Control method of first equipment and first equipment

Abstract

The invention provides a control method of first equipment and the first equipment. The method comprises the steps that in response to the situation that a user wears first equipment, the target area of a target area where the user makes contact with the first equipment and first pressure information corresponding to the target area are obtained, and the first equipment is used for obtaining biological characteristic information; determining first pressure intensity information corresponding to the target area based on the target area and the first pressure information; under the condition that the first pressure intensity information is within the preset pressure intensity range, the first equipment is controlled to obtain first biological characteristic information of the user.

Description

Technical Field

[0001] This application relates to, but is not limited to, the field of information technology, and in particular to a control method for a first device and the first device itself. Background Technology

[0002] In related technologies, when collecting users' biometric information through sensing devices, it is difficult to balance the user's comfort in wearing the sensing devices with the accuracy of the collected data. This may result in large errors in the collected data, low reliability, or a poor user experience. Summary of the Invention

[0003] In view of this, embodiments of this application provide at least one control method for a first device and a first device.

[0004] The technical solution of this application embodiment is implemented as follows: This application provides a method for controlling a first device, including: In response to a user wearing the first device, the first device acquires the target area of ​​the target region in contact between the user and the first device and the first pressure information corresponding to the target region, and the first device is used to acquire biometric information. Based on the target area and the first pressure information, determine the first pressure information corresponding to the target area; When the first pressure information is within a preset pressure range, the first device is controlled to acquire the user's first biometric information.

[0005] This application provides a first device, including: a pressure sensing component, a data acquisition component, and a control component; Pressure sensing components are used to collect pressure information; A control component is configured to respond to a user wearing a first device by acquiring the target area of ​​the target region in contact between the user and the first device and the first pressure information corresponding to the target region; based on the target area and the first pressure information, determining the first pressure information corresponding to the target region; and, when the first pressure information is within a preset pressure range, controlling the first device to acquire the user's first biometric information through a data acquisition component.

[0006] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this application. Attached Figure Description

[0007] Figure 1 This is a schematic diagram illustrating the implementation flow of a control method for a first device provided in an embodiment of this application; Figure 2 This is a schematic diagram illustrating the weighted combination of different types of EEG information provided in an embodiment of this application; Figure 3 This is a schematic diagram of the composition structure of a first device provided in an embodiment of this application; Figure 4 This is a schematic diagram illustrating the implementation process of a method for improving the wearing comfort and detection accuracy of brainwave devices according to an embodiment of this application; Figure 5 This is a schematic diagram of a preset pressure range provided in an embodiment of this application; Figure 6 This is a schematic diagram illustrating the implementation process of a brainprint recognition login and brainprint storage model self-updating method provided in an embodiment of this application; Figure 7 This is a schematic diagram illustrating the implementation process of user identity recognition based on electroencephalogram (EEG) information, as provided in an embodiment of this application.

[0008] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0009] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] In the following description, the terms "first / second / third" are used merely to distinguish similar objects and do not represent a specific ordering of the objects. It is understood that "first / second / third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein. 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 application belongs. The terminology used herein is for descriptive purposes only and is not intended to limit the scope of this application. It should also be noted that, for ease of description, only the parts relevant to the application are shown in the accompanying drawings.

[0011] This application provides a control method for a first device. In implementation, this method can be executed by the first device or other electronic devices communicatively connected to the first device. The first device may include electronic devices, which can be servers, laptops, tablets, desktop computers, smart TVs, set-top boxes, mobile devices (such as mobile phones, portable video players, personal digital assistants, dedicated messaging devices, portable gaming devices), or other devices with data processing capabilities. Figure 1As shown, the method includes the following steps S101 to S103: Step S101: In response to the user wearing the first device, the target area of ​​the target region in contact between the user and the first device and the first pressure information corresponding to the target region are obtained. The first device is used to obtain biometric information.

[0012] Here, the first device is an electronic device with data acquisition capabilities. In implementation, the first device may be equipped with acquisition components for collecting biometric information. It is understood that different types of acquisition components configured in the first device will result in different types of biometric information being collected.

[0013] Biometric information may include electroencephalogram (EEG) information (such as EEG signals, EEG, etc.), electromyogram (EMG) information (such as EMG signals, EMG, etc.), physical features (such as facial features, body features), voice information (such as voiceprints, timbre, etc.), or collected information that can characterize other biometric features.

[0014] In some implementations, the type of biometric information collected may differ, and the way the first device is worn may also differ.

[0015] For example, brain-computer interface devices, such as EEG instruments, EEG headsets, or EEG headsets, can be used as the primary device to collect the wearer's brainwave information through EEG acquisition components.

[0016] For example, a sound acquisition device with an audio acquisition and processing interface, such as a microphone or sound card, can be used as the first device to collect the wearer's voice information through the sound acquisition component.

[0017] For example, electromyographic information of the wearer can be collected by detection electrodes attached to the neck or arm.

[0018] For example, user image data can be collected through the front-facing camera of a head-mounted or wristband-style action camera.

[0019] Understandably, by collecting the wearer's biometric information, it is possible to obtain the wearer's current physiological and / or psychological characteristics. For example, based on electroencephalography (EEG) information, the wearer's current level of focus and / or emotion can be detected. Similarly, based on electromyography (EMG) information, the wearer's current muscle activity and / or fatigue level can be detected. Furthermore, based on the user's facial features, such as expression recognition and eye tracking, visual signals can be used to detect the wearer's current focus and / or psychological state.

[0020] It is understandable that, due to differences in age, body shape, and other biometric characteristics among users, the contact area (the surface area of ​​actual contact within the target region) when wearing the same EEG helmet may differ between users. For example, a helmet that fits comfortably for an adult may be too loose for a minor or young child, resulting in a smaller target area for the child compared to an adult.

