Motion recommendation method, head-mounted device, storage medium and program product
By acquiring wearer information to construct feature vectors and generate exercise recommendations, the problem of poor exercise effects of existing head-mounted devices is solved, achieving better exercise results and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-26
- Publication Date
- 2026-03-27
AI Technical Summary
Existing head-mounted devices rely on users to manually select exercise content, making it difficult to accurately assess one's physical condition and fitness level, resulting in poor training effects, especially posing a risk of sports injury for beginners or those with poor physical fitness.
By acquiring the wearer's user characteristics and real-time physiological state information, user feature vectors and physiological state feature vectors are constructed to generate recommended exercise information, which is then displayed through a head-mounted device and matched and updated using a cloud server.
It provides exercise recommendations that are more in line with the wearer's objective situation, improves exercise results, reduces the risk of sports injuries, and enhances the exercise experience.
Smart Images
Figure CN121743571A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of head-mounted device technology, and more particularly to a method for recommending exercise, a head-mounted device, a storage medium, and a program product. Background Technology
[0002] With the rapid development of technology, smart wearable devices, especially head-mounted devices (such as smart helmets and smart glasses), have greatly enriched users' sports experience by providing immersive experiences, virtual sports scenarios, or real-time data feedback, lowering the threshold for sports and freeing people from the limitations of traditional sports venues and equipment, thus gradually becoming a new and popular way of sports.
[0003] However, despite the increasing popularity of head-mounted devices in the sports field, most current sports applications using these devices rely on users manually selecting exercise types and setting parameters such as exercise volume and intensity. A significant drawback of this approach is that users often struggle to accurately assess their current physical condition, fitness level, and exercise capacity. Exercise plans set solely based on subjective feelings often fail to achieve optimal training results. This is especially true for beginners or users with poor physical condition; excessive or inappropriate exercise can lead to injuries. In short, the current method of manually selecting exercise content with head-mounted devices is insufficient to achieve effective training.
[0004] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention
[0005] The main objective of this application is to provide an exercise recommendation method, a head-mounted device, a storage medium, and a program product, aiming to solve the technical problem that existing head-mounted devices, which rely on manual selection of exercise content, are unlikely to achieve good training results.
[0006] To achieve the above objectives, this application proposes an exercise recommendation method applied to a head-mounted device. The exercise recommendation method includes:
[0007] Obtain user characteristic information and real-time physiological state information of the wearer of the head-mounted device;
[0008] Based on the user feature information and the real-time physiological state information, construct corresponding user feature vectors and physiological state feature vectors;
[0009] Based on the user feature vector and the physiological state feature vector, recommended exercise information is generated and displayed through the head-mounted device.
[0010] In one embodiment, the step of constructing a corresponding user feature vector based on the user feature information includes:
[0011] Obtain the attribute parameters of the user attribute content and the tendency parameters of the motion tendency content from the user feature information;
[0012] Based on the attribute parameters and the first preset weight, a user attribute feature vector is constructed.
[0013] Based on the aforementioned tendency parameters and the second preset weight, a motion tendency feature vector is constructed.
[0014] The user attribute feature vector and the motion tendency feature vector are used as user feature vectors.
[0015] In one embodiment, the step of constructing a corresponding physiological state feature vector based on the physiological state information includes:
[0016] Obtain the physiological sign parameters from the physiological state information;
[0017] Based on the physiological signs parameters and the third preset weight, a physiological state feature vector is constructed.
[0018] In one embodiment, the step of generating recommended exercise information based on the user feature vector and the physiological state feature vector includes:
[0019] The user feature vector and the physiological state feature vector are sent to a predetermined cloud server, so that the predetermined cloud server matches each candidate exercise information in a predetermined exercise database based on the user feature vector and the physiological state feature vector, and obtains recommended exercise information that matches the user feature vector and the physiological state feature vector.
[0020] Receive recommended exercise information sent by the pre-selected cloud server.
[0021] In one embodiment, the step of displaying the recommended exercise information through the head-mounted device further includes:
[0022] Acquire first motion information and second motion information, wherein the first motion information is the currently displayed recommended motion information, and the second motion information is the recommended motion information generated based on the user's characteristic information and real-time physiological state information at the current moment;
[0023] The first motion information and the second motion information are compared to obtain the information similarity.
