Rhythm guidance system based on square dance auxiliary waistcoat and computer program product

Abstract

The invention relates to the technical field of health management, in particular to a rhythm guidance system based on a square dance auxiliary waistcoat and a computer program product, and the system comprises a data collection device which is used for obtaining all physiological data of an auxiliary waistcoat wearer within a preset time through a multi-dimensional sensor array, and sending all physiological data to a cloud platform; the cloud platform is used for inputting all the received physiological data into a preset physiological state classification model by the cloud platform to obtain a corresponding state classification result; and according to the state grading result and a preset corresponding relation table, obtaining a control instruction for indicating each vibrator in the vibrator group to vibrate according to a specified vibration sequence, and sending the control instruction to the corresponding vibrator so as to perform rhythm guidance. The waistcoat has the advantages that health monitoring is provided for a wearer, and meanwhile dancing actions of the waistcoat wearer are accurately assisted.

Description

Technical Field

[0001] The present invention relates to the field of health management technology, and in particular to a rhythm guidance system and computer program product based on a square dance auxiliary vest. Background Art

[0002] Square dancing gets its name from the fact that most people gather in squares. It combines self-entertainment with performance. At this stage, it has become one of the important ways for the elderly to keep fit and relax.

[0003] The movements for elderly people to participate in square dancing are relatively simple, but correct movements and coordination are still difficult for them, and they often need rhythm auxiliary equipment to assist. However, the existing auxiliary equipment mostly guides in the form of sound, and cannot adjust the volume according to personal status, resulting in a failure to meet personal needs.

[0004] In addition, the physical conditions of the elderly are different. Existing auxiliary equipment cannot monitor the physiological status of the elderly in real time, and then discover and warn of potential dangers to the elderly's body. It is even more impossible to adjust the appropriate range of dance movements for each person based on their individual physiological status. Summary of the Invention

[0005] (1) Technical issues to be resolved

[0006] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a rhythmic guidance system and computer program product based on a square dance auxiliary vest, which solves the technical problems that existing auxiliary equipment cannot detect and warn potential dangers to the bodies of the elderly, and cannot adjust the appropriate dance movement amplitude for each person based on the individual's physiological state.

[0007] (2) Technical solution

[0008] In order to achieve the above objectives, the main technical solutions adopted by the present invention include:

[0009] In a first aspect, an embodiment of the present invention provides a rhythmic guidance system based on a square dance auxiliary vest, comprising a data acquisition device and a vibrator group provided on the auxiliary vest, and a cloud platform provided at a remote end;

[0010] The vibrator group includes at least one vibrator disposed on the auxiliary vest, and each vibrator is disposed at a different position on the auxiliary vest;

[0011] The data acquisition device is used to obtain all physiological data of the wearer of the auxiliary vest within a preset time through a preset multi-dimensional sensor array at preset intervals, and send all physiological data to the cloud platform;

[0012] The physiological data includes data collected by all sensors in the multi-dimensional sensor array;

[0013] The cloud platform is used to input all received physiological data into a preset physiological state classification model to obtain corresponding state classification results;

[0014] and, obtaining, based on the state classification result and a preset correspondence table, a control instruction instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence, and sending the control instruction to the corresponding vibrator to perform rhythmic guidance;

[0015] The corresponding relationship table is a corresponding relationship table between the state classification results and the vibration sequences of all vibrators in the vibrator group;

[0016] The vibration sequence includes vibration intensity, vibration time and vibration interval.

[0017] Optionally, the data acquisition device is further used to:

[0018] Perform preliminary processing on all the physiological data based on edge computing;

[0019] The preliminary processing includes:

[0020] Preprocessing all the physiological data; the preprocessing includes data cleaning, data sorting and missing value filling;

[0021] and, screening out abnormal data from all pre-processed physiological data according to a preset abnormal data detection algorithm; the abnormal data detection algorithm includes: Z-Score algorithm, IQR algorithm, KNN algorithm, DBSCAN algorithm or isolation forest algorithm;

[0022] And, all abnormal data are corrected according to a preset abnormal data correction algorithm.

