Punching interaction method and system for exchanging business cards based on AR smart glasses

Abstract

This invention discloses a fist-bump interactive business card exchange method and system based on AR smart glasses, specifically relating to the field of augmented reality interaction technology. Users pre-store digital business cards. When both users clench their fists and their fists touch, the two devices exchange motion sensor data features. The compatibility matching degree of the motion waveforms of both parties is calculated; if it exceeds a threshold, it is determined to be a valid fist bump and pairing is triggered. After pairing, a secure communication link is established and digital business cards are exchanged. The other party's facial image is captured and feature vectors are extracted, then associated with and stored with the received business card information. This record is matched during subsequent meetings. This invention effectively defends against gesture hijacking attacks through motion compatibility verification, ensuring interactive security in open environments. It creatively integrates fist-bump interaction, information exchange, and facial binding into a trusted closed loop, ensuring accurate identity memory and preventing corruption. It supports context-based hierarchical privacy control, achieving refined protection of user data sovereignty while maintaining natural and smooth interaction.

Description

Technical Field

[0001] This invention relates to the field of augmented reality interaction technology, and more specifically, to a fist-bump interactive business card exchange method and system based on AR smart glasses. Background Technology

[0002] In business networking and everyday interpersonal interactions, the ability to quickly and accurately exchange and remember contact information is a fundamental and crucial need. Traditional paper business cards suffer from drawbacks such as static information, susceptibility to loss, environmental unfriendliness, and difficulty in digital management. With technological advancements, various electronic business card solutions have emerged, including exchange methods based on QR code scanning or near-field communication (NFC, Bluetooth).

[0003] QR code solutions typically require users to take out their phones, open specific applications, and complete a series of operations such as alignment and scanning, which is relatively cumbersome and may disrupt the natural flow of conversation. While NFC or Bluetooth solutions can achieve short-range data transmission, they often require users to manually activate the corresponding functions beforehand and perform explicit operation commands such as "touch" or "pair," making the interaction process somewhat stiff and lacking the natural emotional transmission and social ritual of a handshake or fist bump.

[0004] The development of augmented reality technology, especially head-mounted AR smart glasses, has provided a new hardware platform for addressing the aforementioned issues. Some research and solutions have attempted to display virtual business cards in AR glasses, but their triggering mechanisms mostly rely on voice commands, eye-tracking control, or manual selection from virtual menus, leaving room for improvement in terms of naturalness, intuitiveness, and social immersion. More importantly, existing AR business card exchange solutions do not adequately consider security and robustness in real-world scenarios. For example, in crowded settings such as exhibitions and parties, existing visual gesture-triggered solutions may struggle to effectively distinguish between a deliberate social fist bump between two parties and malicious or accidental spatial contact. Attackers could use technical means (such as wearing special gloves or using adversarial samples to interfere with recognition) to forge gestures, inducing the system to trigger incorrectly, thereby stealing or contaminating exchanged data without the user's awareness, or even disrupting subsequent facial recognition and identification processes. This potential threat reduces the system's reliability in open environments. Therefore, this invention proposes a fist bump interactive business card exchange method and system based on AR smart glasses to address the aforementioned problems. Summary of the Invention

[0005] To achieve the above objectives, the present invention provides the following technical solution: The method for interactive business card exchange based on AR smart glasses includes the following steps: S1. Users create and store their digital business card information in the local storage of AR smart glasses or in the cloud server associated with them; The S2 AR smart glasses' built-in camera continuously captures images of the user's hands within their field of vision; S3. By using the gesture recognition engine running on the processing module, the hand image is analyzed in real time. When the user makes a fist gesture, the inertial measurement unit built into the AR smart glasses is activated to collect the motion sensing data of the user's arm. When it is further recognized that the fists of both users are in contact in three-dimensional space, the AR smart glasses of both parties exchange motion sensing data features within a set time window before and after the contact event through a low-power communication link. S4. Calculate the coordination matching degree of the arm motion waveforms of both parties based on the characteristics of the exchanged motion sensor data; when the coordination matching degree exceeds the preset coordination threshold, it is determined to be a valid coordinated fist bump interaction and the pairing process is triggered. S5. After pairing is triggered, the AR smart glasses of both parties establish a point-to-point secure communication link and exchange digital business card information through the link. S6. Upon successful information exchange, capture the other party's facial image and extract facial feature vectors, then associate and store the feature vectors with the received digital business card information. In subsequent meetings, when the bound record is matched through real-time facial recognition, the associated business card information is overlaid on the other party's face or side of their body in the AR field of view.

[0006] In a preferred embodiment, the digital business card information consists of three types of information: public information, privacy information, and information for which active authorization is required.

[0007] In a preferred embodiment, the gesture recognition engine locates multiple preset key points of the hands in real time and determines that both hands are in a closed fist state. At the same time, it calculates the three-dimensional coordinates of the two fists through stereo vision or inertial measurement unit data. When it is detected that the Euclidean distance between the two fists is less than a preset distance threshold within N consecutive frames, it is determined that contact has occurred.