[0021] In this embodiment, the comfort (tightness) of the user wearing the first device affects the accuracy of the biometric information acquired by the first device. For example, if the EEG helmet used to collect EEG information is worn too tightly, the excessive pressure on the user's head may cause interference with the collected EEG information, resulting in fluctuations in the EEG signal and affecting the final EEG analysis results. Similarly, if the headphones used to collect sound information are worn too tightly, the pressure may interfere with the user's speech, making the collected sound signal inaccurate.

[0022] In practice, pressure information can be obtained through a pressure sensing component. For example, the pressure sensing component may include, but is not limited to, at least one of a capacitive pressure sensor, a strain gauge pressure sensor, a piezoresistive pressure sensor, etc.

[0023] During implementation, the target area can be calculated by the first device according to a preset model, or it can be dynamically estimated by pressure distribution.

[0024] In some implementations, conductive silicone can be used as the detection electrode in the first device to contact the user and collect electrical-related biometric signals (such as electroencephalogram, electromyography, etc.). This utilizes the skin-friendly nature of silicone to improve the user's comfort when wearing the first device.

[0025] In some implementations, a pressure sensor diaphragm can be added to the side of the first device closest to the user at the point of contact between the first device and the user to detect pressure information. This reduces the hardware space occupied by the pressure sensing components, thereby reducing the size of the device and further improving user comfort.

[0026] Step S102: Based on the target area and the first pressure information, determine the first pressure information corresponding to the target area.

[0027] Here, pressure information is used to characterize the pressure distribution between the target area where the first device and the user are in contact when the user wears the first device. It can be understood that the user's perception of pressure is essentially their perception of pressure intensity. Therefore, by measuring the contact area A between the first devices and the pressure F applied when wearing the first device, real-time pressure detection can be achieved using the formula P=F / A.

[0028] In some implementations, the area with the maximum pressure and / or the pressure exceeding a preset threshold can be regarded as the main area of ​​contact between the user and the first device, i.e., the effective area; further, this area is determined as the target area, and the pressure information corresponding to the target area is obtained as the first pressure information.

[0029] This value is derived by dividing the pressure data measured by the pressure sensor by the actual contact area, and is used to determine whether the wearing is within a comfortable range suitable for EEG signal acquisition.

[0030] Step S103: When the first pressure information is within the preset pressure range, control the first device to acquire the user's first biometric information.

[0031] Here, the preset pressure range is a predetermined pressure interval. Within this interval, the pressure information ensures user comfort while preventing the quality of the first biometric information from being affected by the device being worn too loosely or too tightly. Thus, within the preset pressure range, the accuracy of the biometric information acquired by the first device will be higher than a preset threshold, indicating a high level of reliability. This preset threshold can be the minimum threshold representing the reliability or validity of the biometric information, such as an accuracy value or accuracy score ranging from 0 to 1, with corresponding preset thresholds of 0.8, 0.9, or 0.95, etc.

[0032] For example, based on empirical values, the pressure range for user comfort when wearing a head-mounted device can be estimated to be below 2.66 kPa. If the pressure exceeds this range, the user may experience discomfort. In practice, pre-experimentation can determine that the normal operating pressure range for the EEG device is approximately between 0.8 kPa and 3 kPa. If the pressure is below 0.8 kPa, the device may slip, accompanied by increased impedance, introducing signal noise and affecting detection accuracy. If the pressure is above 3 kPa, it may cause poor local blood circulation in the user's head, resulting in excessive electromyographic circulation and affecting the triggering of native EEG signals. Therefore, for example, the preset pressure range for the EEG device can be determined to be between 1 kPa and 2 kPa.

[0033] In this embodiment, pressure information is calculated by measuring the contact area and pressure information between the user and the first device. It is then determined whether the pressure information is within a range suitable for user comfort and biometric data collection, and biometric data collection is initiated if the pressure is within a suitable range. This method, determining whether to initiate biometric data collection based on the pressure at the contact surface between the device and the user, reduces noise interference and detection anomalies caused by the device being worn too loosely or too tightly, improving the accuracy of the first biometric data. Furthermore, by adjusting the pressure between the device and the user, the device can be adapted to users with different head shapes during biometric data collection, improving user comfort and enhancing the user experience.

[0034] In some embodiments, the preset pressure range includes a pressure range that is not higher than a first pressure threshold and not lower than a second pressure threshold, wherein the first pressure threshold is higher than the second pressure threshold; the control method of the first device may further include the following step S111: Step S111: When the first pressure information is within the preset pressure range, output the first prompt information; the first prompt information indicates that the first pressure information is not higher than the first pressure threshold and not lower than the second pressure threshold.

[0035] Here, the first pressure threshold is the upper limit of the preset pressure range. If the pressure information is higher than the first pressure threshold, it means that the first device is worn too tightly, which may cause discomfort to the user or interfere with the collection of biometric information.

[0036] The second pressure threshold is the lower limit of the preset pressure range. If the pressure information is lower than the second pressure threshold, it means that the first device is worn too loosely, which may result in excessively high impedance and thus affect the quality of the collected biometric information.

[0037] In implementation, the first and second pressure thresholds can be determined empirically and / or through pre-experiments. For example, the 1 kPa to 2 kPa in step S103 above can correspond to the second and first pressure thresholds, respectively. It is understood that by setting upper and lower pressure thresholds, the user can wear the first device without it being too tight or too loose, thereby improving wearing comfort while further enhancing the accuracy of biometric information collection.