[0023] Optionally, the data acquisition device filters out abnormal data from all pre-processed physiological data according to a preset abnormal data detection algorithm, including:

[0024] According to all physiological data and the preset formula 1, the standard score corresponding to each physiological data is obtained; the formula 1 is:

[0025]

[0026] Among them, Z is the standard score corresponding to the physiological data, x is the corresponding physiological data, μ is the average value of all physiological data, σ is the standard deviation of all physiological data, τ is the pre-set basic threshold, x max is the maximum value of the preset normal data range, xmin is the minimum value of the preset normal data range, and ∈1 is a preset constant;

[0027] According to the standard score corresponding to each physiological data and the preset score threshold, it is determined whether the physiological data is abnormal data.

[0028] Optionally, the data acquisition device corrects all abnormal data according to a preset abnormal data correction algorithm, including:

[0029] Based on all normal data and a preset formula 2, the distance between each abnormal data and all normal data is obtained; the normal data is non-abnormal data; the formula 2 is:

[0030]

[0031] Where d is the distance between any abnormal data and all normal data, p is the abnormal data, and qi is the normal data with index i;

[0032] According to the distance between each abnormal data and all normal data, the data of the nearest neighbor corresponding to the abnormal data is obtained; the nearest neighbor is the normal data with the smallest distance to the abnormal data;

[0033] According to the data of the nearest neighbor corresponding to each abnormal data and the preset formula 3, the correction information corresponding to each abnormal data is obtained to replace the abnormal data; the formula 3 is:

[0034]

[0035] Among them, y is the correction information corresponding to any abnormal data, ρ j is the preset correction coefficient of the nearest neighbor of index j, Lj is the nearest neighbor of index j, u' is the average value of all the nearest neighbors of the abnormal data, σ' is the standard deviation of all the nearest neighbors of the abnormal data, ∈2 is a preset constant, and m is the number of the nearest neighbors of the abnormal data.

[0036] Optionally, the physiological state classification model is used to:

[0037] Perform feature extraction on all received physiological data to obtain the feature parameters corresponding to each physiological data;

[0038] Arrange the characteristic parameters corresponding to all received physiological data according to the collection time corresponding to each physiological data;

[0039] According to the preset time distribution weight and the preset formula 4, all feature parameters are fused to obtain the corresponding fusion feature; the formula 4 is:

[0040]

[0041] Wherein, L is the corresponding fusion feature, Wr is the time distribution weight of the feature parameter of index r, and Er is the feature parameter of index r; the feature parameters include feature dimension and sequence length;

[0042] Obtaining corresponding attention weights according to the fusion features and the pre-set attention mechanism;

[0043] According to the fusion feature and the attention weight corresponding to the fusion feature, a corresponding state classification result is obtained.

[0044] Optionally, the physiological state classification model obtains corresponding attention weights according to the fusion features and a preset attention mechanism, including:

[0045] According to the preset attention mechanism and the preset formula 5, the attention matrix corresponding to the fusion feature is obtained; the formula 5 is:

[0046]

[0047] Among them, H is the attention matrix corresponding to the fused feature, Q, K and V are the corresponding query vector, key vector and value vector respectively, dk is the dimension size of the key, and T is the transpose;

[0048] According to the fusion feature and the attention matrix, and the preset formula 6, the attention weight corresponding to the fusion feature is obtained; the formula 6 is:

[0049] S=Softmax(HW B1 +X);

[0050] Among them, S is the attention weight corresponding to the fusion feature, W B1 is the pre-set feature transformation weight, and X is the fusion feature.

[0051] Optionally, the physiological state classification model obtains a corresponding state classification result according to the fusion feature and the attention weight corresponding to the fusion feature, including:

[0052] According to the fusion feature and the attention weight corresponding to the fusion feature, and the preset formula 7, the corresponding state classification result is obtained; the formula 7 is:

[0053] Y=W B2 Dropout[tanh(W B3 ·S·X)];

[0054] Among them, Y is the corresponding state classification result, WB2 and W B3 These are all pre-set feature transformation weights.

