Wearable device, electronic device, and storage medium, and suitability evaluation method

Abstract

The present disclosure relates to a method for fit assessment of a wearable device, an electronic device, and a storage medium. The method can include obtaining data of a target body part of a user, obtaining at least one biophysical characteristic of the user based on the data of the target body part, obtaining data of at least one personal preference of the user, and providing recommendation information for the fit assessment of the wearable device to the user based on the at least one biophysical characteristic of the user and the data of at least one personal preference.

Description

Technical Field

[0001] The embodiments disclosed herein relate to a method for compatibility assessment of wearable devices, electronic devices, and storage media. Background Technology

[0002] As an emerging smart technology product, wearable devices have permeated people's daily lives, encompassing various forms such as smartwatches, smart bracelets, smart glasses, and smart clothing. By integrating advanced sensor technology, wireless communication technology, and artificial intelligence algorithms, these wearable devices can monitor users' physiological data, behavioral habits, and environmental information in real time, thus providing personalized health monitoring, life assistant, exercise guidance, and other functions.

[0003] In today's digital consumer era, online shopping has gradually become the mainstream shopping method for consumers. However, compared with traditional offline shopping, one of the main drawbacks of online shopping is that consumers cannot directly experience and try out physical products, especially in the wearable device market where various wearable devices with different sizes, materials, and functions are available. Because wearable devices need to fit closely to the human body, consumers often find it difficult to accurately determine which wearable device truly matches their physiological characteristics and personalized needs without actual try-on experience. With the development of smart technology and consumers' demand for personalized experiences, how to efficiently and scientifically recommend the most suitable wearable devices to consumers has undoubtedly become an urgent problem to be solved. Summary of the Invention

[0004] One or more embodiments of this disclosure provide a method for suitability assessment of a wearable device, the method comprising: acquiring data of a target body part of a user; obtaining at least one bio-appearance characteristic of the user based on the data of the target body part; acquiring data of at least one personal preference of the user; and providing the user with recommendation information for suitability assessment of the wearable device based on the user's at least one bio-appearance characteristic and the at least one personal preference data.

[0005] One or more embodiments of this disclosure provide an electronic device, the electronic device comprising: at least one processor; and a memory including one or more computer program modules. The one or more computer program modules are stored in the memory and include instructions for implementing the methods provided in the one or more embodiments above, upon execution by the at least one processor.

[0006] One or more embodiments of this disclosure provide a non-transitory computer-readable storage medium, which includes computer instructions. The computer instructions, when executed by at least one processor, perform the methods provided in the one or more of the above embodiments. Attached Figure Description

[0007] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Obviously, the drawings described below are only related to some embodiments of this disclosure and are not intended to limit this disclosure.

[0008] Figure 1 This is a flowchart of a method for adaptability assessment of a wearable device according to one or more embodiments of this disclosure;

[0009] Figure 2 This is a flowchart of a method for adaptability assessment of a wearable device according to one or more embodiments of this disclosure;

[0010] Figure 3A , 3B 3C, 3D, 3E, 3F and 3G are schematic diagrams of visual interfaces according to one or more embodiments of the present disclosure;

[0011] Figure 4 This is a schematic block diagram of an electronic device according to one or more embodiments of the present disclosure;

[0012] Figure 5 This is a schematic block diagram of another electronic device according to one or more embodiments of the present disclosure; and

[0013] Figure 6 This is a schematic block diagram of a non-transient computer-readable storage medium according to one or more embodiments of the present disclosure. Detailed Implementation

[0014] To make the objectives, technical details, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments are described in a clear and fully understandable manner in conjunction with the accompanying drawings related to the embodiments of this disclosure. Obviously, the described embodiments are only a part of, and not all of, the embodiments of this disclosure. Based on the embodiments described herein, those skilled in the art can obtain one or more other embodiments without any inventive effort, and these embodiments should be within the scope of this disclosure.