[0038] The first prompt message is a feedback signal output when the current wearing pressure of the first device is detected to be within a preset pressure range, indicating that the collection of the first biometric information can proceed. In implementation, the first prompt message may include, but is not limited to, at least one of the following: sound signals, visual signals, vibration feedback, etc.

[0039] For example, the first prompt message can be presented by turning on a light, a voice prompt, or displaying a notification in the application interface to indicate to the user that the current wearing status is good and the biometric signal collection function can be enabled.

[0040] The prompting mechanism corresponding to the first prompt message can help the wearer adjust the wearing position and / or tightness in a timely manner, reducing problems such as increased signal noise or slippage of the first device caused by improper wearing, thereby improving the user experience and the reliability of the collected data.

[0041] In this embodiment, when the first pressure information is within a preset pressure range, a first prompt message is output indicating that the first pressure information is not higher than a first pressure threshold and not lower than a second pressure threshold. This allows the wearer to adjust the wearing status of the first device based on the presence or absence of the first prompt message, thereby improving user comfort and the effectiveness of collecting biometric information.

[0042] In implementation, the first biometric information can be collected manually or automatically in response to receiving the first prompt information. For example, after the user or detector receives the first prompt information, the first device can be manually triggered to collect the first biometric information. Alternatively, after the first prompt information is output or received, the first device can control the collection component to automatically complete the collection of the first biometric information.

[0043] In some embodiments, the control method for the first device may further include the following step S121: Step S121: If the first pressure information is higher than the first pressure threshold or lower than the second pressure threshold, output the second prompt information; the second prompt information indicates that the acquisition of the first biometric information will not be initiated.

[0044] Here, if the first pressure information is higher than the first pressure threshold, it indicates that the first device is currently worn too tightly, and it is not recommended to initiate the acquisition of the first biometric information in this case. Similarly, if the first pressure information is lower than the second pressure threshold, it indicates that the first device is currently worn too loosely, and it is not recommended to initiate the acquisition of the first biometric information in this case.

[0045] The second prompt message is a feedback signal output when the current wearing pressure of the first device is not within a preset pressure range, used to indicate that the collection of the first biometric information should not be initiated. In implementation, the second prompt message may include, but is not limited to, at least one of the following: sound signals, visual signals, vibration feedback, etc.

[0046] For example, the second prompt message can be presented through a light, voice prompt, or notification displayed within the application interface to inform the user that the current wearing status is not good and that it is not recommended to enable the biometric signal collection function. It is understood that the second prompt message can not only improve the user's awareness of the current wearing status but also reduce misjudgments caused by improper wearing of the first device.

[0047] In some implementations, when the prompt information corresponding to the first device includes only the first or second prompt information, the presentation of the first and second prompt information can be the same; however, when the prompt information corresponding to the first device includes both the first and second prompt information, their presentation methods must be different. For example, the indicator light can be controlled to flash only when the user is wearing the device properly or only when the user is not wearing it properly. Alternatively, a voice prompt can be given saying "Wearing complete" when the user is wearing the device properly, and a voice prompt saying "Not wearing complete" when the user is not wearing it properly. This way, a single prompt can reduce setup costs and the complexity of the data collection process; multiple prompts can also provide wearing suggestions for various scenarios, improving the efficiency of information collection in each scenario.

[0048] In this embodiment, when the first pressure information is higher than a first pressure threshold or lower than a second pressure threshold, a second prompt message indicating that the acquisition of the first biometric information will not be initiated is output. This allows for timely cessation of biometric information acquisition when the device is worn too loosely or too tightly, reducing signal interference and misjudgments caused by improper device wearing, thereby improving the stability of the acquisition process and the user experience.

[0049] In some embodiments, the first device and the second device are communicatively connected, and the control method of the first device may further include the following steps S131 to S132: Step S131: In response to obtaining the first biometric information, determine the target matching degree between the first biometric information and the second biometric information of the user in the target database; the second biometric information is used to trigger the target program in the second device.

[0050] Here, the second device can be an electronic device with data processing capabilities, and the second device runs or is capable of running the target program. The target program is a preset application program or functional module, such as a system program, a login program, or a functional program (payment function, verification function, audio and video playback function, etc.).

[0051] Here, the first biometric information is used to activate the target program in the second device, thereby starting the target program or calling the functional services that the target program can provide.

[0052] In some implementations, the first device and the second device can be connected via wired or wireless communication methods such as Bluetooth, wireless LAN, or signal transmission cables.

[0053] The target database is used to store biometric information corresponding to the target object. The target object may include the user corresponding to the first biometric information, or any user or organism wearing the first device.

[0054] In this step, the second biometric information is the biometric information of the user currently wearing the first device, pre-stored in the target database, used to establish the user's identity characteristics. It is understood that the first and second biometric information are of the same type. By storing the second biometric information as the user's biometric identifier, this information can be used as reference information for authenticating the user, thereby protecting user privacy and data security.

[0055] Target matching degree is used to characterize the similarity between first biometric information and second biometric information. For example, target matching degree can be obtained through Euclidean distance calculation, cosine similarity calculation, etc., or it can be inferred from the target matching degree using computational models such as deep learning models. The higher the target matching degree between the first biometric information and the second biometric information, the greater the probability that the first biometric information belongs to the user corresponding to the second biometric information, and the higher the credibility of the first biometric information.

[0056] In some implementations, determining the target matching degree between the first biometric information and the second biometric information of a user in the target database may include: extracting features from the first biometric information to obtain a first feature; determining the target similarity between the first biometric information and the second feature corresponding to the second biometric information; and determining the target matching degree based on the target similarity.