[0055] Optionally, the system further comprises: a mobile device;

[0056] The mobile device is used to acquire physiological data acquired by the data acquisition device through the multi-dimensional sensor array in real time;

[0057] And obtain the status classification results generated by the cloud platform in real time;

[0058] The data acquisition device is further used to determine whether the wearer is handling abnormal physiological activities based on the physiological data acquired in real time by the multi-dimensional sensor array and the preset physiological data threshold;

[0059] and when abnormal physiological activity occurs, sounding an alarm and sending an alarm signal to the mobile device;

[0060] The cloud platform is further used to determine whether the wearer is in an abnormal physiological activity according to the status classification result and the pre-set status level threshold;

[0061] And, when abnormal physiological activity occurs, an alarm signal is sent to the mobile device.

[0062] Optionally, the data acquisition device is further configured to send the music information input by the user to the cloud platform;

[0063] Then, the cloud platform obtains a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result and a preset correspondence table, including:

[0064] Obtaining a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result, a preset correspondence table, and the music information;

[0065] The corresponding relationship table is a corresponding relationship table between the state classification results and the music information and the vibration sequences of all vibrators in the vibrator group.

[0066] In a second aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements the above-mentioned rhythmic guidance system based on a square dance auxiliary vest.

[0067] (3) Beneficial effects

[0068] The beneficial effect of the present invention is that the rhythmic guidance system based on the square dance auxiliary vest of the present invention obtains the wearer's physiological data through a multi-dimensional sensor array, and controls the corresponding vibrator according to the physiological data to perform rhythmic guidance. Compared with the existing technology, it can provide health monitoring for the wearer while accurately assisting the dance movements of the vest wearer. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 A schematic structural diagram of a rhythmic guidance system based on a square dance auxiliary vest provided by an embodiment of the present invention;

[0070] Figure 2 A schematic diagram of the rhythmic guidance process of the auxiliary vest provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to better explain the present invention and facilitate understanding, the present invention is described in detail below through specific implementation methods in conjunction with the accompanying drawings.

[0072] The rhythmic guidance system based on the square dance auxiliary vest proposed in the embodiment of the present invention obtains the wearer's physiological data through a multi-dimensional sensor array, and controls the corresponding vibrator according to the physiological data to perform rhythmic guidance. Compared with the existing technology, it can provide health monitoring for the wearer while accurately assisting the dance movements of the vest wearer.

[0073] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. Instead, these embodiments are provided to enable a clearer and more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0074] Example 1

[0075] The embodiment of the present invention provides a rhythm guidance system based on square dance auxiliary vest, such as Figure 1 As shown, it includes a data acquisition device and a vibrator group arranged on the auxiliary vest, and a cloud platform arranged at a remote end;

[0076] The vibrator group includes at least one vibrator disposed on the auxiliary vest, and each vibrator is disposed at a different position on the auxiliary vest;

[0077] The data acquisition device is used to obtain all physiological data of the wearer of the auxiliary vest within a preset time through a preset multi-dimensional sensor array at preset intervals, and send all physiological data to the cloud platform;

[0078] The physiological data includes data collected by all sensors in the multi-dimensional sensor array;

[0079] The cloud platform is used to input all received physiological data into a preset physiological state classification model to obtain corresponding state classification results;

[0080] and, obtaining, based on the state classification result and a preset correspondence table, a control instruction instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence, and sending the control instruction to the corresponding vibrator to perform rhythmic guidance;

[0081] The corresponding relationship table is a corresponding relationship table between the state classification results and the vibration sequences of all vibrators in the vibrator group;

[0082] The vibration sequence includes vibration intensity, vibration time and vibration interval.

[0083] The rhythmic guidance system based on the square dance auxiliary vest proposed in the embodiment of the present invention obtains the wearer's physiological data through a multi-dimensional sensor array, and controls the corresponding vibrator according to the physiological data to perform rhythmic guidance. Compared with the existing technology, it can provide health monitoring for the wearer while accurately assisting the dance movements of the vest wearer.