[0024] Once the information similarity is less than a predetermined similarity threshold, the recommended motion information displayed by the head-mounted device is updated according to the second motion information.
[0025] In one embodiment, after the step of displaying the recommended exercise information through the head-mounted device, the exercise recommendation method further includes:
[0026] After the recommended exercise content corresponding to the recommended exercise information is completed, relaxation exercise content is obtained and displayed through the head-mounted device;
[0027] Based on the real-time physiological state information, it is determined whether the wearer is in a relaxed state;
[0028] If the wearer is in a relaxed state, the display of the relaxation exercise content will be canceled.
[0029] In one embodiment, the step of obtaining relaxation exercise content includes:
[0030] Obtain a predetermined relaxation action mapping table, wherein the predetermined relaxation action mapping table includes the correspondence between exercise types and relaxation actions;
[0031] Based on the recommended exercise type in the recommended exercise information, the predetermined relaxation action mapping table is queried to obtain at least one target relaxation action, and the target relaxation action is used as the relaxation exercise content.
[0032] In addition, to achieve the above objectives, this application also proposes a head-mounted device, the head-mounted device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the exercise recommendation method as described above.
[0033] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the exercise recommendation method described above.
[0034] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the exercise recommendation method described above.
[0035] One or more technical solutions proposed in this application have at least the following technical effects:
[0036] This application applies to head-mounted devices. It acquires user characteristic information and real-time physiological state information of the wearer of the head-mounted device; based on the user characteristic information and the real-time physiological state information, it constructs corresponding user feature vectors and physiological state feature vectors; based on the user feature vectors and the physiological state feature vectors, it generates recommended exercise information and displays the recommended exercise information through the head-mounted device. Therefore, this application recommends exercise content to the wearer based on relatively fixed user characteristic information such as age, weight, and height, and real-time physiological state information such as heart rate, respiration, and blood pressure. Compared to the traditional method of users selecting exercise content themselves, the recommended exercise information displayed by this application is more closely matched to the wearer's own objective situation, achieving better training results. Attached Figure Description
[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.
[0038] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0039] Figure 1 This is a flowchart illustrating an embodiment of the exercise recommendation method of this application.
[0040] Figure 2 This is a schematic diagram of a scenario involving the exercise recommendation method according to an embodiment of this application;
[0041] Figure 3 This is a flowchart illustrating Embodiment 2 of the exercise recommendation method of this application;
[0042] Figure 4 This is a flowchart illustrating Embodiment 3 of the exercise recommendation method of this application;
[0043] Figure 5 This is a schematic diagram of the device structure of the head-mounted device involved in the embodiments of this application.
[0044] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation
[0045] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.
[0046] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.
[0047] The main solution of this application embodiment is: to obtain user feature information and real-time physiological state information of the wearer of the head-mounted device; to construct corresponding user feature vectors and physiological state feature vectors based on the user feature information and the real-time physiological state information; to generate recommended exercise information based on the user feature vectors and the physiological state feature vectors; and to display the recommended exercise information through the head-mounted device.
[0048] In this embodiment, for ease of description, the following description uses a head-mounted device as the execution subject.
[0049] With the rapid development of technology, smart wearable devices, especially head-mounted devices (such as smart helmets and smart glasses), have greatly enriched users' sports experience by providing immersive experiences, virtual sports scenarios, or real-time data feedback, lowering the threshold for sports and freeing people from the limitations of traditional sports venues and equipment, thus gradually becoming a new and popular way of sports.
[0050] However, despite the increasing popularity of head-mounted devices in the sports field, most current sports applications for head-mounted devices rely on users manually selecting exercise types and setting parameters such as exercise volume and intensity. A significant drawback of this approach is that users often struggle to accurately assess their current physical condition, fitness level, and exercise capacity. Exercise plans set solely based on subjective feelings often fail to achieve optimal training results. This is especially true for beginners or users with poor physical condition, where excessive or inappropriate exercise can lead to injuries. Traditional exercise recommendation systems are primarily designed for traditional sports such as outdoor running and gym workouts, focusing on recommending suitable exercises or plans based on the user's exercise history, preferences, and goals. However, these systems fall short in the new sports scenarios created by head-mounted devices. In other words, the current method of manually selecting exercise content with existing head-mounted devices is insufficient to achieve good training results.