[0015] This disclosure uses flowcharts to illustrate operations performed by a system according to an embodiment of this application. It should be understood that preceding or subsequent operations do not necessarily need to be performed in a strict order. Rather, as needed, various steps can be processed in reverse order or simultaneously. Furthermore, additional operations can be added to these procedures, or steps or steps can be removed from them.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” etc., used in the specification and claims of this disclosure are not intended to indicate any sequence, quantity, or importance, but are used to distinguish various components. Similarly, the terms “a,” “an,” “described,” etc., are not intended to indicate a limitation in quantity, but rather to indicate the presence of at least one. Terms such as “comprising” are intended to specify that elements or objects stated before these terms encompass elements or objects listed after these terms and their equivalents, but do not exclude other elements or objects. Terms such as “connection,” “linkage,” etc., are not limited to physical or mechanical connections, but may also directly or indirectly include electrical connections / linkages. The terms “upper,” “lower,” “left,” “right,” etc., are used only to indicate relative positional relationships, and the relative positional relationships change accordingly when the position of the described objects changes.

[0017] One or more embodiments of this disclosure provide a method for suitability assessment of a wearable device, the method comprising: acquiring data of a target body part of a user; obtaining at least one bio-appearance characteristic of the user based on the data of the target body part; acquiring data of at least one personal preference of the user; and providing the user with recommendation information for suitability assessment of the wearable device based on the user's at least one bio-appearance characteristic and the at least one personal preference data.

[0018] The methods for wearable device fit assessment provided in one or more embodiments of this disclosure above provide users with highly personalized recommendations for wearable device fit assessment by acquiring and analyzing at least one biometric appearance characteristic and at least one personal preference of the user. This helps users select wearable devices that better match their appearance characteristics and personal needs.

[0019] Furthermore, the methods for adaptability assessment of wearable devices provided in one or more embodiments of this disclosure are compatible with various platforms and devices, thereby broadly meeting the diverse needs of different users and providing a foundation for building user-friendly and easy-to-operate systems for adaptability assessment of wearable devices.

[0020] Figure 1This is a flowchart of a method for adaptability evaluation of a wearable device according to one or more embodiments of this disclosure. For example, the method can be implemented using software running on various types of terminal devices (see attached diagram for details). Figure 4 and Figure 5 (Refer to the following instructions).

[0021] like Figure 1 As shown, the method for adaptability assessment of wearable devices provided by one or more embodiments of the present disclosure includes the following S101 to S104.

[0022] At S101, data on the user's target body part is obtained.

[0023] In one or more embodiments of this disclosure, the data for the target body part includes, but is not limited to, at least one of eye data, ear data, head data, hand data, foot data, and body shape data. Depending on the type of wearable device, different target body part data may be acquired. For example, when the wearable device is smart glasses, the target body part data may be eye data. When the wearable device is headphones, the target body part data may be ear data. When the wearable device is a VR (virtual reality) headset, the target body part data may be head data. When the wearable device is a smartwatch, the target body part data may be hand data. When the wearable device is smart sneakers, the target body part data may be foot data. When the wearable device is a smart belt or smart clothing, the target body part data may be body shape data.

[0024] At S102, at least one biological appearance characteristic of the user is obtained based on data from the target body part.

[0025] For example, at least one biological appearance characteristic of a user can be obtained through a deep learning model or machine learning model based on data from a target body part. For example, the deep learning model can be a convolutional neural network (CNN) and its various extended implementations, such as VGG (VGG-16, VGG-19), GoogLeNet / Inception, ResNet (residual network), generative pre-trained transformers, etc. For example, the machine learning model can be a support vector machine (SVM), principal component analysis (PCA), linear discriminant analysis (LDA), etc. For example, at least one biological appearance characteristic can include various data, such as shape, contour, size, etc.

[0026] At S103, obtain data on at least one of the user's personal preferences.

[0027] For example, data on at least one of a user's personal preferences can be obtained through questionnaires, user profiling, behavioral data collection, and big data analytics. For instance, a questionnaire can be provided within the software. At least one personal preference might include at least one of the following: daily use of the wearable device, price range, and functional requirements.

[0028] At S104, recommendations for wearable device fit assessment are provided to the user based on at least one biometric characteristic and at least one personal preference.

[0029] For example, at least one biometric appearance feature vector and at least one personal preference vector can be extracted and fused by weights to construct a multidimensional comprehensive scoring system. Based on the scoring system, recommendation information that conforms to the user's personal characteristics and preferences can be obtained. For example, a deep learning fusion model can be used to extract and fuse multimodal features of at least one biometric appearance feature and at least one personal preference of the user to generate recommendation information. For example, a hierarchical decision tree can be constructed, where the first layer provides preliminary recommendation information based on at least one biometric appearance feature of the user, and the second layer refines the preliminary recommendation information based on at least one personal preference of the user. The specific algorithm will vary depending on the needs of different types of wearable devices, and one or more embodiments of this disclosure are not limited thereto.