[0084] Example 2

[0085] This embodiment provides a rhythmic guidance system based on a square dance auxiliary vest, including a data acquisition device and a vibrator group arranged on the auxiliary vest, and a cloud platform arranged at a remote end; the cloud platform can remotely communicate with multiple auxiliary vests to monitor and adjust the multiple auxiliary vests.

[0086] The vibrator group includes at least one vibrator disposed on the auxiliary vest, and each vibrator is disposed at a different position on the auxiliary vest;

[0087] For any auxiliary vest, the control process of the auxiliary vest is as follows Figure 2 Shown, including:

[0088] The data acquisition device is used to obtain all physiological data of the auxiliary vest wearer within a preset time through a preset multi-dimensional sensor array at every preset time;

[0089] Performing preliminary processing on all the physiological data based on edge computing;

[0090] Among them, the initial processing includes:

[0091] Preprocessing all the physiological data; the preprocessing includes data cleaning, data sorting and missing value filling;

[0092] and, screening out abnormal data from all pre-processed physiological data according to a preset abnormal data detection algorithm; the abnormal data detection algorithm includes: Z-Score algorithm, IQR algorithm, KNN algorithm, DBSCAN algorithm or isolation forest algorithm;

[0093] In this embodiment, the standard score corresponding to each physiological data is obtained based on all physiological data and a preset formula 1; the formula 1 is:

[0094]

[0095] Among them, Z is the standard score corresponding to the physiological data, x is the corresponding physiological data, μ is the average value of all physiological data, σ is the standard deviation of all physiological data, τ is the pre-set basic threshold, x max is the maximum value of the preset normal data range, x min is the minimum value of the preset normal data range, and ∈1 is a preset constant;

[0096] According to the standard score corresponding to each physiological data and the preset score threshold, it is determined whether the physiological data is abnormal data.

[0097] And, based on all normal data and a preset formula 2, the distance between each abnormal data and all normal data is obtained; the normal data is non-abnormal data; the formula 2 is:

[0098]

[0099] Where d is the distance between any abnormal data and all normal data, p is the abnormal data, and qi is the normal data with index i;

[0100] According to the distance between each abnormal data and all normal data, the data of the nearest neighbor corresponding to the abnormal data is obtained; the nearest neighbor is the normal data with the smallest distance to the abnormal data;

[0101] According to the data of the nearest neighbor corresponding to each abnormal data and the preset formula 3, the correction information corresponding to each abnormal data is obtained to replace the abnormal data; the formula 3 is:

[0102]

[0103] Among them, y is the correction information corresponding to any abnormal data, ρ jis the preset correction coefficient of the nearest neighbor of index j, Lj is the nearest neighbor of index j, u' is the average value of all the nearest neighbors of the abnormal data, σ' is the standard deviation of all the nearest neighbors of the abnormal data, ∈2 is a preset constant, and m is the number of the nearest neighbors of the abnormal data.

[0104] The cloud platform is used to input all received physiological data into a preset physiological state classification model to obtain corresponding state classification results;

[0105] and, obtaining, based on the state classification result and a preset correspondence table, a control instruction instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence, and sending the control instruction to the corresponding vibrator to perform rhythmic guidance;

[0106] The corresponding relationship table is a corresponding relationship table between the state classification results and the vibration sequences of all vibrators in the vibrator group;

[0107] The vibration sequence includes vibration intensity, vibration time and vibration interval.