[0057] Here, the first feature and the second feature can be data features of the same type extracted from the first biometric information and the second biometric information, respectively. For example, the first feature and the second feature can be at least one of the following: the frequency of brain waves, voiceprints, electromyography frequency, facial contours, etc.

[0058] Step S132: If the target matching degree is higher than the matching degree threshold, trigger the target program.

[0059] Here, the matching threshold is a preset minimum threshold representing the similarity between the first biometric information and the second biometric information.

[0060] During implementation, if the target matching degree is not higher than the matching degree threshold, the target program can be left untriggered, and a warning message can be output. The warning message can be used to indicate potential risks to the device or data, whether biometric information needs to be updated, or whether deeper data protection measures (such as data migration or data deletion) are needed.

[0061] In this embodiment, by comparing the collected first biometric information with pre-stored reference data (second biometric information), a target program in the second device is triggered when the target matching degree between the first and second biometric information is higher than a matching degree threshold. This allows for the triggering of runnable programs in the second device based on the user's biometric information, improving the device's security and intelligence level.

[0062] In some embodiments, the control method for the first device may further include the following step S141: Step S141: In response to the target matching degree being higher than the matching degree threshold within a consecutive number of target attempts, but continuously decreasing, the second biometric information in the target database is updated.

[0063] Here, the target number is a preset maximum number of times used to determine the validity of the second biometric information. It is understood that if the target matching degree between the first and second biometric information collected by the user multiple times is higher than the matching degree threshold but continuously decreases, the user's biometric information can be considered valid, but has changed compared to historical times and fluctuates. Therefore, the second biometric information needs to be updated to improve the validity and reliability of the user's reference data in the target database, i.e., the second biometric information. For example, the target number may include, but is not limited to, 3 times, 5 times, 10 times, etc., and this application embodiment does not limit this.

[0064] In some implementations, the target number of occurrences can be the maximum number of occurrences within a preset time period. For example, if the target matching degree of the first biometric information reaches a matching degree threshold but continues to decrease within three days (or a week or a month, which is not limited in this embodiment), an update of the second biometric information in the target database can be triggered. This can improve the real-time performance of the updated second biometric information.

[0065] In this embodiment, by monitoring the changing trend of the target matching degree, and when the target matching degree is higher than the matching degree threshold but continues to decline within a consecutive number of target attempts, an update of the second biometric information in the target database is triggered. In this way, by sensing changes in the user's biometric characteristics, the reference biometric information used by the user to trigger the target program in the target database can be adjusted in a timely manner, thereby improving the stability and accuracy of the user triggering the target program using the first biometric information.

[0066] In some embodiments, the target program includes an authentication program for a second device used to authenticate the user.

[0067] Here, the authentication process is used to verify the user's identity, not to limit the functional services that the user can use after being authenticated by the authentication process; that is, the authentication process is used to implement the pre-authentication process of the functional services called by the user, so as to determine whether the user is allowed to perform subsequent operations related to the corresponding functional services.

[0068] For example, the authentication process can be used for at least one of the following scenarios: unlocking a second device, logging into other applications (such as system applications, social applications, game applications, etc.), making payments, and viewing files.

[0069] Understandably, this authentication process can be applied to any scenario requiring user authentication information. This user authentication information can include, but is not limited to, data used for identity verification such as passwords, fingerprints, voiceprints, EEG data, and facial recognition.

[0070] In this embodiment, user authentication is performed using biometric information. On the one hand, compared to easily obtainable static identity information such as passwords, biometric authentication enhances the accuracy and security of user identification, making it particularly suitable for applications requiring high security levels. On the other hand, by determining whether the pressure information is within a preset range, the accuracy of the first biometric information obtained by the user for authentication can be improved, thereby enhancing the accuracy, fluency, and efficiency of biometric authentication.

[0071] In some embodiments, the biometric information includes electroencephalogram (EEG) information, the preset pressure range includes multiple pressure ranges, and the control method of the first device may further include the following steps S151 to S153: Step S151: Determine the target pressure range where the first pressure information is located from multiple pressure ranges.

[0072] Here, EEG information refers to the electrical signals generated by brain neural activity collected by EEG acquisition components. EEG information may differ between different users, and may also differ for the same user at different times or in different environments. However, EEG information collected from the same user in similar environments is usually the same or nearly the same.

[0073] Electroencephalogram (EEG) data can be categorized into several types based on waveform and / or frequency. For example, common EEG data may include, but is not limited to, alpha waves with a frequency range of approximately 8 Hz to 13 Hz, beta waves with a frequency range of approximately 13 Hz to 30 Hz, gamma waves with a frequency range of approximately 30 Hz to 45 Hz, and theta waves with a frequency range of approximately 4 Hz to 8 Hz. Among these, lower-frequency theta and alpha waves are more susceptible to motion artifacts. For instance, if the EEG device is worn too loosely, friction between the device and the wearer's head may produce motion artifacts, thus resulting in a higher pressure range that is more suitable for theta and alpha waves. Conversely, mid-to-high-frequency beta and gamma waves are more susceptible to electromyographic noise generated by pressure, affecting the collected beta and gamma waves, thus resulting in a lower pressure range that is more suitable for beta and gamma waves.

[0074] In this case, the preset pressure range can be divided into multiple pressure ranges according to the different types of EEG information; further, the pressure range in which the first pressure information is located in the multiple pressure ranges can be determined as the target pressure range.

[0075] Step S152: Based on the correspondence between different pressure ranges and weight combinations of different types of EEG information, determine the target weight combination corresponding to different types of EEG information under the target pressure range.