[0030] For example, the recommendation information includes at least one of the attributes and rating levels of the recommended wearable device. For example, the attributes of the recommended wearable device include at least one of type, performance, functionality, appearance, size, and model.

[0031] Some current methods rank multiple recommended wearable devices directly based on recommendation ratings output by the algorithm. However, due to floating-point errors and inherent uncertainties in the algorithms, the results can exhibit fluctuations, small but significant enough to affect the ranking order. For example, when the same user performs multiple tests, the ranking results obtained from those tests may be inconsistent. For instance, in the first test, wearable device A receives a rating of 5.01, and wearable device B receives a rating of 5.02; in the second test, wearable device A receives a rating of 5.02, and wearable device B receives a rating of 5.01; in the second test, wearable device A receives a rating of 5.02, and wearable device B receives a rating of 5.01; in the second test, wearable device A receives a ranking higher than wearable device B. This can lead to confusion for users regarding the recommendation results.

[0032] In one or more embodiments of this disclosure, the continuous ratings output by the algorithm are segmented and mapped to multiple rating levels (e.g., levels 1 to 10), and the recommended wearable devices are ranked based on the rating levels. In this way, the impact of floating-point errors and inherent uncertainties in the algorithm can be effectively reduced, thereby ensuring more stable and reliable recommendation results.

[0033] In one or more embodiments of this disclosure, obtaining data on a user's target body parts may include collecting data on the user's target body parts via a collection device.

[0034] For example, the collecting device may be a visible light camera, an infrared camera, an ultrasonic sensor, a lidar, a time-of-flight (ToF) sensor, and other different types of collecting devices provided in the terminal device. The terminal device may be a mobile phone, a laptop computer, etc., which is equipped with at least one of the above collecting devices. In one or more embodiments of this disclosure, data of the target body part is collected in response to the target body part being located within the collecting area of ​​the collecting device.

[0035] In one or more embodiments of this disclosure, collecting data on a user’s target body part via a collection device may include collecting data on the target body part in response to the target body part being located within the collection area of ​​the collection device.

[0036] For example, the collection area of ​​the collecting device reflects the physical space area in which the collecting device can effectively sense, capture, and record information. For example, a terminal device including the collecting device may also include a display configured to display in real time an image collected by the collecting device within a preset frame, where the preset frame may represent the collection area of ​​the collecting device. If a target body part appears entirely within the preset frame (where the user can see the image of the target body part collected by the collecting device within the preset frame), it is determined that the target body part is located within the collection area of ​​the collecting device.

[0037] For example, existing image recognition algorithms (such as those provided by the OpenCV library) can be used to analyze images collected by a collection device to determine whether a target body part is located within the collection area.

[0038] In one or more embodiments of this disclosure, before collecting data on a target body part in response to the target body part being located within the collection area of ​​the collection device, the method may include: determining the distance between a user and the collection device; and providing a prompt to guide the user to move closer to the collection device in response to the distance being greater than a first distance threshold.

[0039] For example, the distance between the user and the collection device can be determined by ultrasonic ranging, infrared ranging, laser ranging, depth camera ranging, etc.

[0040] For example, a first distance threshold and a second distance threshold can be preset, and when the distance is not greater than the first distance threshold and not less than the second distance threshold, the distance is determined to meet the preset threshold. The first distance threshold and the second distance threshold can be preset according to the actual situation, and are not limited to the examples described in this disclosure.

[0041] In one or more embodiments of this disclosure, the prompt includes at least one of voice prompts, tone prompts, vibration prompts, and visual prompts. For example, it may be one of the above prompts or a combination of multiple prompts, and is not limited to one or more embodiments of this disclosure.

[0042] For example, a prompt animation may be played to guide the user to move and position the target body part within the collection area of ​​the collection device. For example, the prompt text "Please position the target body part within the collection area of ​​the collection device" might be displayed. For example, the prompt voice "Please position the target body part within the collection area of ​​the collection device" might be played. It should be noted that the above prompts are merely examples, and different prompts may be used depending on the actual situation, and are not limited to the examples described in this disclosure.

[0043] In one or more embodiments of this disclosure, after determining the distance between the user and the collection device, the method may include providing a prompt to guide the user to move closer to the collection device in response to the distance being greater than a first distance threshold; or providing a prompt to guide the user to move away from the collection device in response to the distance being less than a second distance threshold.