[0108] The physiological state classification model is used to:

[0109] Perform feature extraction on all received physiological data to obtain the feature parameters corresponding to each physiological data;

[0110] Arrange the characteristic parameters corresponding to all received physiological data according to the collection time corresponding to each physiological data;

[0111] According to the preset time distribution weight and the preset formula 4, all feature parameters are fused to obtain the corresponding fusion feature; the formula 4 is:

[0112]

[0113] Wherein, L is the corresponding fusion feature, Wr is the time distribution weight of the feature parameter of index r, and Er is the feature parameter of index r; the feature parameters include feature dimension and sequence length;

[0114] According to the preset attention mechanism and the preset formula 5, the attention matrix corresponding to the fusion feature is obtained; the formula 5 is:

[0115]

[0116] Among them, H is the attention matrix corresponding to the fused feature, Q, K and V are the corresponding query vector, key vector and value vector respectively, dk is the dimension size of the key, and T is the transpose;

[0117] According to the fusion feature and the attention matrix, and the preset formula 6, the attention weight corresponding to the fusion feature is obtained; the formula 6 is:

[0118] S=Softmax(HW B1 +X);

[0119] Among them, S is the attention weight corresponding to the fusion feature, W B1 is the pre-set feature transformation weight, and X is the fusion feature.

[0120] According to the fusion feature and the attention weight corresponding to the fusion feature, and the preset formula 7, the corresponding state classification result is obtained; the formula 7 is:

[0121] Y=W B2 Dropout[tanh(W B3 ·S·X)];

[0122] Among them, Y is the corresponding state classification result, W B2 and W B3 These are all pre-set feature transformation weights.

[0123] Furthermore, the system further comprises: a mobile device;

[0124] The mobile device is used to acquire physiological data acquired by the data acquisition device through the multi-dimensional sensor array in real time;

[0125] And obtain the status classification results generated by the cloud platform in real time;

[0126] The data acquisition device is further used to determine whether the wearer is handling abnormal physiological activities based on the physiological data acquired in real time by the multi-dimensional sensor array and the preset physiological data threshold;

[0127] and when abnormal physiological activity occurs, sounding an alarm and sending an alarm signal to the mobile device;

[0128] The cloud platform is further used to determine whether the wearer is in an abnormal physiological activity according to the status classification result and the pre-set status level threshold;

[0129] And, when abnormal physiological activity occurs, an alarm signal is sent to the mobile device.

[0130] Furthermore, the data collection device is also used to send the music information input by the user to the cloud platform;

[0131] Then, the cloud platform obtains a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result and a preset correspondence table, including:

[0132] Obtaining a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result, a preset correspondence table, and the music information;

[0133] The corresponding relationship table is a corresponding relationship table between the state classification results and the music information and the vibration sequences of all vibrators in the vibrator group.

[0134] Furthermore, the vest can also be equipped with functions such as volume vibration sensing, Bluetooth, and a small loudspeaker. With these functions, when the elderly are square dancing, the vest can vibrate in different parts according to the different music rhythms, prompting the elderly to perform corresponding dance moves.

[0135] When guiding movements, micro-vibrators can be installed on the inside of key areas such as the shoulders, waist, and arms. When a rhythmic point needs to be emphasized or the direction changes during the dance, the vibration motors vibrate, and the vibration amplitude automatically adjusts based on the available intensity. For example, during the heavy beats of the dance, the vibrations on both sides of the waist are more pronounced. In this way, the elderly can perceive the rhythm and changes in movement through their body's touch.

[0136] Furthermore, a small music player can be installed inside the vest. This player has simple and easy-to-use operation buttons. Elderly people can easily control music playback with the help of a few buttons on the vest, such as play / pause, previous / next song, and volume adjustment. The music player is also connected to a data acquisition device. When switching music, the data acquisition device collects music information and sends it to the cloud platform to assist in the generation of control instructions. This enables the system to automatically adjust the vibration frequency according to the music rhythm and dance movements, achieving a perfect combination of music, movement guidance and square dancing.

[0137] The multi-dimensional sensor array includes a heart rate monitoring sensor on the side of the vest near the heart, a respiratory rate sensor near the chest, a pulse detection sensor on the arm, etc., which perform real-time detection of the wearer's physiological data. When these physiological data exceed the normal range, such as when the heart rate exceeds the normal range (this range can be set according to the elderly's age, physical condition and other factors), the vest will remind the elderly to take proper rest through voice prompts.

[0138] The guidance of the wearer's dance movements can also be adjusted through the physiological data detected by these sensors to meet individual needs.