[0076] It is understandable that the reliability of various EEG information will vary within different pressure ranges due to differences in factors such as the stability of the contact between the EEG device and the user's head, impedance, and noise levels. Therefore, during implementation, a mapping relationship between different pressure ranges and the weight allocation of EEG information can be established in advance, that is, the correspondence between different pressure ranges and the weight combinations of different types of EEG information; wherein the weight combination includes the weight corresponding to at least one type of EEG information.

[0077] For example, in the pressure range of approximately 1 kPa to 1.3 kPa, β waves and γ waves may have a higher signal-to-noise ratio, so the weights of β waves and γ waves can be set higher, while in other ranges of approximately 1.3 kPa to 2 kPa, the weights of β waves and γ waves can be reduced.

[0078] In practice, the determination of the target weight combination can be achieved by searching a preset table, using an interpolation algorithm, or employing a machine learning model, etc., and this application embodiment does not limit this.

[0079] Step S153: Determine the user's brainwave detection results based on the target weight combination and the type of the first brainwave information.

[0080] Here, the first EEG information is the aforementioned first biometric information, and the EEG detection results corresponding to the first EEG information can characterize the physiological or psychological state of the user corresponding to the first EEG information.

[0081] Based on the user's EEG information, parameters such as the user's emotions and concentration can be analyzed and identified. By combining target weight combinations to comprehensively analyze the user's EEG information, and taking into account the pressure state of the device worn by the user, the detection interference introduced by the wearing device can be reduced, and the user's current emotions or concentration can be more accurately identified.

[0082] In this embodiment, the weight allocation of different types of EEG information is dynamically adjusted according to different pressure ranges. Thus, considering the interference of different pressure ranges on the detection of different types of EEG signals, combining the pressure range and the type of EEG information can yield more accurate EEG detection results.

[0083] In some embodiments, step S152 may include steps S161 to S162: Step S161: Based on the correspondence between the weight combination of different types of EEG information and different pressure ranges, when the target pressure range is within the first pressure range, determine that the target weight combination includes the first weight corresponding to the first type of EEG information and the second weight corresponding to the second type of EEG information.

[0084] Step S162: Based on the correspondence between the weight combination of different types of EEG information and different pressure ranges, when the target pressure range is within the second pressure range, determine that the target weight combination includes the third weight corresponding to the first type of EEG information and the fourth weight corresponding to the second type of EEG information; the first pressure corresponding to the first pressure range is less than the second pressure corresponding to the second pressure range, the first weight is lower than the third weight, and the second weight is higher than the fourth weight.

[0085] Here, the first type of EEG information may include low-frequency or low-to-medium-frequency EEG information, and the second type of EEG information may include high-frequency or high-to-medium-frequency EEG information.

[0086] The first pressure range can be the pressure range corresponding to low pressure or medium-low pressure, and the second pressure range can be the pressure range corresponding to high pressure or medium-high pressure.

[0087] The first weight is lower than the third weight. That is, the weight of low-frequency or mid-low-frequency EEG information within the pressure range corresponding to low or medium-low pressure is lower than the weight of low-frequency or mid-low-frequency EEG information within the pressure range corresponding to high or medium-high pressure. This is understandable because, under low pressure conditions, low-frequency or mid-low-frequency EEG information is easily affected by the relative movement between the device and the user's head. Therefore, reducing the weight of low-frequency or mid-low-frequency EEG information within the pressure range corresponding to low or medium-low pressure to the first weight can improve the overall reliability of the EEG information.

[0088] The second weight is higher than the third weight. That is, the weight of high-frequency or mid-frequency EEG information within the pressure range corresponding to low or medium-low pressure is higher than the weight of high-frequency or mid-frequency EEG information within the pressure range corresponding to high or medium-high pressure. This is understandable because, under high pressure, high-frequency or mid-frequency EEG information is easily affected by the pressure between the device and the user's head. Therefore, reducing the weight of high-frequency or mid-frequency EEG information within the pressure range corresponding to high or medium-high pressure to the fourth weight can improve the overall reliability of the EEG information.

[0089] For example, such as Figure 2As shown, different processing models (EEG detection models or EEG analysis models) are selected based on different pressures. Different models use different weights for each type of EEG information for analysis, comprehensively evaluating the EEG detection results. For example, when the pressure range is 1 kPa to 1.3 kPa, the low-pressure adaptation model M1 can be selected for analyzing and evaluating the EEG detection results. Processing with the low-pressure adaptation model M1 can reduce the weight of low-frequency theta and alpha waves, which are susceptible to motion artifacts, and increase the weight of relatively stable mid-to-high-frequency beta and gamma waves, which are sensitive to cognitive activity, thereby improving the accuracy of EEG detection results in low-pressure (e.g., 1 kPa to 1.3 kPa) scenarios. For example, when the pressure range is 1.3 kPa to 1.7 kPa, the standard pressure adaptation model M2 can be selected for the analysis and evaluation of EEG detection results. Processing with the standard pressure adaptation model M2 allows for a more balanced distribution of weights across various EEG information types, with a slight emphasis on alpha waves, which correspond to relaxation, and beta waves, which are more relevant to active cognition. This improves the accuracy of EEG detection results under standard pressure (e.g., 1.3 kPa to 1.7 kPa). Similarly, when the pressure range is 1.7 kPa to 2 kPa, the high pressure adaptation model M3 can be selected for the analysis and evaluation of EEG detection results. Since electromyographic noise mainly affects the high-frequency bands corresponding to beta and gamma waves, while theta and alpha waves are less affected and can serve as core reliable signal sources, the high pressure adaptation model M3 can reduce the weights of beta and gamma waves and increase the weights of theta and alpha waves, thereby improving the accuracy of EEG detection results under high pressure (e.g., 1.7 kPa to 2 kPa). In this way, by using multiple processing models adapted to different pressure ranges, the compatibility between EEG detection results and the target pressure range corresponding to the user's current wearing of the first device can be improved.

