Face health detection telescopic bathroom mirror and detection method thereof

Abstract

The invention discloses a mirror, and aims to provide a face health detection telescopic bathroom mirror capable of detecting the health state of a user in the use process and adjusting the distance between the mirror and the user and a detection method of the face health detection telescopic bathroom mirror. A display is arranged behind the one-way light-transmitting area, a signal acquisition device is further arranged at the upper end of the display and used for acquiring facial information of a user, the display further comprises a sensing element, and the sensing element is used for sensing a human face in front of the mirror surface body and controlling the display to enter a screen-on working mode. The sensing element is an infrared sensor, face data of a user can be collected through the signal collecting device, data collection is controlled through the infrared sensor, the detection precision and efficiency are high, health management is made to be daily and intelligent, the health monitoring effect is improved, and the intelligent health monitoring system is suitable for the field of bathroom appliances.

Description

Technical Field

[0001] The present invention relates to a bathroom article, and more specifically, to a telescopic bathroom mirror for face health detection and a detection method thereof. Background Art

[0002] A mirror is an essential article in people's bathrooms. Everyone uses a mirror to tidy up their appearance after washing. As a daily necessity, mirrors are widely used in people's lives. At the same time, with the development of today's society, the functions of mirrors are becoming increasingly diverse. Mirrors are used in many industrial or commercial products. However, the main and most common application of mirrors is still for household use. Currently, the functions of mirrors are single, they occupy a large space, and mirrors are usually fixedly installed on the bathroom wall, with a fixed distance from the user, making it difficult to adjust and difficult to meet the needs of different users.

[0003] At the same time, with the development of medical technology, wearable technology and precise portable home health data monitoring devices enable patients to complete various health index measurements at home, help patients conduct health analysis, generate health reports, and even make video calls with doctors, enabling patients to have examinations at home and see a doctor when needed. Therefore, we combine a mirror with a health data monitoring device to propose a telescopic bathroom mirror for face health detection and a detection method thereof. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a telescopic bathroom mirror for face health detection and a detection method thereof that can detect the health status of users during use and can adjust the distance between the mirror and the user at the same time.

[0005] To achieve the above purpose, the present invention provides the following technical solutions: A telescopic bathroom mirror for face health detection and a detection method thereof, including a mirror body and a mirror frame. At least one one-way light-transmitting area is provided on the mirror body. A display is provided behind the one-way light-transmitting area. The display is configured to display data. A signal acquisition device is further provided at the upper end of the display. The signal acquisition device is used to collect user facial information. The display further includes a sensing element. The sensing element is used to sense the face in front of the mirror body and control the display to enter the bright screen working mode, and the sensing element is an infrared sensor.

[0006] By adopting the above technical solutions, the display is arranged behind the one-way light-transmitting area, which is convenient for observing data. And the facial data of users can be collected by the signal acquisition device, and the collection of data is controlled by an infrared sensor, with high detection accuracy and efficiency, making health management daily and intelligent, and improving the effect of health monitoring.

[0007] The present invention is further configured as follows: a telescopic mechanism is also provided at the rear end of the mirror body, and the telescopic mechanism includes at least two groups of telescopic rods arranged in parallel at the rear end of the mirror body, and the telescopic rods are used to pull the mirror body closer and farther and adjust the inclination angle of the mirror body, and a light-transmitting groove is also provided on the mirror frame, and a reflective layer is provided on the inner wall of the light-transmitting groove, and a plurality of LED lamp beads are also provided in the light-transmitting groove, and the LED lamp beads are used to provide lighting.

[0008] By adopting the above technical solution, the distance between the mirror and the user can be adjusted by a telescopic mechanism, which can reduce the occupied space, and the LED lamp beads and reflective layer on the frame can supplement the light for the user's face, thereby enhancing the clarity of facial data collection.