[0139] The vest is made of environmentally friendly, non-toxic, breathable and soft fabric that meets national safety standards. This fabric is skin-friendly and breathable, and can quickly absorb sweat and avoid discomfort even when sweating a lot in the summer.

[0140] A Bluetooth chip is installed at the waist of the vest, which connects directly to a mobile device app. After the square dance, the user can view the exercise data, posture improvements, and real-time communication on the phone, allowing the elderly to clearly understand the shortcomings of each square dance and enjoy the dance more easily next time.

[0141] Furthermore, it can be connected to a mobile phone APP via Bluetooth, and the parameters of the vibration motor can be adjusted through the mobile phone APP, and music tracks, dance moves and other content can be added to meet the personalized preferences of different users, further enhancing the practicality and adaptability of the product.

[0142] On top of that, it can be equipped with lithium battery power supply and remote control functions, which provides great convenience for square dance activities and gets rid of the limitations of traditional power supply and close-range operation.

[0143] Furthermore, the inner layers at the positions of the Jianjing acupoints on the shoulders and the Shenshu acupoints on the waist are carefully designed with soft cushioning layers, which not only ensures wearing comfort but also effectively transmits vibration effects, combining vibration with acupoint stimulation.

[0144] This embodiment provides a rhythmic guidance system based on a square dance auxiliary vest, which accurately controls the vibration motor through real-time analysis of the wearer's physiological signals and square dance music, allowing the user to keenly perceive the dance rhythm, greatly improving the accuracy and timeliness of keeping up with the dance rhythm, and assisting dancers to better learn and perform square dance movements.

[0145] Adjusting the dance amplitude based on the wearer's physiological signals can better suit individual needs and enable the elderly to exercise more healthily.

[0146] Example 3

[0147] This embodiment provides a rhythmic guidance system based on square dance auxiliary vests, including a cloud platform and all auxiliary vests in the target area controlled by the cloud platform; each auxiliary vest is provided with a corresponding data acquisition device; the cloud platform and the data acquisition device are both the contents of Example 1 or Example 2.

[0148] Since there is data transmission between a large number of data acquisition devices and the cloud platform, it is necessary to compress all physiological data collected within the preset time at the data acquisition end to reduce the amount of data transmitted to the cloud platform and relieve the pressure on the cloud platform. Generally, compression can be performed on the data acquisition device at the auxiliary end, that is, the complete data filled in from the first moment to the tth moment of all data acquisition devices can be compressed (the preset time is t moments). For a single data acquisition device p, the physiological data matrix obtained after t moments is Then the data acquisition device is further used for:

[0149] According to the physiological data matrix and the preset formula 8, the prediction equation corresponding to the data acquisition device is obtained; the formula 8 is:

[0150]

[0151] Among them, a k is the prediction coefficient, e γ is the corresponding prediction error;

[0152] Obtaining a prediction matrix corresponding to the physiological data matrix according to the physiological data matrix and the prediction equation;

[0153] Based on the physiological data matrix and the prediction matrix, and the preset formula nine, the observation vector corresponding to the physiological data matrix is obtained; the formula nine is:

[0154]

[0155] V P is the prediction matrix, is a pre-set standard matrix, B is the approximate sparsity;

[0156] Then the physiological data matrix is compressed.

[0157] The cloud platform is also used to decompress the received observation vectors; that is, to reconstruct the data according to the preset formula 10; the formula 10 is:

[0158]

[0159] Then, the received observation vector is decompressed and reconstructed to restore the original data for subsequent processing.

[0160] The present embodiment provides a rhythmic guidance system based on a square dance auxiliary vest, which reduces the amount of data transmission between the auxiliary vest and the cloud platform by compressing data, alleviates the pressure of data transmission, and ensures the stability of the system during operation.

[0161] Example 4

[0162] This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the rhythm guidance system based on the square dance auxiliary vest described in Example 1 or Example 2.