[0090] Understandably, during implementation, multiple processing models with different weight combinations can be preset so that when EEG information is received, the appropriate model can be directly selected according to different pressure ranges; alternatively, when EEG information is received, a single processing model can be used to select the corresponding weight combination for analysis in real time according to different pressure ranges.

[0091] In this embodiment, by configuring weight combinations of different types of EEG information according to different pressure ranges, noise interference caused by different device wearing pressures can be reduced during the process of determining EEG detection results, thereby improving the processing accuracy of EEG information and the accuracy of EEG detection results.

[0092] This application provides a first device, such as... Figure 3 As shown, the first device 300 includes: a pressure sensing component 310, a data acquisition component 320, and a control component 330; Pressure sensing component 310 is used to collect pressure information; The control component 330 is used to respond to the user wearing the first device 300, to acquire the target area of ​​the target area in contact between the user and the first device 300 and the first pressure information corresponding to the target area; to determine the first pressure information corresponding to the target area based on the target area and the first pressure information; and to control the first device 300 to acquire the user's first biometric information through the acquisition component 320 when the first pressure information is within a preset pressure range.

[0093] In some embodiments, the preset pressure range includes a pressure not higher than a first pressure threshold and not lower than a second pressure threshold, wherein the first pressure threshold is higher than the second pressure threshold; the control component can also be used to: output a first prompt message when the first pressure information is within the preset pressure range; the first prompt message indicates that the first pressure information is not higher than the first pressure threshold and not lower than the second pressure threshold.

[0094] In some embodiments, the control component described above can also be used to: output a second prompt message when the first pressure information is higher than a first pressure threshold or lower than a second pressure threshold; the second prompt message indicates that the acquisition of the first biometric information is not initiated.

[0095] In some embodiments, the first device and the second device are connected in communication, and the control component can also be used to: in response to obtaining the first biometric information, determine the target matching degree between the first biometric information and the second biometric information of the user in the target database; use the second biometric information to trigger the target program in the second device; and trigger the target program if the target matching degree is higher than the matching degree threshold.

[0096] In some embodiments, the control component described above can also be used to: trigger an update of the second biometric information in the target database in response to a target matching degree being higher than the matching degree threshold within a consecutive number of target attempts, but continuously decreasing.

[0097] In some embodiments, the target program includes an authentication program for a second device used to authenticate the user.

[0098] In some embodiments, the biometric information includes electroencephalogram (EEG) information, and the preset pressure range includes multiple pressure ranges. The control component can also be used to: determine the target pressure range where the first pressure information is located from the multiple pressure ranges; determine the target weight combination corresponding to different types of EEG information under the target pressure range based on the correspondence between different pressure ranges and weight combinations of different types of EEG information; and determine the user's EEG detection result based on the target weight combination and the type of the first EEG information.

[0099] In some embodiments, the control component described above can also be used to: based on the correspondence between weight combinations of different types of EEG information and different pressure ranges, when the target pressure range is within a first pressure range, determine that the target weight combination includes a first weight corresponding to a first type of EEG information and a second weight corresponding to a second type of EEG information; based on the correspondence between weight combinations of different types of EEG information and different pressure ranges, when the target pressure range is within a second pressure range, determine that the target weight combination includes a third weight corresponding to a first type of EEG information and a fourth weight corresponding to a second type of EEG information; the first pressure corresponding to the first pressure range is less than the second pressure corresponding to the second pressure range, the first weight is lower than the third weight, and the second weight is higher than the third weight.

[0100] In related technologies, EEG devices can be used to detect users' brainwaves, thereby detecting information such as attention span, fatigue, and / or emotional state. Furthermore, attention training can be provided to users with attention deficit issues. However, different users may have different head shapes and sizes, which may lead to conflicts and detection abnormalities when adjusting the tightness of the EEG device. These problems mainly include the following: For users, a looser fit on an EEG device reduces pressure; however, from the device's perspective, an overly loose fit may cause it to slip off and generate significant impedance (e.g., a resistance greater than 100 kilohms), introducing signal noise that affects detection accuracy, resulting in lower detected EEG amplitude fluctuations, or even failing to detect EEG signals. Conversely, an overly tight fit not only affects user comfort but may also compress blood vessels, affecting the native triggering of EEG signals and leading to the detection of noise caused by neuromuscular resistance. Furthermore, even when the user's comfort level is within a comfortable range, their EEG signals may still be affected by various noises, interfering with the final detection results.

[0101] To address the aforementioned issues, this application proposes a method for improving the wearing comfort and detection accuracy of brain electrical devices, which can be applied to brain electrical devices.

[0102] For example, such as Figure 4 As shown, the method for improving the wearing comfort and detection accuracy of EEG devices may include the following steps S401 to S414: Step S401: The pressure sensor detects pressure data.

[0103] During implementation, the pressure can be detected in real time through a pressure-sensitive diaphragm.

[0104] Step S402: Determine the pressure based on the pressure data.

[0105] During implementation, the pressure of the wearable device can be determined based on the pressure-bearing area (the target area corresponding to the target region) in contact with the EEG device and the detected pressure, i.e., the first pressure information mentioned above.

[0106] Step S403: Determine whether the pressure is less than the preset lower limit.

[0107] In implementation, such as Figure 5 As shown, the operating pressure range of the EEG device can be preset based on empirical values ​​(such as 0.8 kPa to 3 kPa), i.e., the preset pressure range (such as 1 kPa to 2 kPa) in the above embodiment.