[0044] For example, when the distance between the user and the collection device exceeds a first distance threshold, this indicates that the user is too far from the collection device and data on the target body part cannot be accurately collected. Therefore, it is necessary to prompt the user to move closer to the collection device.

[0045] For example, a prompt animation can be played, in which arrows can be used to guide the user to move closer to the collection device. For example, the prompt text "Please move closer to the collection device" might be displayed. For example, a prompt voice message "Please move closer to the collection device" might be played. It should be noted that the above prompts are merely examples, and different prompts may be used depending on the actual situation, and are not limited to the examples described in this disclosure.

[0046] For example, when the distance between the user and the collection device is less than a second distance threshold, this indicates that the user is too close to the collection device and data on the target body part cannot be accurately collected. Therefore, it is necessary to prompt the user to move away from the collection device.

[0047] For example, a prompt animation could be played, in which arrows can be used to guide the user to move away from the collection device. For example, the prompt text "Please move away from the collection device" might be displayed. For example, a prompt voice message "Please move away from the collection device" might be played. It should be noted that the above prompts are merely examples, and different prompts may be used depending on the actual situation, and are not limited to the examples described in this disclosure.

[0048] In one or more of the above-described embodiments of this disclosure, by means of real-time distance sensing and various prompting methods, users can intuitively understand whether the distance between themselves and the collection device is appropriate. Therefore, inaccurate data collection due to the user being too far from the collection device, or incomplete data collection due to the user being too close to the collection device, can be avoided, thereby improving the user experience.

[0049] In one or more embodiments of this disclosure, a collection progress indicator may be provided during the process of collecting data on the target body part, and a collection completion indicator may be provided at the end of the process of collecting data on the target body part.

[0050] For example, a collection progress bar or percentage number can be displayed on the monitor in real time, and the progress bar will gradually fill or the percentage number will increase as the collection process progresses. When collection is complete, a "√" icon may be displayed to indicate that collection is complete. For example, as the collection process continues, the prompt text "Collecting" may be displayed, indicating that collection is in progress. For example, when collection is complete, the prompt text "Collection Complete" may be displayed, indicating that collection is complete. For example, a continuous slight vibration indicates that collection is in progress, while a strong vibration indicates that collection is complete. For example, a gradually rising tone indicates that collection is in progress, while a continuous high-frequency tone indicates that collection is complete. It should be noted that the above prompts are only some examples, and different prompts may be used depending on the actual situation, and are not limited to the examples described in this disclosure.

[0051] In one or more of the above-described embodiments of this disclosure, real-time progress updates allow users to clearly understand the current collection status and how much more time is needed or how to cooperate with the collection device to complete the collection. This ensures that all necessary data has been fully collected and avoids data loss or inaccuracy due to premature termination or movement by the user.

[0052] In one or more embodiments of this disclosure, existing cross-platform development technologies and unified standard interfaces are used, thereby supporting seamless integration with mainstream operating systems (e.g., iOS, Android, Windows, and macOS), allowing users to easily access and use the compatibility assessment service on any supported device. Therefore, the method for compatibility assessment of wearable devices is compatible with various platforms and devices, thus broadly meeting the diverse needs of different users and providing a foundation for building user-friendly and easy-to-use systems for compatibility assessment of wearable devices.

[0053] For example, taking the ear as the target body part and a camera as the collecting device, Figure 2 This is a flowchart of a method for adaptability assessment of a wearable device according to one or more embodiments of this disclosure. Figures 3A to 3G It is a schematic diagram of a visual interface according to one or more embodiments of the present disclosure, and the visual interface can be displayed on a monitor.

[0054] like Figure 2 As shown, the method for adaptability assessment of wearable devices according to one or more embodiments of the present disclosure includes the following steps S201 to S211.

[0055] At S201, determine whether to grant camera access. If yes, the method proceeds to S203; otherwise, the method proceeds to S202.

[0056] For example, if the method runs on a platform such as Windows, macOS, Android, or iOS, the corresponding platform's API can be used to determine whether to grant camera access. If the method runs on a web application (e.g., a PWA), most browsers (e.g., Chrome, Edge, Firefox, Safari) also provide an interface to determine whether to grant camera access.

[0057] At S202, the user is prompted and guided to grant camera access. The method then returns to S201. For example, in this case, a visual cue can be used, and the visual cue can be in text form.