[0009] The present invention is further configured as follows: a detection method for a telescopic bathroom mirror for facial health detection, further comprising a data acquisition unit for collecting user data information;

[0010] A data processing unit, used for preprocessing data;

[0011] A comparison and analysis unit is used to train the collected and processed data and obtain a health status data graph of the user;

[0012] The evaluation and display unit is used to detect and rate the trained data and display the evaluated data. The specific method is as follows: S1. The data acquisition unit shoots the target, obtains the user's facial image data X, and uploads the image data X to the data processing unit;

[0013] S2, the data processing unit processes the image, selects the face area, and crops the area to obtain specific data X1, and uploads the specific data X1 to the comparison and analysis unit;

[0014] S3, the comparison and analysis unit performs fine processing and verification on the specific data X1 to obtain a human face model Y1;

[0015] S4. The data display unit compares the model Y1 with the data X1 and obtains the user's health status data by judging various indicators.

[0016] The present invention is further configured as follows: the data processing unit also includes an image classification module, the image classification module is configured to divide the data into a training set and a comparison set, and the ratio of the test set to the comparison set is 2:1.

[0017] The present invention is further configured as follows: the comparison and analysis unit also includes an adaptive spatial feature fusion module, and the adaptive spatial feature fusion module is used for high-precision target detection based on different layer features.

[0018] The present invention is further configured that: the data acquisition method of the adaptive spatial feature fusion module includes the following specific steps: S1. Collect data to obtain an initial image S;

[0019] S2. Output the initial image S as feature maps S1, S2, and S3, multiply and add the weights of the feature maps, sample the facial data in the feature maps, and after sampling, merge the feature maps to form a facial sampling map T;

[0020] S3. Perform feature calibration on the facial sampling map T to suppress the background information in the map and highlight the facial information features in the map.

[0021] By adopting the above technical solution, the facial data of the user is subjected to feature extraction through adaptively spatial feature fusion (ASFF). The extracted information is accurate, greatly reducing the interference of the bathroom background on the user's facial data. Moreover, the facial data of the user is compared through a training set and a comparison set, and the detection accuracy is high. During the process of detecting health data, the trained data can provide a reference for subsequent health detection, greatly improving the efficiency and accuracy of health detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flowchart of the detection method of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0023] Figure 2 It is a flowchart of the data acquisition method of the adaptive spatial feature fusion module of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0024] Figure 3 It is a structural diagram of the adaptive spatial feature fusion module of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0025] Figure 4 It is a front view of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0026] Figure 5 It is a specific structural schematic diagram of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0027] Figure 6 It is a side view of an embodiment of a telescopic bathroom mirror for human face health detection and its detection method according to the present invention;

[0028] Reference numerals in the drawings: 1. Mirror body; 2. Frame; 3. One-way light-transmitting area; 4. Display; 5. Signal acquisition device; 6. Telescopic rod; 7. LED lamp beads. DETAILED DESCRIPTION

[0029] Reference Figures 1 to 6 The present invention further describes a telescopic bathroom mirror for detecting health of human face and a detection method thereof.

[0030] For ease of explanation, spatial relative terms such as "upper", "lower", "left", "right" and the like are used in the embodiments to illustrate the relationship of one element or feature shown in the figure relative to another element or feature. It should be understood that, in addition to the orientation shown in the figure, the spatial terms are intended to include different orientations of the device in use or operation. For example, if the device in the figure is inverted, the element described as being "under" other elements or features will be positioned "on" other elements or features. Therefore, the exemplary term "under" can include both upper and lower orientations. The device can be positioned in other ways (rotated 90 degrees or in other orientations), and the spatial relative descriptions used here can be interpreted accordingly.

[0031] Furthermore, relational terms such as “first” and “second” and the like are merely used to distinguish one component from another having the same name, but do not necessarily require or imply any such actual relationship or order between these components.

[0032] A telescopic bathroom mirror for facial health detection and a detection method thereof, comprising a mirror body 1 and a mirror frame 2, wherein the mirror body 1 is provided with at least one one-way light transmission area 3, a display 4 is provided behind the one-way light transmission area 3, the display 4 is configured to display data, a signal acquisition device 5 is also provided on the upper end of the display 4, the signal acquisition device 5 is used to collect user facial information, the display 4 is arranged behind the one-way light transmission area 3, and it is convenient to observe data, the display 4 also includes a sensing element, the sensing element is used to sense the face in front of the mirror body 1 and control the display 4 to enter a bright screen working mode, and the sensing element is an infrared sensor, data collection is controlled by the infrared sensor, the detection accuracy and efficiency are high, health management is made daily and intelligent, and the effect of health monitoring is improved.