[0051] This application provides a solution that recommends exercise content to the wearer based on relatively fixed user characteristics such as age, weight, and height, as well as real-time physiological status information such as heart rate, respiration, and blood pressure. Compared with the traditional method of users selecting exercise content themselves, the recommended exercise information presented to the wearer in this application is more matched with the wearer's own objective situation, and can achieve better training results.
[0052] Based on this, embodiments of this application provide a method for recommending exercise, referring to... Figure 1 , Figure 1This is a flowchart illustrating the first embodiment of the exercise recommendation method of this application.
[0053] In this embodiment, the exercise recommendation method is applied to a head-mounted device, and the exercise recommendation method includes steps S10 to S30:
[0054] Step S10: Obtain user characteristic information and real-time physiological status information of the wearer of the head-mounted device;
[0055] It should be noted that the execution subject of this embodiment is a head-mounted device, which may include VR (Virtual Reality) helmets, AR (Augmented Reality) glasses, MR (Mixed Reality) helmets, and other head-mounted smart wearable devices.
[0056] It should also be noted that the user characteristic information includes at least one of the following: the wearer's username, age, gender, height, weight, body fat percentage, BMI (Body Mass Index), or exercise tendency information such as preferred exercise methods, exercise time, and exercise intensity. The real-time physiological status information refers to the real-time collection of the wearer's physiological parameters, such as heart rate, blood oxygen saturation, body temperature, and resting heart rate.
[0057] This embodiment can use the user's currently entered or pre-entered user attribute content and exercise tendency content as the wearer's user characteristic information. The wearer's real-time physiological state information can be obtained by collecting the wearer's physiological characteristic parameters through the physiological characteristic detection device (such as an infrared thermometer, heart rate monitor, etc.) configured on the head-mounted device itself, or by communicating with devices that have physiological characteristic detection functions (such as smart bracelets, smartwatches, etc.) to obtain the wearer's real-time physiological state information collected by that device.
[0058] Step S20: Based on the user feature information and the real-time physiological state information, construct the corresponding user feature vector and physiological state feature vector;
[0059] In this embodiment, after preprocessing the user feature information and the real-time physiological state information by denoising, standardizing, and imputing missing values, feature extraction is performed on each attribute parameter and / or each tendency parameter in the user feature information to obtain each user feature quantity. Then, the user feature quantities are combined into a user feature vector. Similarly, feature extraction is performed on each physiological sign parameter in the real-time physiological state information to obtain each physiological sign feature quantity. Then, the physiological sign feature quantities are combined into a physiological state feature vector. Further, taking the user feature information as including each attribute parameter of user attribute content and each tendency parameter of movement tendency content as an example, this embodiment can also obtain each attribute parameter of user attribute content and each tendency parameter of movement tendency content in the user feature information, and then construct a user attribute feature vector based on each attribute parameter and a first preset weight, and construct a movement tendency feature vector based on each tendency parameter and a second preset weight. In this case, the user attribute feature vector and the movement tendency feature vector can be used as the user feature vector. Then, the physiological characteristic parameters in the physiological state information are obtained, and a physiological state feature vector is constructed based on each physiological characteristic parameter and a third preset weight. Therefore, this embodiment can make the recommended exercise information generated based on the user feature vector and the physiological state feature vector more accurate by adjusting the weights corresponding to different parameters (i.e., the first preset weight, the second preset weight, and the third preset weight) according to specific needs.
[0060] In one feasible implementation, step S20, which involves constructing a corresponding user feature vector based on the user feature information, may include steps S21 to S24:
[0061] Step S21: Obtain the attribute parameters of the user attribute content and the tendency parameters of the motion tendency content in the user feature information.
[0062] Step S22: Based on each attribute parameter and the first preset weight, a user attribute feature vector is constructed;
[0063] Step S23: Based on each of the aforementioned tendency parameters and the second preset weight, a motion tendency feature vector is constructed;
[0064] Step S24: Use the user attribute feature vector and the motion tendency feature vector as the user feature vector.