[0163] In the description of the present invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Therefore, a feature specified as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0164] In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," "connect," "fixed," etc. should be understood broadly. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; and internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0165] In the present invention, unless otherwise expressly specified or limited, when a first feature is "above" or "below" a second feature, it may mean that the first and second features are in direct contact, or that the first and second features are in indirect contact through an intermediate medium. Furthermore, when a first feature is "above," "above," or "above" a second feature, it may mean that the first feature is directly above or obliquely above the second feature, or simply means that the first feature is at a higher level than the second feature. When a first feature is "below," "below," or "below" a second feature, it may mean that the first feature is directly below or obliquely below the second feature, or simply means that the first feature is at a lower level than the second feature.

[0166] In the description of this specification, the terms "one embodiment", "some embodiments", "embodiments", "examples", "specific examples" or "some examples" refer to the specific features, structures, materials or characteristics described in conjunction with the embodiment or example and included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and features of different embodiments or examples, unless they are mutually inconsistent.

[0167] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may alter, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A rhythm guidance system based on a square dance auxiliary vest, characterized in that: It includes a data acquisition device and a vibrator group arranged on the auxiliary vest, and a cloud platform arranged at a remote end; The vibrator group includes at least one vibrator disposed on the auxiliary vest, and each vibrator is disposed at a different position on the auxiliary vest; The data acquisition device is used to obtain all physiological data of the wearer of the auxiliary vest within a preset time through a preset multi-dimensional sensor array at preset intervals, and send all physiological data to the cloud platform; The physiological data includes data collected by all sensors in the multi-dimensional sensor array; The cloud platform is used to input all received physiological data into a preset physiological state classification model to obtain corresponding state classification results; and, obtaining, based on the state classification result and a preset correspondence table, a control instruction instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence, and sending the control instruction to the corresponding vibrator to perform rhythmic guidance; The corresponding relationship table is a corresponding relationship table between the state classification results and the vibration sequences of all vibrators in the vibrator group; The vibration sequence includes vibration intensity, vibration time and vibration interval.

2. The rhythm guidance system based on the square dance auxiliary vest according to claim 1 is characterized in that: The data acquisition device is also used for: Perform preliminary processing on all the physiological data based on edge computing; The preliminary processing includes: Preprocessing all the physiological data; the preprocessing includes data cleaning, data sorting and missing value filling; and, screening out abnormal data from all pre-processed physiological data according to a preset abnormal data detection algorithm; the abnormal data detection algorithm includes: Z-Score algorithm, IQR algorithm, KNN algorithm, DBSCAN algorithm or isolation forest algorithm; And, all abnormal data are corrected according to a preset abnormal data correction algorithm.

3. The rhythm guidance system based on the square dance auxiliary vest according to claim 2 is characterized in that: The data acquisition device, according to a preset abnormal data detection algorithm, filters out abnormal data from all pre-processed physiological data, including: According to all physiological data and the preset formula 1, the standard score corresponding to each physiological data is obtained; the formula 1 is: Among them, Z is the standard score corresponding to the physiological data, x is the corresponding physiological data, μ is the average value of all physiological data, σ is the standard deviation of all physiological data, τ is the pre-set basic threshold, x max is the maximum value of the preset normal data range, x min is the minimum value of the preset normal data range, and ∈1 is a preset constant; According to the standard score corresponding to each physiological data and the preset score threshold, it is determined whether the physiological data is abnormal data.

4. The rhythmic guidance system based on the square dance auxiliary vest according to claim 2 is characterized in that: The data acquisition device corrects all abnormal data according to a preset abnormal data correction algorithm, including: Based on all normal data and a preset formula 2, the distance between each abnormal data and all normal data is obtained; the normal data is non-abnormal data; the formula 2 is: Where d is the distance between any abnormal data and all normal data, p is the abnormal data, and qi is the normal data with index i; According to the distance between each abnormal data and all normal data, the data of the nearest neighbor corresponding to the abnormal data is obtained; the nearest neighbor is the normal data with the smallest distance to the abnormal data; According to the data of the nearest neighbor corresponding to each abnormal data and the preset formula 3, the correction information corresponding to each abnormal data is obtained to replace the abnormal data; the formula 3 is: Among them, y is the correction information corresponding to any abnormal data, ρ j is the correction coefficient of the nearest neighbor of the preset index j, L j is the nearest neighbor of index j, u' is the average value of all the nearest neighbors of the abnormal data, σ' is the standard deviation of all the nearest neighbors of the abnormal data, ∈2 is a preset constant, and m is the number of the nearest neighbors of the abnormal data.