[0008] In a preferred embodiment, the motion sensing data features include motion waveform data acquired by the inertial measurement unit during the approach, contact, and rebound phases of the fist, as well as energy envelope features and dominant frequency features extracted from the motion waveform data of each phase.

[0009] In a preferred embodiment, before calculating the degree of coordination matching of the arm motion waveforms of both parties, the characteristics of the exchanged motion sensing data are processed as follows: The original motion waveform data of both parties in the overall stage are compared, and the similarity metric between the two waveform sequences is calculated. This metric is obtained by calculating the normalized cross-correlation coefficient or the inverse distance obtained after applying the dynamic time warping algorithm, and a motion waveform similarity score is generated for the overall stage. Energy envelope features are extracted from the motion waveform data of both parties at each stage. Then, the energy envelope vectors of the three consecutive stages, i.e. the overall stage, are constructed. The similarity between the energy envelope vectors of both parties is calculated by calculating the reciprocal or cosine similarity of the Euclidean distance, and the energy similarity score of the overall stage is obtained. The dominant frequency features are extracted from the motion waveform data of both sides in each stage. The absolute value of the difference between the dominant frequency values ​​of both sides in each stage is calculated. Then, the three stages are summed to obtain the overall dominant frequency difference score.

[0010] In a preferred embodiment, the energy envelope feature refers to: dividing the approach, contact, or rebound phase of the fist into multiple time intervals according to the same time interval, obtaining the area enclosed by the motion waveform and the horizontal axis of the rectangular coordinate system for each time interval, and the sequence number of the peak and valley values ​​in the multiple time intervals divided in that phase.

[0011] In a preferred embodiment, the degree of synergy matching is calculated as follows: The overall stage frequency difference score is converted into a positive index using a preset exponential decay function. Then, it is weighted and summed together with the positive indexes, namely the motion waveform similarity score and the energy similarity score, to obtain the synergy matching degree.

[0012] In a preferred embodiment, public information can be exchanged via a low-power communication link, privacy information is exchanged after a point-to-point secure communication link is established, and actively authorized access information is displayed after a point-to-point secure communication link is established and with the user's permission.

[0013] In a preferred embodiment, the fist-bump interactive business card exchange system based on AR smart glasses includes: The information pre-setting module is used to create and store the user's digital business card information locally or in the cloud on AR smart glasses; The gesture sensing module is used to continuously capture images of the user's hands within their field of vision via the camera; The fist-bump trigger module is used to identify the fist-clenching posture and fist contact event in the hand image, and trigger the acquisition and exchange of motion sensor data. The collaborative verification module is used to calculate the degree of collaborative matching of the arm motion waveforms of both parties based on the characteristics of the exchanged motion sensing data, and to determine that the interaction is valid and trigger pairing when the degree of matching exceeds the threshold. The secure exchange module is used to establish a secure communication link between devices and to complete the mutual transmission of digital business card information; The AR prompt module is used to bind the other party's facial features and business card information after a successful exchange, and to overlay the corresponding business card in the AR field of view during subsequent recognition.

[0014] The technical effects and advantages of this invention are as follows: This invention establishes a dynamic security barrier in augmented reality interactions by introducing a collaborative verification mechanism based on multimodal biomechanical characteristics, effectively addressing potential gesture hijacking and man-in-the-middle attacks in dense social scenarios. During fist-bump recognition, this mechanism not only relies on visual information to determine contact but, more importantly, simultaneously collects and compares complete motion data recorded by the inertial measurement units (IMUs) of both parties' devices—including acceleration and angular velocity waveforms of the arms during approach, contact, and rebound, the envelope shape of energy release, and the dominant frequency of the movement. An algorithm generates a matching score that comprehensively characterizes the coordination between the two parties' movements; only fist bumps exhibiting high synchronization in three-dimensional spatial trajectory, force timing, and rhythmic frequency are considered valid interactions. This biomechanical-based two-way verification makes it difficult for third parties to forge compliant collaborative movement patterns through simple position insertion or static gesture imitation. Thus, in an open environment full of uncontrollable factors, it constructs reliable identity verification for each information exchange, significantly improving the system's robustness against interference and fraud.

[0015] This invention seamlessly integrates natural social rituals, secure information exchange, and biometric binding into a functional closed loop, completely resolving the core pain point of traditional solutions where exchanged information and facial recognition are disconnected, and fundamentally eliminating the risk of identity binding being compromised. The system design ensures that the immediate, high-precision facial feature acquisition and storage process is only triggered after successful multimodal collaborative verification and information exchange via an encrypted link. This means that the facial features ultimately encrypted with digital business card information have had their corresponding social object identity double-confirmed through motion collaboration verification and secure communication, thus guaranteeing the authenticity and credibility of the "face-identity" correspondence. This closed-loop logic not only allows users to obtain accurate identity recall assistance through AR prompts during subsequent encounters, but more importantly, it establishes a positive trust chain: each successful AR recognition, in turn, verifies the authenticity and security of the initial interaction, making the user's augmented reality social graph a trustworthy network that continuously verifies itself over time.