[0108] If it is less than the preset lower limit, proceed to step S404; if it is not less than the preset lower limit, proceed to step S405.

[0109] Step S404: Turn on the yellow light and disable EEG detection.

[0110] For example, if the pressure is less than 1 kPa, a yellow light can be lit to remind the user that the device is too loose, the detection noise may be too loud, and there is a risk of the device falling off, and the EEG acquisition function will not be activated.

[0111] Step S405: Determine whether the pressure is greater than the preset upper limit.

[0112] If the value is greater than the preset upper limit, proceed to step S406; if the value is not greater than the preset upper limit, proceed to step S407.

[0113] Step S406: Turn on the red light and disable EEG detection.

[0114] For example, if the pressure is greater than 2 kPa, a red light can be lit to remind the user that the device is too tight, the noise level may be too high, and the EEG acquisition function will not be activated.

[0115] Step S407: Collect EEG information.

[0116] During implementation, the wearer's brainwave information can be collected using an EEG sensor.

[0117] Step S408: Preprocessing of EEG information.

[0118] During implementation, preprocessing such as filtering can be performed after acquiring EEG information.

[0119] Step S409: Feature fusion.

[0120] During implementation, a pre-set feature fusion engine can be used to perform comprehensive analysis of EEG information and pressure information.

[0121] Step S410: Adaptive model selection.

[0122] During implementation, even with the user wearing the device, EEG noise originates from multiple sources. Therefore, within a pressure range of 1 kPa to 2 kPa, multiple EEG detection models can be adapted. By inputting pressure and EEG information, such as specific data from electroencephalograms (EEGs), different EEG detection models can be selected. Under different EEG detection models, different weights corresponding to different frequencies of EEG information are input, thereby reducing the impact of noise and improving detection accuracy. Step S411: Perform EEG analysis using the M1 model.

[0123] Step S412: Perform EEG analysis using the M2 model.

[0124] Step S413: Perform EEG analysis using the M3 model.

[0125] Step S414: Obtain the EEG detection results.

[0126] During implementation, the model can be used to analyze and detect the user's focus, mood, and / or fatigue state.

[0127] In this embodiment, by performing wearing comfort detection and comprehensive analysis of EEG signals based on the pressure of the user's worn device, the wearing comfort of the EEG device can be improved while the accuracy of EEG detection results can be increased.

[0128] Furthermore, related technologies for unlocking and logging into electronic devices typically rely on static authentication information collected by fingerprints and cameras. However, this method may have potential risks. For example, non-liveness detection carries the risk of feature theft, leading to unauthorized login to the device. Additionally, involuntary login raises privacy and security concerns; for instance, after automatically identifying user characteristics and unlocking the device, the user may not be in a position to use the device.

[0129] Brainprint recognition, on the other hand, only triggers the generation of brainwaves (i.e., brainprints, EEG information) when the individual is alive and has subjective intent. Furthermore, the distinctive characteristics of these brainwaves make them difficult to forge or steal. However, using EEG information for identification may present the following problems: as users age or their physical condition changes, the feature model corresponding to their EEG information may also change. If a fixed model is used for user authentication, the detected model and the stored model may become mismatched in later stages, thus preventing authentication.

[0130] In summary, based on the above methods for improving the wearing comfort and detection accuracy of EEG devices, this application proposes a method for brainprint recognition login and self-updating of the brainprint storage model, which can be applied to EEG devices to achieve automatic updating of the user's brainprint recognition login storage model.

[0131] For example, such as Figure 6 As shown, the method for brainprint recognition login and brainprint storage model self-updating may include the following steps S601 to S609: Step S601: Electroencephalogram (EEG) data detection.

[0132] Here, the EEG data can correspond to the first EEG information in the above embodiment.

[0133] Step S602: Pre-construct user model.

[0134] Here, the user model can correspond to the second biometric information in the above embodiments.

[0135] Step S603: Compare with the storage model.

[0136] Here, the first EEG information (first biometric information) is compared with the stored user model (second biometric information).

[0137] In implementation, such as Figure 7 As shown, a flashing icon and its corresponding flashing frequency can be preset. Further, the user is guided to gaze at the icon to induce brainwaves, thereby controlling the EEG device to collect the user's EEG information. Feature extraction is performed on the EEG information, and a user model is constructed based on the extracted EEG features and stored in a matching model library, i.e., the target database in the above embodiment. Further, by comparing the brainprint models in the matching model library with the currently collected brainprint model, the user's identity is identified, and corresponding instructions are output.

[0138] During implementation, if the user does not wear the EEG device, the device will not be unlocked via brainprint. Only when the user wears the EEG device and looks at the icon in step S602 will visual evoked EEG be triggered. The EEG device will then collect the user's EEG information, analyze it, and extract brainprint features. The extracted model will then be compared with the user's feature model in the model library.

[0139] Step S604: Determine whether the authentication score is greater than the preset value.

[0140] Here, the authentication score can correspond to the target matching degree in the above embodiments.

[0141] During implementation, if the authentication score is greater than 70, the user corresponding to the brainprint model can be identified and step S605 can be executed; if the authentication score is not greater than 70, the current identity authentication is considered unsuccessful and step S606 can be executed.

[0142] Step S605: Authentication passed.

[0143] After executing step S605, proceed to step S607.

[0144] During implementation, if the authentication score shows a continuous downward trend for 5 consecutive times after the authentication is passed (i.e., step S608), the automatic update of the brain pattern model can be triggered.

[0145] Step S606: Authentication failed.

