on cloud computing as claimed in claim 1, wherein: the parameters of the heart rate sensor monitored by the monitoring system comprise the light source power fluctuation rate, the sensing element data acquisition standard deviation and the running environment temperature, and the heart rate sensor index is established after the light source power fluctuation rate, the sensing element data acquisition standard deviation and the running environment temperature are removed from dimensionsThe computational expression is: />In (1) the->For the light source power fluctuation rate, < >>Data acquisition mark for sensing elementPoor (poor) and (poor) of (>Is the operating ambient temperature.
3. The smart watch health data monitoring method based on cloud computing as claimed in claim 2, wherein: acquiring the heart rate sensor indexAfter that, the heart rate sensor index +.>And comparing the first threshold value with the heart rate sensor index, judging that the heart rate sensor is not supported for use if the heart rate sensor index is smaller than the first threshold value, and judging that the heart rate sensor is supported for use if the heart rate sensor index is larger than or equal to the first threshold value.
4. The smart watch health data monitoring method based on cloud computing as recited in claim 3, wherein: the calculation expression of the light source power fluctuation rate is as follows:
in (1) the->For the real-time acquisition of the source power,is a stable operating range of the light source power.
5. The intelligent watch health data monitoring method based on cloud computing as claimed in claim 4, wherein the method comprises the following steps: the calculation expression of the standard deviation of the data acquisition of the sensing element is as follows:
wherein i= =>，/>Representing the number of data acquisitions of the sensor element, +.>Is a positive integer>Representing different data measurements, +.>Mean of the data measurements is shown.
6. The cloud computing-based smart watch health data monitoring method as claimed in claim 5, wherein: the monitoring system monitors parameters of the accelerometer including a scale factor error, a signal and interference correlation coefficient and an operation environment temperature, and establishes an accelerometer index after removing dimensions of the scale factor error, the signal and interference correlation coefficient and the operation environment temperatureThe calculation expression is: />In (1) the->For signal and interference correlation coefficients, < > and->Is a scale factor error>Is the operating ambient temperature.
7. The cloud computing-based smart watch health data monitoring method as claimed in claim 6, wherein: acquiring the accelerometer indexAfter that, accelerometer index ++>And comparing the first threshold value with the second threshold value, judging that the accelerometer index is not supported for use if the accelerometer index is smaller than the second threshold value, and judging that the accelerometer index is supported for use if the accelerometer index is larger than or equal to the second threshold value.
8. The intelligent watch health data monitoring method based on cloud computing as claimed in claim 7, wherein: the calculation expression of the signal and interference correlation coefficient is as follows:in (1) the->And->Observations representing signal and interference, respectively, +.>And->Representing the average of the signal and the interference, respectively.
9. The intelligent watch health data monitoring method based on cloud computing as claimed in claim 8, wherein: acquiring the heart rate sensor indexSaid accelerometer index +.>Average over a period of time, establishing an evaluation coefficient +.>The computational expression is: />In (1) the->Represents heart rate sensor index mean, +.>Represents an accelerometer exponential average, j is +.>Heart rate sensor index number acquired in a time period, and j= {1, 2, 3,..r }, r is a positive integer, +.>Representing the sum of the jth heart rate sensor indices,is->Number of accelerometer indices acquired during a time period, and +.>= {1, 2, 3,..once., s }, s is a positive integer,/-a->Represents the sum of the kth accelerometer indices, < +.>For starting recording the time point +.>For ending the recording time point +.>、/>Proportional coefficients of heart rate sensor index average and accelerometer index average, respectively, and +.>、/>Are all greater than 0; obtaining an evaluation coefficientAfter that, the evaluation coefficient->And comparing the evaluation threshold value with the evaluation threshold value, if the evaluation coefficient is more than or equal to the evaluation threshold value, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is optimal, and if the evaluation coefficient is less than the evaluation threshold value, evaluating that the overall state of the health data acquisition sensor of the intelligent watch is poor.
10. A cloud computing-based smart watch health data monitoring system for implementing the monitoring method of any one of claims 1-9, characterized in that: the system comprises an evaluation module, a display module, a warning module, an acquisition module, a transmission module, a processing module, an analysis module and a visualization module:
the evaluation module periodically collects performance data of the intelligent watch sensor, after comprehensive analysis is carried out on the performance data, the performance condition of the intelligent watch sensor is evaluated, whether the intelligent watch sensor supports use is judged, the evaluation result is fed back to a user through a display module of the intelligent watch, when the intelligent watch sensor is judged not to support use, the warning module sends out warning signals, the warning signals are fed back to the user through the display module of the intelligent watch, when the intelligent watch sensor is judged to support use, the collection module collects health data of the user and stores the health data in a memory of the intelligent watch, the transmission module is connected with a smart phone, the collected health data is transmitted to a cloud end through the transmission module, the processing module receives and stores the health data from the intelligent watch, the health data collected from the intelligent watch is preprocessed, various analyses and calculation are carried out through the analysis module, when abnormal conditions are detected, the abnormal conditions are sent to the user, and the visual module displays the analysis results to the user in a visual mode.
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| CN117169927A (en) * | 2023-11-01 | 2023-12-05 | 河歌科技(深圳)有限责任公司 | Intelligent wearable device state evaluation method based on data analysis |
| CN117241229A (en) * | 2023-11-15 | 2023-12-15 | 深圳市光速时代科技有限公司 | Remote data processing method for intelligent wearable equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117169927A (en) * | 2023-11-01 | 2023-12-05 | 河歌科技(深圳)有限责任公司 | Intelligent wearable device state evaluation method based on data analysis |
| CN117169927B (en) * | 2023-11-01 | 2024-01-26 | 河歌科技(深圳)有限责任公司 | Intelligent wearable device state evaluation method based on data analysis |
| CN117241229A (en) * | 2023-11-15 | 2023-12-15 | 深圳市光速时代科技有限公司 | Remote data processing method for intelligent wearable equipment |
| CN117241229B (en) * | 2023-11-15 | 2024-01-26 | 深圳市光速时代科技有限公司 | Remote data processing method for intelligent wearable equipment |
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