[0058] At point S203, the user is prompted to position their head within the collection area. For example, in this scenario, both voice and visual cues can be used, and visual cues could be presented as follows: Figure 3A As shown in the image.

[0059] For example, such as Figure 3AAs shown, a preset frame 300 is displayed in the visualization interface to guide the user to move their head so that the user's portrait appears in the preset frame 300 (i.e., the user can see the portrait collected by the camera in real time in the preset frame). For example, the prompt text "Please position your head within the collection area" can be displayed in the text box 301 below the preset frame 300.

[0060] At S204, it is determined whether the user's head is positioned within the collection area. If yes, the method proceeds to S204; otherwise, the method returns to S203. For example, when the user's portrait appears in the preset frame 300 (i.e., when the user can see the portrait collected by the collection device within the preset frame), it is determined that the user's head is positioned within the collection area. For example, the prompt text "Head positioned within the collection area" can be displayed in the text box 301 below the preset frame 300.

[0061] At step S205, the distance between the user and the camera is determined. If the distance is greater than a first distance threshold T1, the method proceeds to step S206. If the distance is less than a first distance threshold T2, the method proceeds to step S207. If the distance meets a preset threshold (e.g., the distance is not greater than the first distance threshold T1 and not less than the second distance threshold T2), the method proceeds to step S208. For example, detailed information about step S205 can be found in the above description and will not be repeated here.

[0062] At point S206, the user is prompted to move closer to the camera. For example, both voice and visual cues can be used, and visual cues can be, for example, as follows: Figure 3B As shown in the image.

[0063] For example, such as Figure 3B As shown, an arrow 302 pointing in the direction of the portrait will be displayed in the preset box 300, guiding the user to move closer to the camera. For example, the prompt text "Please move closer to the camera" can be displayed in the text box 301 below the preset box 300.

[0064] At point S207, the user is prompted to move away from the camera. For example, this can be done using both voice and visual cues, and the visual cues could be, for example, as follows: Figure 3C As shown in the image.

[0065] For example, such as Figure 3C As shown, an arrow 303 pointing away from the portrait will appear in the preset box 300, instructing the user to move away from the camera. For example, the prompt text "Please move away from the camera" can be displayed in the text box 301 below the preset box 300.

[0066] At point S208, the user is prompted to turn their head and position their right ear within the collection area. For example, both verbal and visual cues can be used, and the visual cues can be, for example, as... Figure 3D As shown in the image.

[0067] For example, such as Figure 3D As shown, a head icon 304 and an arrow 305 are displayed in the preset box 300 to guide the user to turn their head in the direction of the arrow 305. For example, the prompt text "Please turn your head and position your right ear in the collection area" can be displayed in the text box 301 below the preset box 300.

[0068] At S209, determine whether the right ear is located in the collection area. If yes, the method proceeds to S210; otherwise, the method returns to S208.

[0069] At S210, data from the right ear is collected, with progress indicators provided during the collection process and a completion indicator provided at the end of the collection process.

[0070] For example, vibrational cues, tone cues, and visual cues can be used, and visual cues can be, for example, such as... Figure 3E and Figure 3F As shown in the image.

[0071] For example, such as Figure 3E As shown, the visualization interface displays a circular progress bar 306 to provide collection progress indicators, and an ear icon 307 can be displayed to indicate the target body part to be collected. Figure 3F As shown, a "√" icon 308 will be displayed in the visualization interface to provide a notification that the collection is complete. For example, the text "Collecting" can be displayed in the text box 301 below the progress bar 306 to indicate that the collection is in progress, or the text "Collection Complete" can be displayed to indicate that the collection is complete.

[0072] At S211, the user is prompted to turn their head and position their left ear in the collection area.

[0073] At S212, determine whether the left ear is located in the collection area. If yes, the method proceeds to S213; otherwise, the method returns to S211.

[0074] At S213, data from the left ear is collected, with progress indicators provided during the collection process and a completion indicator provided at the end of the collection process.

[0075] At S214, at least one biometric characteristic of the user is obtained based on the collected data. For example, details of S214 may be found in the related description of S102 above, and will not be repeated here.

[0076] At point S215, data on at least one of the user's personal preferences is obtained. For example, details of S215 can be found in the relevant description above in S103, and will not be repeated here.