[0065] It should be noted that the user characteristic information includes the attribute parameters of the user attribute content and the tendency parameters of the exercise tendency content. The user attribute content describes the wearer's inherent physical attributes such as username, age, gender, height, weight, body fat, and BMI. The exercise tendency content describes the wearer's preferred exercise methods, exercise time, exercise intensity, and other exercise tendency characteristics.
[0066] It should also be noted that the first preset weight is the weight value set in advance for each attribute parameter in the user attribute content, and the second preset weight is the weight value set in advance for each tendency parameter in the motion tendency content.
[0067] This embodiment can obtain the attribute parameters of the user attribute content and the tendency parameters of the motion tendency content from the user feature information. Then, this embodiment can perform feature extraction on each attribute parameter to obtain the attribute feature quantity of each attribute parameter, and then construct a user attribute feature vector based on each attribute feature quantity and the first preset weight corresponding to each attribute parameter. For example, the user attribute feature vector can be represented as follows:
[0068]
[0069] In the formula, This represents the user attribute feature vector of the wearer u. This represents the nth attribute feature of the wearer u. The first preset weight represents the nth attribute feature of the wearer u.
[0070] Therefore, this embodiment can extract features from each of the aforementioned tendency parameters to obtain tendency feature quantities for each tendency parameter. Then, based on each tendency feature quantity and the second preset weight corresponding to each tendency parameter, a motion tendency feature vector is constructed. For example, the motion tendency feature vector can be represented as follows:
[0071]
[0072] In the formula, This represents the feature vector representing the wearer u's movement tendency. This represents the nth tendency characteristic of the wearer u. The second preset weight represents the nth tendency characteristic of the wearer u.
[0073] Therefore, the user attribute feature vector and the motion tendency feature vector can be used as user feature vectors.
[0074] In this embodiment, by acquiring the attribute parameters of the user attribute content and the tendency parameters of the exercise tendency content from the user feature information; a user attribute feature vector is constructed based on each attribute parameter and a first preset weight; an exercise tendency feature vector is constructed based on each tendency parameter and a second preset weight; and the user attribute feature vector and the exercise tendency feature vector are used as the user feature vector. On the one hand, the constructed user feature vector includes an exercise tendency feature vector representing the wearer's preferred exercise, which makes the recommended exercise more aligned with the wearer's preferences. On the other hand, configuring corresponding weights for different parameters allows for a more comprehensive utilization of different feature quantities, resulting in more accurate recommended exercise information.
[0075] In one feasible implementation, step S20, which involves constructing a corresponding physiological state feature vector based on the physiological state information, may include steps S25 to S26:
[0076] Step S25: Obtain the physiological sign parameters from the physiological state information;
[0077] Step S26: Based on the physiological sign parameters and the third preset weight, a physiological state feature vector is constructed.
[0078] It should be noted that the real-time physiological status information includes physiological parameters such as heart rate, blood oxygen saturation, body temperature, and resting heart rate. The third preset weight is a weight value pre-set for each physiological parameter.
[0079] In this embodiment, after obtaining the physiological sign parameters in the physiological state information, feature extraction can be performed on each physiological sign parameter to obtain the physiological feature quantity of each physiological sign parameter. Then, based on each physiological feature quantity and the third preset weight corresponding to each physiological sign parameter, a physiological state feature vector is constructed. For example, the physiological state feature vector can be represented as follows:
[0080]
[0081] In the formula, This represents the physiological state feature vector of the wearer u. This represents the nth physiological characteristic of the wearer u. The third preset weight represents the nth physiological characteristic of the wearer u.
[0082] In this embodiment, by acquiring the physiological sign parameters from the physiological state information, and constructing a physiological state feature vector based on each physiological sign parameter and a third preset weight, this embodiment configures corresponding weights for different parameters in the physiological state feature vector, which can more comprehensively utilize different feature quantities and make the generated recommended exercise information more accurate.
[0083] Step S30: Based on the user feature vector and the physiological state feature vector, generate recommended exercise information and display the recommended exercise information through the head-mounted device.
[0084] It should be noted that the recommended exercise information may include recommended exercise type, intensity, duration, and other recommended exercise content.