[0033] A telescopic mechanism is also provided inside the frame 2, and the telescopic mechanism includes at least two groups of telescopic rods 6 arranged in parallel at the rear end of the mirror body. The telescopic rods 6 are used to bring the mirror body 1 closer and farther away and adjust the inclination angle of the mirror body 1. The distance between the mirror body 1 and the user and the inclination angle of the mirror body 1 can be adjusted as needed, and it is suitable for a wider range of people. A light-transmitting groove is also provided on the frame 2, and a reflective layer is provided on the inner wall of the light-transmitting groove. A plurality of LED lamp beads 7 are also provided in the light-transmitting groove. The LED lamp beads 7 are used to provide lighting, which can fill in the light of the user's face and enhance the clarity of facial data collection.

[0034] Reference Figures 1 to 3, a detection method for a telescopic bathroom mirror for face health detection, further comprising a data acquisition unit for acquiring user data information;

[0035] A data processing unit for preprocessing the data;

[0036] A comparison and analysis unit for training the acquired and processed data and obtaining a health status data graph of the user;

[0037] An evaluation and display unit for detecting and rating the trained data and displaying the evaluated data. The specific method is as follows: S1. The data acquisition unit captures the target, obtains the user's facial image data X, and uploads the image data X to the data processing unit;

[0038] S2. The data processing unit processes the image, frames the face area, and crops the area to obtain specific data X1, and at the same time uploads the specific data X1 to the comparison and analysis unit;

[0039] S3. The comparison and analysis unit finely processes and verifies the specific data X1 to obtain a human face model Y1;

[0040] S4. The data display unit compares the model Y1 with the data X1 and obtains the user's health status data by judging various indicators.

[0041] The data processing unit further includes an image classification module, and the image classification module is configured to divide the data into a training set and a comparison set, and the ratio of the test set to the comparison set is 2:1.

[0042] The comparison and analysis unit further includes an adaptive spatial feature fusion module, and the adaptive spatial feature fusion module is based on different layer features for high-precision target detection.

[0043] The data acquisition method of the adaptive spatial feature fusion module includes the following specific steps: S1. Acquire data to obtain an initial graph S;

[0044] S2. Output the initial graph S as feature graphs S1, S2, and S3, multiply and add the weights of the feature graphs, sample the facial data in the feature graphs, and merge the feature graphs after sampling to form a facial sampling graph T;

[0045] S3. Perform feature calibration on the facial sampling graph T to suppress the background information in the graph and highlight the facial information features in the graph.

[0046] The facial data of the user is used to extract features through Adaptively Spatial Feature Fusion (ASFF). The extracted information is accurate, which greatly reduces the interference of the bathroom background on the user's facial data. Moreover, the user's facial data is compared through a training set and a comparison set, and the detection accuracy is high. During the process of detecting health data, the trained data can provide reference for subsequent health detection, greatly improving the efficiency and accuracy of health detection. The adaptive spatial feature fusion module recombines high-level features and low-level features, makes full use of features of different scales, and fuses different features by learning weight parameters. Making full use of features of different layers improves the accuracy of object detection. The adaptive spatial feature fusion module filters features through learning parameter fusion, only retaining the attention information of the current layer, making the information hierarchical, and the learning efficiency of the model will also be better.

[0047] The result representation formula after multiplying the input data features Level1, Level2, and Level3 collected by the adaptive spatial feature fusion module by the learnable weights and then adding them is as follows:

[0048]

[0049] Among them, represents the (i, j)th vector of the output feature map y l between channels, represents the learnable weights from 3 different levels to the feature map of layer l, represents the output of the feature map at a certain position.