[0077] At S216, recommendations for suitability assessment of the wearable device are provided to the user based on at least one biometric characteristic and at least one personal preference. For example, details of S216 may refer to the relevant description above in S104, and will not be repeated here.

[0078] For example, recommended information might be like Figure 3G As shown in the figure. The rating level can be represented by the number of solid pentagrams in the rating boxes 311A, 311B, and 311C. The appearance of the recommended wearable device can be displayed in the display boxes 310A, 310B, and 310C, and other attributes of the most recommended wearable device can be displayed in the attribute box 312.

[0079] Figure 4 This is a schematic block diagram of an electronic device according to one or more embodiments of the present disclosure.

[0080] For example, such as Figure 4 As shown, the electronic device 400 includes at least one processor 401 and a memory 402, the memory including one or more computer program modules. The one or more computer program modules are stored in the memory 402 and configured to be executed by the at least one processor 401. The one or more computer program modules include instructions for implementing a method for adaptability evaluation of a wearable device, which, upon execution by the at least one processor 401, can implement one or more steps of the method for adaptability evaluation of a wearable device provided by one or more embodiments of this disclosure. The memory 402 and the processor 401 can be interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0081] For example, processor 401 may be a central processing unit (CPU), a digital signal processor (DSP), or other form of processing unit with data processing and / or program execution capabilities, such as a field-programmable gate array (FPGA). For example, the central processing unit (CPU) may be an x86 or ARM architecture. Processor 401 may be a general-purpose processor or a special-purpose processor that can control other components in electronic device 400 to perform desired functions.

[0082] For example, memory 402 may include any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable optical disc read-only memory (CD-ROM), USB memory, flash memory, etc.

[0083] Figure 5 This is a schematic block diagram of another electronic device according to one or more embodiments of the present disclosure.

[0084] Electronic devices according to one or more embodiments of this disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDAs), portable Android devices (PADs), portable media players (PMPs), in-vehicle terminals (e.g., in-vehicle navigation terminals), wearable electronic devices, and fixed terminals such as digital televisions and desktop computers. Figure 5 The electronic devices shown are merely examples and should not be construed as limiting the functionality and scope of use of one or more embodiments of this disclosure.

[0085] The electronic device includes at least one processor and a memory. The processor may be referred to herein as processing device 501 as described below, and the memory may include at least one selected from the group consisting of: read-only memory (ROM) 502, random access memory (RAM) 503, and storage device 508 as described below. The memory is configured to store a program for performing the methods described in one or more of the above method embodiments, and the processor is configured to execute the program stored in the memory. The processor may include a central processing unit (CPU) or other form of processing unit having data processing capabilities and / or instruction execution capabilities, and being able to control other components in the electronic device to perform desired functions.

[0086] like Figure 5As shown, the electronic device 500 may include a processing device 501 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various suitable actions and processes according to a program stored in a read-only memory (ROM) 502 or a program loaded from a storage device 508 into a random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device 500. The processing device 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0087] For example, input device 506, output device 507, storage device 508, and communication device 509 can be connected to I / O interface 505. Input device 506 includes, for example, a touchscreen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, infrared camera, ultrasonic sensor, lidar, time-of-flight sensor, etc. Output device 507 includes, for example, a display (e.g., liquid crystal display (LCD), organic light-emitting diode display (OLED), etc.), speaker, vibrator, etc. For example, the display can be used for the aforementioned visual signal cues, the speaker can be used for the aforementioned voice signal cues and tone signal cues, and the vibrator can be used for the aforementioned vibration signal cues. Storage device 508 includes, for example, magnetic tape, hard disk, etc. Communication device 509 allows electronic device 500 to communicate wirelessly or wiredly with other devices to exchange data. Although... Figure 5 Electronic devices 500 with various equipment are shown, but it should be understood that not all of the equipment shown will necessarily be implemented or included. More or fewer equipment may be implemented or included alternatively.

[0088] For example, according to one or more embodiments of this disclosure, the processes described above with reference to the flowchart can be implemented as a computer software program. For example, one or more embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium. The computer program includes program code for performing the methods shown in the flowchart. In one or more embodiments, the computer program can be downloaded and installed online via communication device 509, or installed from storage device 508, or installed from ROM 502. When the computer program is executed by processing device 501, the functions mentioned above defined in the methods of one or more embodiments of this disclosure will be performed.