[0085] This embodiment can match candidate exercise information in a predetermined exercise database based on the user feature vector and the physiological state feature vector to obtain recommended exercise information that matches the user feature vector and the physiological state feature vector. The predetermined exercise database includes multiple candidate exercise information, which includes exercise type, intensity, duration, and other exercise content for exercise recommendation. Further, the predetermined exercise database includes a pre-exercise database, a mid-exercise database, and a post-exercise database. This embodiment can obtain the wearer's current exercise duration. When the current exercise duration is within a preset pre-exercise range, the first candidate exercise information in the pre-exercise database can be matched based on the user feature vector and the physiological state feature vector to obtain recommended exercise information for the pre-exercise range. Then, when the current exercise duration is within a preset mid-exercise range, the second candidate exercise information in the mid-exercise database can be matched based on the user feature vector and the physiological state feature vector to obtain recommended exercise information for the mid-exercise range. Finally, when the current exercise duration falls within the preset exercise repercussions range, the third candidate exercise information in the exercise repercussions database can be matched based on the user feature vector and the physiological state feature vector to obtain recommended exercise information for the repercussions. Specifically, the first candidate exercise information in the exercise pre-stage database can be warm-up exercises (such as joint exercises, stretching exercises, and low-intensity simulated movements based on the type of exercise to be performed), the second candidate exercise information in the exercise mid-stage database can be vigorous exercise (such as aerobic exercise, strength training, speed training, and other high-intensity activities), and the third candidate exercise information in the exercise repercussions database can be cool-down exercises (such as light aerobic exercise, stretching exercises, and deep breathing exercises for recovery). Therefore, this application can further improve exercise effectiveness by recommending suitable exercise content in real time based on the user's physiological state and user attributes during the early, middle, and late stages of exercise.
[0086] Therefore, in this embodiment, the recommended exercise information can be displayed directly through the head-mounted device in the form of audio, video, text, and images. Alternatively, the recommended exercise information can be processed by the exercise application mounted on the head-mounted device, and the processed recommended exercise information can be displayed in the form of audio, video, text, and images.
[0087] In one feasible implementation, step S30 may include steps S31 to S32:
[0088] Step S31: Send the user feature vector and the physiological state feature vector to a predetermined cloud server, so that the predetermined cloud server matches each candidate exercise information in the predetermined exercise database based on the user feature vector and the physiological state feature vector to obtain recommended exercise information that matches the user feature vector and the physiological state feature vector.
[0089] Step S32: Receive the recommended exercise information sent by the predetermined cloud server.
[0090] It should be noted that the predetermined cloud server is equipped with a matching algorithm for matching candidate exercise information in the predetermined exercise database based on the user feature vector and the physiological state feature vector. Furthermore, the predetermined exercise database can also be configured on the predetermined cloud server. The predetermined exercise database includes multiple candidate exercise information entries, which are exercise types, intensity levels, durations, and other exercise content used for exercise recommendations.
[0091] See Figure 2 , Figure 2 This is a schematic diagram of a sports recommendation scenario according to an embodiment of this application. Due to the limited size of the head-mounted device, the computational resources it can provide are also limited. To further improve the efficiency and accuracy of sports recommendations, this embodiment can send the user feature vector and the physiological state feature vector to a predetermined cloud server. The predetermined cloud server then matches candidate sports information in a predetermined sports database based on the user feature vector and the physiological state feature vector to obtain recommended sports information that matches the user feature vector and the physiological state feature vector. The recommended sports information is received from the predetermined cloud server. Therefore, this embodiment implements the matching process by the predetermined cloud server, effectively reducing the computational resource pressure on the head-mounted device and thus improving the accuracy of the recommended sports information.
[0092] The first embodiment of this application provides an exercise recommendation method applied to a head-mounted device. It acquires user characteristic information and real-time physiological state information of the wearer of the head-mounted device; constructs corresponding user feature vectors and physiological state feature vectors based on the user characteristic information and the real-time physiological state information; generates recommended exercise information based on the user feature vectors and the physiological state feature vectors, and displays the recommended exercise information through the head-mounted device. Therefore, this embodiment recommends exercise content to the wearer based on relatively fixed user characteristic information such as age, weight, and height, and real-time physiological state information such as heart rate, respiration, and blood pressure. Compared to the traditional method of users selecting exercise content themselves, the recommended exercise information displayed to the wearer in this embodiment is more closely matched to the wearer's own objective situation, achieving better training results.