[0146] If the user is not paying attention to the preset icon, or is distracted, the corresponding brainprint model cannot be generated. In the event of authentication failure, further steps will not be taken.

[0147] Step S607: Log in to the system.

[0148] Here, after the user model is authenticated, the device can be unlocked and the device system can be logged in.

[0149] Step S608: The score for multiple consecutive authentications shows a downward trend.

[0150] Step S609: Trigger the model update process.

[0151] In this embodiment, brainprint recognition is implemented in two steps. The first step is registration, which involves extracting the EEG signal features of known users under specific visual stimuli as templates and storing them in a template library (matching model library). The second step involves extracting the EEG signal features of unknown users under the same stimuli and matching them with the templates (user models) in the template library. The matching result is the identified identity information. Thus, a predetermined icon can be pushed to the screen after the second device (such as a computer) is powered on. The user wears the first device (such as an EEG device) and performs visually evoked EEG operations based on the icon. When the brainprint information collected by the second device matches the matched brainprint model in the second device, the second device is unlocked. Furthermore, if the model authentication score decreases repeatedly, the model in the template library is automatically updated, allowing the user to update their brainprint model imperceptibly.

[0152] This application also proposes a computer program including computer-readable code. When the computer-readable code is run in an electronic device, the processor in the electronic device executes a control method for implementing the first device provided in this application.

[0153] This application provides a computer program product, including a computer program or instructions. When the computer program or instructions are executed by a processor, they implement the control method of the first device provided in this application.

[0154] This application provides a computer-readable storage medium storing a computer program or instructions, which, when executed by a processor, implement the control method of the first device provided in this application.

[0155] This application is described with reference to flowchart illustrations and / or block diagrams of methods and apparatus according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0156] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0157] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0158] 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.

[0159] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0160] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0161] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0163] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0164] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0165] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0166] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks. The above descriptions are merely embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, and improvements made within the spirit and scope of this application are included within the scope of protection of this application.

Claims

1. A method for controlling a first device, the method comprising: In response to a user wearing the first device, the first device acquires the target area of ​​the target region in contact between the user and the first device and the first pressure information corresponding to the target region, wherein the first device is used to acquire biometric information. Based on the target area and the first pressure information, the first pressure information corresponding to the target area is determined; When the first pressure information is within a preset pressure range, the first device is controlled to acquire the user's first biometric information.

2. The method according to claim 1, wherein the preset pressure range includes a pressure not higher than a first pressure threshold and not lower than a second pressure threshold, wherein the first pressure threshold is higher than the second pressure threshold; the method further includes: If the first pressure information is within the preset pressure range, output a first prompt message; The first prompt information indicates that the first pressure information is not higher than the first pressure threshold and not lower than the second pressure threshold.

3. The method according to claim 2, further comprising: If the first pressure information is higher than the first pressure threshold or lower than the second pressure threshold, a second prompt message is output. The second prompt message indicates that the acquisition of the first biometric information will not be initiated.

4. The method according to claim 1, wherein the first device and the second device are connected in communication, the method further comprising: In response to acquiring the first biometric information, a target matching degree is determined between the first biometric information and the second biometric information of the user in the target database; the second biometric information is used to trigger a target program in the second device; If the target matching degree is higher than the matching degree threshold, the target program is triggered.

5. The method according to claim 4, further comprising: In response to a target matching degree exceeding the matching degree threshold within a consecutive number of target attempts, but continuously decreasing, an update of the second biometric information in the target database is triggered.

6. The method according to claim 4, wherein the target program includes an authentication program for the second device, the authentication program being used to authenticate the user.

7. The method according to any one of claims 1 to 6, wherein the biometric information includes electroencephalogram (EEG) information, the preset pressure range includes multiple pressure ranges, and the method further includes: The target pressure range containing the first pressure information is determined from the plurality of pressure ranges; Based on the correspondence between different pressure ranges and weight combinations of different types of EEG information, the target weight combinations corresponding to different types of EEG information under the target pressure range are determined. Based on the target weight combination and the type of the first EEG information, the EEG detection result of the user is determined.

8. The method according to claim 7, wherein determining the target weight combination corresponding to different types of EEG information under the target pressure range based on the correspondence between different pressure ranges and weight combinations of different types of EEG information includes: Based on the correspondence between weight combinations of different types of EEG information and different pressure ranges, when the target pressure range is within the first pressure range, the target weight combination is determined to include a first weight corresponding to the first type of EEG information and a second weight corresponding to the second type of EEG information. Based on the correspondence between weight combinations of different types of EEG information and different pressure ranges, when the target pressure range is within the second pressure range, the target weight combination is determined to include a third weight corresponding to the first type of EEG information and a fourth weight corresponding to the second type of EEG information; the first pressure corresponding to the first pressure range is less than the second pressure corresponding to the second pressure range, the first weight is lower than the third weight, and the second weight is higher than the fourth weight.

9. A first device, comprising: Pressure sensing components, data acquisition components, and control components; The pressure sensing component is used to collect pressure information; The control component is configured to, in response to a user wearing the first device, acquire the target area of ​​the target region in contact between the user and the first device and the first pressure information corresponding to the target region; Based on the target area and the first pressure information, the first pressure information corresponding to the target area is determined; When the first pressure information is within a preset pressure range, the first device is controlled to acquire the user's first biometric information through the acquisition component.

10. The first device according to claim 9, wherein the first device and the second device are communicatively connected, and the control component is further configured to: In response to acquiring the first biometric information, a target matching degree is determined between the first biometric information and the second biometric information of the user in the target database; the second biometric information is used to trigger a target program in the second device; If the target matching degree is higher than the matching degree threshold, the target program is triggered.

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