5. The rhythmic guidance system based on the square dance auxiliary vest according to claim 1 is characterized in that: The physiological state classification model is used to: Perform feature extraction on all received physiological data to obtain the feature parameters corresponding to each physiological data; Arrange the characteristic parameters corresponding to all received physiological data according to the collection time corresponding to each physiological data; According to the preset time distribution weight and the preset formula 4, all feature parameters are fused to obtain the corresponding fusion feature; the formula 4 is: Wherein, L is the corresponding fusion feature, Wr is the time distribution weight of the feature parameter of index r, and Er is the feature parameter of index r; the feature parameters include feature dimension and sequence length; Obtaining corresponding attention weights according to the fusion features and the pre-set attention mechanism; According to the fusion feature and the attention weight corresponding to the fusion feature, a corresponding state classification result is obtained.

6. The rhythmic guidance system based on the square dance auxiliary vest according to claim 5 is characterized in that: The physiological state classification model obtains corresponding attention weights according to the fusion features and the preset attention mechanism, including: According to the preset attention mechanism and the preset formula 5, the attention matrix corresponding to the fusion feature is obtained; the formula 5 is: Among them, H is the attention matrix corresponding to the fused feature, Q, K and V are the corresponding query vector, key vector and value vector respectively, dk is the dimension size of the key, and T is the transpose; According to the fusion feature and the attention matrix, and the preset formula 6, the attention weight corresponding to the fusion feature is obtained; the formula 6 is: S=Softmax(HW B1 +X); Among them, S is the attention weight corresponding to the fusion feature, W B1 is the pre-set feature transformation weight, and X is the fusion feature.

7. The rhythmic guidance system based on the square dance auxiliary vest according to claim 5 is characterized in that: The physiological state classification model obtains a corresponding state classification result according to the fusion feature and the attention weight corresponding to the fusion feature, including: According to the fusion feature and the attention weight corresponding to the fusion feature, and the preset formula 7, the corresponding state classification result is obtained; the formula 7 is: Y=W B2 ·Dropout[tanh(W B3 ·S·X)]; Among them, Y is the corresponding state classification result, W B2 and W B3 These are all pre-set feature transformation weights.

8. The rhythmic guidance system based on the square dance auxiliary vest according to claim 1 is characterized in that: The system further comprises: a mobile device; The mobile device is used to acquire physiological data acquired by the data acquisition device through the multi-dimensional sensor array in real time; And obtain the status classification results generated by the cloud platform in real time; The data acquisition device is further used to determine whether the wearer is handling abnormal physiological activities based on the physiological data acquired in real time by the multi-dimensional sensor array and the preset physiological data threshold; and when abnormal physiological activity occurs, sounding an alarm and sending an alarm signal to the mobile device; The cloud platform is further used to determine whether the wearer is in an abnormal physiological activity according to the status classification result and the pre-set status level threshold; And, when abnormal physiological activity occurs, an alarm signal is sent to the mobile device.

9. The rhythmic guidance system based on the square dance auxiliary vest according to claim 1 is characterized in that: The data acquisition device is also used to send the music information input by the user to the cloud platform; Then, the cloud platform obtains a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result and a preset correspondence table, including: Obtaining a control instruction for instructing each vibrator in the vibrator group to vibrate according to a prescribed vibration sequence based on the state classification result, a preset correspondence table, and the music information; The corresponding relationship table is a corresponding relationship table between the state classification results and the music information and the vibration sequences of all vibrators in the vibrator group.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, it realizes the rhythmic guidance system based on the square dance auxiliary vest as described in any one of claims 1 to 9.

Images