[0093] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 Before step S30, including steps A10 to A30:
[0094] Step A10: Obtain first motion information and second motion information, wherein the first motion information is the currently displayed recommended motion information, and the second motion information is the recommended motion information generated based on the user's feature information and real-time physiological state information at the current moment;
[0095] Step A20: Compare the first motion information and the second motion information to obtain the information similarity.
[0096] Step A30: After the information similarity is less than a predetermined similarity threshold, the recommended motion information displayed by the head-mounted device is updated according to the second motion information.
[0097] It should be noted that the first motion information is the currently displayed recommended motion information, while the second motion information is the recommended motion information generated based on the user's current characteristic information and real-time physiological state information.
[0098] In this embodiment, the generation of recommended exercise information is performed in real time. Therefore, this embodiment obtains first exercise information and second exercise information, where the first exercise information is the currently displayed recommended exercise information, and the second exercise information is the recommended exercise information generated based on the user's current characteristic information and real-time physiological state information. This allows us to determine the currently displayed recommended exercise information and the currently generated recommended exercise information. Furthermore, this embodiment compares the first exercise information and the second exercise information to obtain information similarity, thereby determining whether there are significant differences between the currently displayed recommended exercise information and the currently generated recommended exercise information, such as changes in exercise type, exercise intensity, or exercise duration. If the information similarity is less than a predetermined similarity threshold, the currently displayed recommended exercise information differs significantly from the currently generated recommended exercise information, and the currently displayed recommended exercise information is no longer suitable for the wearer at the current time. In this case, the recommended exercise information displayed by the head-mounted device can be updated according to the second exercise information. For example, the second motion information can be directly used as new recommended motion information displayed by the head-mounted device, or the second motion information can be processed by the motion application mounted on the head-mounted device, and the processed second motion information can be used as new recommended motion information displayed by the head-mounted device.
[0099] In the second embodiment of this application, first motion information and second motion information are obtained, wherein the first motion information is the currently displayed recommended motion information, and the second motion information is the recommended motion information generated based on the user's current characteristic information and real-time physiological state information; the first motion information and the second motion information are compared to obtain information similarity; after the information similarity is less than a predetermined similarity threshold, the recommended motion information displayed by the head-mounted device is updated according to the second motion information. Therefore, this embodiment avoids the recommended motion information displayed by the head-mounted device changing in real time, which could affect the wearer's viewing experience and the stability of the display.
[0100] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 The steps following step S30 include steps B10 to B30:
[0101] Step B10: After the recommended exercise content corresponding to the recommended exercise information is completed, obtain the relaxation exercise content and display the relaxation exercise content through the head-mounted device;
[0102] Step B20: Based on the real-time physiological state information, determine whether the wearer is in a relaxed state;
[0103] Step B30: If the wearer is in a relaxed state, then the display of the relaxation exercise content is cancelled.
[0104] It should be noted that the soothing state refers to the body's recovery state after exercise, such as a calm heart rate, normal body temperature, and relaxed breathing.
[0105] In this embodiment, after the recommended exercise content corresponding to the recommended exercise information is completed, relaxation exercise content is obtained. For example, this embodiment can match each fourth candidate exercise information in the relaxation exercise database based on the user feature vector and the physiological state feature vector to obtain the matched fourth candidate exercise information as the relaxation exercise content. Further, this embodiment can also obtain a predetermined relaxation action mapping table, wherein the predetermined relaxation action mapping table includes the correspondence between exercise types and relaxation actions; according to the recommended exercise type in the recommended exercise information, the predetermined relaxation action mapping table is queried to obtain at least one target relaxation action, and the target relaxation action is used as the relaxation exercise content. Thus, this embodiment can specifically perform corresponding relaxation actions for the recommended exercise content to relieve muscle fatigue caused by the exercise content in the recommended exercise information. Then, the relaxation exercise content is displayed through the head-mounted device so that the wearer can perform relaxation exercises based on the relaxation exercise content. Then, based on the real-time physiological state information, it is determined whether the wearer is in a relaxed state; if the wearer is in a relaxed state, it indicates that the wearer has recovered their physical state well, and the display of the relaxation exercise content can be canceled.