[0089] It should be noted that the computer-readable medium mentioned above in this disclosure can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. For example, a computer-readable storage medium can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer floppy disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, optical disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In one or more embodiments of this disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device, or apparatus. In one or more embodiments of this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave and carrying computer-readable program code. The data signal propagated in this manner can take many forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any other computer-readable medium other than computer-readable storage media. Computer-readable signal media can send, propagate, or transmit programs used by or in connection with an instruction execution system, device, or apparatus. Program code contained on a computer-readable medium can be transmitted using any suitable medium, including but not limited to wires, fiber optic cables, radio frequency (RF) signals, or any suitable combination thereof.

[0090] The computer-readable medium mentioned above may be included in the electronic device 500 mentioned above, or it may exist independently without being assembled into the electronic device 500.

[0091] Figure 6 This is a schematic block diagram of a non-transient computer-readable storage medium according to one or more embodiments of the present disclosure.

[0092] like Figure 6 As shown, the non-transient computer-readable storage medium 600 includes computer instructions 601, which, when executed by at least one processor, implement one or more steps of a method for adaptability assessment of a wearable device.

[0093] For example, non-transitory computer-readable storage media may include storage components of smartphones, tablet computers, hard disks of personal computers, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), portable optical disc read-only memory (CD-ROM), flash memory, or any combination of the above storage media, and may also be other suitable storage media. For example, non-transitory computer-readable storage media may also be... Figure 4 The memory 402, and related details can be found in the previous description, which will not be repeated here.

[0094] As can be seen in the detailed description above, different features are grouped together in the examples. This manner of disclosure should not be construed as an intention to have more features than those expressly mentioned in each clause. Rather, the various aspects of this disclosure may include fewer features than all the features of the individual example clauses disclosed. Therefore, the following clauses should be regarded accordingly as incorporated into the specification, whereby each clause may serve as a separate example. Although each dependent clause may refer in the clause to a specific combination with one of the other clauses, one or more aspects of that dependent clause are not limited to that specific combination. It should be understood that other example clauses may also include combinations of one or more aspects of a dependent clause with the subject matter of any other dependent or independent clause, or any feature combined with other dependent and independent clauses. The various aspects disclosed herein expressly include these combinations unless expressly stated or readily inferred that no particular combination is intended (e.g., contradictory aspects, such as defining an element as both an electrical insulator and an electrical conductor). Furthermore, it is intended that aspects of a clause may be included in any other independent clause, even if that clause does not directly depend on that independent clause.

[0095] Examples of implementation methods are described in the following numbered clauses:

[0096] Clause 1. A method for suitability assessment of a wearable device, the method comprising: acquiring data of a target body part of a user; obtaining at least one bio-appearance characteristic of the user based on the data of the target body part; acquiring data of at least one personal preference of the user; and providing the user with recommendation information for suitability assessment of the wearable device based on the data of the at least one bio-appearance characteristic and the at least one personal preference of the user.

[0097] Clause 2. The method according to Clause 1, wherein the data of the target body part includes at least one of eye data, ear data, head data, hand data, foot data, and body shape data.

[0098] Clause 3. The method according to Clause 1 or 2, wherein obtaining data on the target body part of the user includes collecting data on the target body part of the user through a collection device.

[0099] Clause 4. The method according to Clause 3, wherein collecting data on a target body part of a user by means of a collecting device comprises: collecting data on the target body part in response to the target body part being located within the collecting area of ​​the collecting device.

[0100] Clause 5. The method according to Clause 4, wherein collecting data on a user’s target body part via the collection device further includes: determining the distance between the user and the collection device; and, in response to the distance meeting a preset threshold, providing a prompt to guide the user to position the target body part within the collection area of ​​the collection device.

[0101] Clause 6. The method according to Clause 5, wherein after determining the distance between the user and the collection device, the method further comprises: providing a prompt to guide the user to move closer to the collection device in response to the distance being greater than a first distance threshold; or providing a prompt to guide the user to move away from the collection device in response to the distance being less than a second distance threshold.

[0102] Clause 7. The method described in Clause 3 further includes: providing a progress indicator during the process of collecting data on the target body part; and providing a completion indicator at the end of the process of collecting data on the target body part.

[0103] Clause 8. The method according to any one of Clauses 4 to 7, wherein the cues include at least one of voice cues, tone cues, vibration cues, and visual cues.