[0050] Since the addition method is adopted, it is necessary to ensure that the number of channels and the feature sizes of Level1 to Level3 are the same. The sizes are adjusted by using downsampling or upsampling methods for different levels. Then, the feature maps of Level1, Level2, and Level3 pass through a 1×1 convolution to obtain the weight parameters a, b, and c. Then, the weight parameters are concatenated and normalized through the softmax function, mapping the input to the range [0, 1] and the sum being 1. Among them The formula is as follows:

[0051]

[0052]

[0053] Due to the different sizes of different feature layers, when adjusting the size of the feature map, it is necessary to perform upsampling or downsampling operations on the feature maps of different layers. After performing different nearest neighbor interpolations and pooling on the feature maps of three different sizes, the feature maps are compressed to 1 dimension through 1×1 convolution to obtain global spatial information. The three single-channel spatial information after compression is merged to form channel correlations, and the softmax activation function is used to obtain the spatial attention weights of each size and the channel attention weights among the three sizes, completing adaptive feature recalibration to suppress the background information in the feature maps of the three sizes.

[0054] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any ordinary changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A telescopic bathroom mirror for facial health detection, comprising a mirror body (1) and a mirror frame (2), characterized in that: The mirror body (1) is provided with at least one one-way light-transmitting area (3), a display (4) is provided behind the one-way light-transmitting area (3), the display (4) is configured to display data, and a signal collection device (5) is also provided on the upper end of the display (4), the signal collection device (5) is used to collect user facial information.

2. The face health detection telescopic bathroom mirror according to claim 1, characterized in that: The display (4) further comprises a sensing element, wherein the sensing element is used to sense a human face in front of the mirror body (1) and control the display (4) to enter a bright screen working mode.

3. The face health detection telescopic bathroom mirror according to claim 2, characterized in that: The sensing element is an infrared sensor.

4. The face health detection telescopic bathroom mirror according to claim 1, characterized in that: The rear end of the mirror body (1) is also provided with a telescopic mechanism, which comprises at least two sets of telescopic rods (6) arranged in parallel at the rear end of the mirror body, and the telescopic rods (6) are used to pull the mirror body (1) closer or farther away and to adjust the inclination angle of the mirror body (1).

5. The face health detection telescopic bathroom mirror according to claim 1, characterized in that: The mirror frame (2) is also provided with a light-transmitting groove, the inner wall of which is provided with a light-reflecting layer, and a plurality of LED lamp beads (7) are also provided in the light-transmitting groove, and the LED lamp beads (7) are used to provide lighting.

6. A method for detecting a telescopic bathroom mirror for detecting facial health according to claim 1, characterized in that: It also includes a data collection unit for collecting user data information; A data processing unit, used for preprocessing data; A comparison and analysis unit is used to train the collected and processed data and obtain a health status data graph of the user; The evaluation and display unit is used to detect and rate the trained data and display the evaluated data. The specific detection method is as follows: S1. The data acquisition unit shoots the target, obtains the user's facial image data X, and uploads the image data X to the data processing unit; S2, the data processing unit processes the image, selects the face area, and crops the area to obtain specific data X1, and uploads the specific data X1 to the comparison and analysis unit; S3, the comparison and analysis unit performs fine processing and verification on the specific data X1 to obtain a human face model Y1; S4. The data display unit compares the model Y1 with the data X1 and obtains the user's health status data by judging various indicators.

7. The face health detection telescopic bathroom mirror according to claim 7, characterized in that: The data processing unit also includes an image classification module, which is configured to divide the data into a training set and a comparison set, and the ratio of the test set to the comparison set is 2:

1.

8. The face health detection telescopic bathroom mirror according to claim 7, characterized in that: The comparison and analysis unit also includes an adaptive spatial feature fusion module, which is used for high-precision target detection based on different layer features.

9. The face health detection telescopic bathroom mirror according to claim 8, characterized in that: The data collection method of the adaptive spatial feature fusion module includes the following specific steps: S1, collecting data to obtain an initial graph S; S2, output the initial image S as feature images S1, S2 and S3, multiply and add weights of the feature images, sample the facial data in the feature images, and merge the feature images after sampling to form a facial sampling image T; S3. Perform feature calibration on the facial sampling image T, suppress the background information in the image, and highlight the features in the image.

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