[0106] In one feasible embodiment, the step of obtaining relaxation exercise content in step B20 includes steps C10 to C20:
[0107] Step C10: Obtain a predetermined relaxation action mapping table, wherein the predetermined relaxation action mapping table includes the correspondence between exercise types and relaxation actions;
[0108] Step C20: Based on the recommended exercise type in the recommended exercise information, query the predetermined relaxation action mapping table to obtain at least one target relaxation action, and use the target relaxation action as the relaxation exercise content.
[0109] It should be noted that the predetermined relaxation action mapping table includes the correspondence between exercise types and relaxation actions. The relaxation actions may include movements such as slow walking, leg stretching, and waist stretching to relax the muscles of different parts of the body.
[0110] This embodiment constructs a predetermined relaxation action mapping table in advance based on the correspondence between exercise types and relaxation actions. Then, after the recommended exercise content corresponding to the recommended exercise information is completed, this embodiment can obtain the predetermined relaxation action mapping table, which includes the correspondence between exercise types and relaxation actions. Based on the recommended exercise type in the recommended exercise information, the predetermined relaxation action mapping table is queried to obtain at least one target relaxation action, which is then used as the relaxation exercise content. This allows for the selection of target relaxation actions suitable for the exercise types in the recommended exercise information. For example, if the exercise type in the recommended exercise information involves a lot of leg muscle activity, then leg stretching, slow walking, and other leg muscle relaxation exercises can be selected as target relaxation actions.
[0111] This embodiment obtains a predetermined relaxation action mapping table, which includes the correspondence between exercise types and relaxation actions. Based on the recommended exercise types in the recommended exercise information, the predetermined relaxation action mapping table is queried to obtain at least one target relaxation action, which is then used as the relaxation exercise content. Therefore, this embodiment can specifically perform corresponding relaxation actions for the recommended exercise content to relieve muscle fatigue caused by the recommended exercise content, thereby effectively improving the muscle relaxation effect after exercise.
[0112] In the third embodiment of this application, after completing the recommended exercise content corresponding to the recommended exercise information, relaxation exercise content is obtained and displayed through the head-mounted device. Based on the real-time physiological state information, it is determined whether the wearer is in a relaxed state; if the wearer is in a relaxed state, the display of the relaxation exercise content is canceled. This embodiment, after completing the recommended exercise content, guides the wearer to perform stretching and relaxation exercises after exercise, which can better reduce muscle soreness caused by exercise, quickly restore bodily functions, and, by monitoring real-time physiological state information, identify the wearer's degree of relaxation, thereby achieving better post-exercise recovery.
[0113] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the motion recommendation method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.
[0114] This application provides a head-mounted device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the exercise recommendation method in Embodiment 1 above.
[0115] The following is for reference. Figure 5 The diagram illustrates a structural schematic suitable for implementing the head-mounted device in the embodiments of this application. The head-mounted device in the embodiments of this application may include VR (Virtual Reality) helmets, AR (Augmented Reality) glasses, MR (Mixed Reality) helmets, and other head-mounted smart wearable devices. Figure 5 The head-mounted device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.
[0116] like Figure 5 As shown, the head-mounted device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the head-mounted device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, display modules, speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tape, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the head-mounted device to communicate wirelessly or wiredly with other devices to exchange data. While head-mounted devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.
[0117] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.
[0118] The head-mounted device provided in this application, employing the exercise recommendation method described in the above embodiments, can solve the technical problem that existing head-mounted devices, which rely on manual selection of exercise content, are unlikely to achieve good training results. Compared with the prior art, the beneficial effects of the head-mounted device provided in this application are the same as those of the exercise recommendation method provided in the above embodiments, and other technical features of this head-mounted device are the same as those disclosed in the previous embodiment method, and will not be repeated here.
[0119] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.
[0120] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
[0121] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the motion recommendation method in the above embodiments.
[0122] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
[0123] The aforementioned computer-readable storage medium may be included in the head-mounted device; or it may exist independently and not assembled into the head-mounted device.