[0104] Clause 9. The method according to any one of Clauses 1 to 8, wherein obtaining at least one biometric appearance characteristic of the user based on data from the target body part comprises: obtaining at least one biometric appearance characteristic of the user through a deep learning model or machine learning model based on data from the target body part.

[0105] Clause 10. The method according to any one of Clauses 1 to 9, wherein the at least one personal preference includes at least one of the daily use of the wearable device, price range, and functional requirements.

[0106] Clause 11. The method according to any one of Clauses 1 to 10, wherein the recommendation information includes at least one of the attributes and rating levels of the recommended wearable device.

[0107] Clause 12. An electronic device comprising: at least one processor; and a memory including one or more computer program modules, wherein the one or more computer program modules are stored in the memory and include instructions for implementing the method according to any one of Clauses 1 to 11 upon execution by the at least one processor.

[0108] Clause 13. A non-transient computer-readable storage medium comprising computer instructions, wherein the computer instructions, when executed by at least one processor, implement the method according to any one of Clauses 1 to 11.

[0109] Although this disclosure has been described in detail above with reference to the general description and specific embodiments, it will be apparent to those skilled in the art that modifications or improvements can be made based on the embodiments of this disclosure. Therefore, such modifications or improvements made without departing from the spirit of this disclosure fall within the scope of protection claimed herein.

[0110] Regarding the content of this disclosure, the following points should be noted. The accompanying drawings of the embodiments of this disclosure only relate to the structures involved in the embodiments of this disclosure, and other structures can be referenced to conventional designs. For clarity, the thickness of each layer or region in the drawings used to describe the embodiments of this disclosure is enlarged or reduced; that is, these drawings are not drawn to scale. Without conflict, the embodiments and features in the embodiments of this disclosure can be combined with each other to obtain new embodiments.

[0111] The above are merely specific embodiments of this disclosure, but the scope of protection of this disclosure is not limited thereto. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A method for adaptability assessment of wearable devices, comprising: Acquire data about the user's target body parts; Based on the data from the target body part, at least one biological appearance characteristic of the user is obtained; Obtain data on at least one of the user's personal preferences; as well as Based on the user's data on at least one biometric appearance characteristic and at least one personal preference, the user is provided with recommended information for the fit assessment of the wearable device.

2. The method according to claim 1, wherein the data of the target body part includes at least one of eye data, ear data, head data, hand data, foot data, and body shape data.

3. The method according to claim 1 or 2, wherein obtaining the data of the target body part of the user comprises: The data of the user's target body part is collected by the collecting device.

4. The method of claim 3, wherein collecting the data of the user's target body part via the collecting device comprises: In response to the target body part being positioned within the collection area of ​​the collection device, the data of the target body part is collected.

5. The method of claim 4, wherein collecting the data of the user's target body part via the collecting device further comprises: Determine the distance between the user and the collection device; as well as In response to the distance meeting a preset threshold, a prompt is provided to guide the user to locate the target body part within the collection area of ​​the collection device.

6. The method of claim 5, wherein after determining the distance between the user and the collection device, the method further comprises: In response to the distance being greater than a first distance threshold, a prompt is provided to guide the user to move closer to the collection device; or In response to the distance being less than a second distance threshold, a prompt is provided to guide the user to move away from the collection device.

7. The method according to claim 3, further comprising: Provide progress indicators during the data collection process for the target body part; as well as A notification indicating completion of the data collection process is provided at the end of the process.

8. The method according to any one of claims 4 to 7, wherein the prompt includes at least one of voice signal prompts, tone signal prompts, vibration signal prompts, and visual signal prompts.

9. The method according to any one of claims 1 to 8, wherein obtaining the at least one bio-appearance characteristic of the user based on the data from the target body part comprises: The user's at least one biological appearance characteristic is obtained through a deep learning model or machine learning model based on the data from the target body part.

10. The method according to any one of claims 1 to 9, wherein the at least one personal preference includes at least one of the daily use of the wearable device, price range, and functional requirements.

11. The method according to any one of claims 1 to 10, wherein the recommendation information includes at least one of the attributes of the recommended wearable device and the rating level.

12. An electronic device comprising: At least one processor; as well as The memory includes one or more computer program modules. The one or more computer program modules are stored in the memory and include instructions for implementing the method according to any one of claims 1 to 11 after being executed by the at least one processor.

13. A non-transient computer-readable storage medium comprising computer instructions, wherein the computer instructions, when executed by at least one processor, perform the method according to any one of claims 1 to 11.

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