[0124] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a head-mounted device, cause the head-mounted device to: acquire user characteristic information and real-time physiological state information of the wearer of the head-mounted device; construct corresponding user feature vectors and physiological state feature vectors based on the user characteristic information and the real-time physiological state information; generate recommended exercise information based on the user feature vectors and the physiological state feature vectors; and display the recommended exercise information through the head-mounted device.
[0125] Computer program code for performing the operations of this application can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0126] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
[0127] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.
[0128] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described exercise recommendation method. This solves the technical problem that existing head-mounted devices, which rely on manual selection of exercise content, struggle to achieve good training results. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the exercise recommendation method provided in the above embodiments, and will not be elaborated upon here.
[0129] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the exercise recommendation method described above.
[0130] The computer program product provided in this application can solve the technical problem that existing head-mounted devices, which rely on manual selection of exercise content, are unlikely to achieve good training results. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the exercise recommendation method provided in the above embodiments, and will not be repeated here.
[0131] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.
Claims
1. A method for recommending exercise, characterized in that, When applied to head-mounted devices, the exercise recommendation method includes: Obtain user characteristic information and real-time physiological state information of the wearer of the head-mounted device; Based on the user feature information and the real-time physiological state information, construct corresponding user feature vectors and physiological state feature vectors; Based on the user feature vector and the physiological state feature vector, recommended exercise information is generated and displayed through the head-mounted device.
2. The exercise recommendation method as described in claim 1, characterized in that, The steps for constructing the corresponding user feature vector based on the user feature information include: Obtain the attribute parameters of the user attribute content and the tendency parameters of the motion tendency content from the user feature information; Based on the attribute parameters and the first preset weight, a user attribute feature vector is constructed. Based on the aforementioned tendency parameters and the second preset weight, a motion tendency feature vector is constructed. The user attribute feature vector and the motion tendency feature vector are used as user feature vectors.
3. The exercise recommendation method as described in claim 2, characterized in that, The step of constructing a corresponding physiological state feature vector based on the physiological state information includes: Obtain the physiological sign parameters from the physiological state information; Based on the physiological signs parameters and the third preset weight, a physiological state feature vector is constructed.
4. The exercise recommendation method as described in claim 1, characterized in that, The step of generating recommended exercise information based on the user feature vector and the physiological state feature vector includes: The user feature vector and the physiological state feature vector are sent to a predetermined cloud server, so that the predetermined cloud server matches each candidate exercise information in a predetermined exercise database based on the user feature vector and the physiological state feature vector, and obtains recommended exercise information that matches the user feature vector and the physiological state feature vector. Receive recommended exercise information sent by the pre-selected cloud server.
5. The exercise recommendation method as described in claim 1, characterized in that, The step of displaying the recommended exercise information through the head-mounted device further includes: Acquire first motion information and second motion information, wherein the first motion information is the currently displayed recommended motion information, and the second motion information is the recommended motion information generated based on the user's characteristic information and real-time physiological state information at the current moment; The first motion information and the second motion information are compared to obtain the information similarity. Once the information similarity is less than a predetermined similarity threshold, the recommended motion information displayed by the head-mounted device is updated according to the second motion information.
6. The method according to any one of claims 1 to 5, characterized in that, After the step of displaying the recommended exercise information through the head-mounted device, the exercise recommendation method further includes: After the recommended exercise content corresponding to the recommended exercise information is completed, relaxation exercise content is obtained and displayed through the head-mounted device; Based on the real-time physiological state information, it is determined whether the wearer is in a relaxed state; If the wearer is in a relaxed state, the display of the relaxation exercise content will be canceled.
7. The method as described in claim 6, characterized in that, The steps for obtaining relaxation exercise content include: Obtain a predetermined relaxation action mapping table, wherein the predetermined relaxation action mapping table includes the correspondence between exercise types and relaxation actions; Based on the recommended exercise type in the recommended exercise information, the predetermined relaxation action mapping table is queried to obtain at least one target relaxation action, and the target relaxation action is used as the relaxation exercise content.
8. A head-mounted device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the exercise recommendation method as described in any one of claims 1 to 7.
9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the motion recommendation method as described in any one of claims 1 to 7.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the exercise recommendation method as described in any one of claims 1 to 7.