[0016] This invention introduces a context-based, trust-driven hierarchical information management and dynamic access control mechanism. While pursuing seamless and natural interaction, it provides refined sovereignty protection for users' core data assets. Based on the initial trust foundation established by the aforementioned security loop, the system allows users to define digital business card content in multiple dimensions and intelligently implement differentiated data flow strategies at different stages of interaction. For example, basic identity information can be lightweightly synchronized during the collaborative verification stage to maintain smooth social interaction; while private contact information is transmitted only after establishing a high-level encrypted channel; for particularly sensitive or context-dependent data, the system supports initiating real-time authorization requests within a secure channel, with the information owner making the final contextual decision. This design transforms one-time, static privacy settings into a dynamic protection process that spans the entire social lifecycle and can be linked to specific scenarios and levels of trust. Technically, it perfectly interprets the privacy design principles of "minimum necessity" and "user controllability," enabling convenient and efficient social connections to coexist with robust and flexible data sovereignty. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a schematic diagram of the fist-bump interactive business card exchange method based on AR smart glasses in this invention.

[0018] Figure 2 This is a schematic diagram of the fist-bump interactive business card exchange system based on AR smart glasses in this invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Reference Figure 1 - Figure 2 The following examples were obtained: Example 1: A fist-bump interactive business card exchange method based on AR smart glasses, comprising the following steps: S1. Users create and store their digital business card information in the local storage of their AR smart glasses or in the associated cloud server. The significance of this step is that it digitizes and structures traditional static, physical business card information, establishing a personal data foundation for users that can be recognized and accessed by the intelligent system. It completes the transformation from physical carrier to digital resource, providing a data source for subsequent fully automatic and seamless information exchange, and is a prerequisite for system operation.

[0021] The S2 AR smart glasses' built-in camera continuously captures images of the user's hands within their field of vision; this step is significant because it provides the system with real-time, continuous environmental perception input. Through uninterrupted visual monitoring, the system is ready to recognize specific interaction intentions (fist bump gestures) at any time without the user's active intervention, thus completely handing over the power to initiate interaction to the user's natural social actions. This eliminates the intermediate operations required by traditional solutions, such as actively searching, clicking, or focusing, laying the perceptual foundation for achieving a "natural and intuitive" interactive experience.

[0022] S3. The gesture recognition engine running on the processing module analyzes hand images in real time. When a user makes a fist gesture, the inertial measurement unit built into the AR smart glasses is activated to collect motion sensor data of the user's arm. When further detection indicates that the fists of both users are in contact in three-dimensional space, the AR smart glasses exchange motion sensor data features within a set time window before and after the contact event via a low-power communication link. The significance of this step is to accurately capture and confirm a social fist bump and simultaneously collect multi-dimensional bio-motion evidence. It includes two key sub-processes: first, visually confirming the "fist bump" gesture and the "spatial contact" event, transforming a social ritual rich in human emotion into a clear, machine-recognizable instruction; second, at this instant, activating the inertial measurement unit and exchanging motion sensor data features, providing crucial data evidence for subsequent verification that the interaction was a genuine, collaborative, and proactive action (rather than accidental touch or malicious interference). This achieves an upgrade from "recognizing actions" to "collecting interaction evidence."

[0023] S4. Based on the characteristics of the exchanged motion sensor data, calculate the degree of coordination matching of the arm motion waveforms of both parties. When the degree of coordination matching exceeds a preset coordination threshold, it is determined to be a valid coordinated fist bump interaction, and the pairing process is triggered. The significance of this step is to verify the security and authenticity of the "interaction evidence" collected in the previous step. By analyzing the degree of coordination of the motion data of both parties through algorithms, the system can effectively distinguish between a conscious, coordinated fist bump and an unintentional touch, passive contact, or interference deliberately inserted by a third party. This verification step is like a dynamic "social password," ensuring a high degree of confidence in the information exchange triggering conditions, greatly reducing the false trigger rate, improving the security and reliability of the entire interaction process, and serving as the core defense against information hijacking or contamination.

[0024] S5. After pairing is triggered, the AR smart glasses of both parties establish a secure point-to-point communication link and exchange digital business card information through this link. The significance of this step is that, after passing the collaboration verification, secure and efficient data transmission is performed. It establishes a direct encrypted communication channel between the devices, ensuring the confidentiality and integrity of the business card information during the exchange process. This step completes the secure transfer from social intent (fist bump) to actual data assets (digital business card), which is the substantial delivery stage of information transmission from one party to the other.

[0025] S6. Simultaneously with successful information exchange, capture the other party's facial image and extract facial feature vectors. For example, at the instant of successful data exchange, the front-facing cameras of both parties' glasses quickly capture a frontal photo of the other party's face. For instance, using existing technologies like ArcFace or other models, extract high-dimensional feature vectors from the photo. This feature vector is then associated with and stored in conjunction with the received digital business card information. In subsequent meetings, when this binding record is matched through real-time facial recognition, the associated business card information is overlaid and displayed next to the other party's face or body in the AR view. The significance of this step lies in achieving a closed loop and enhancement of the information exchange function, solving the fundamental social pain point of "remembering names but not matching faces." It proactively establishes a "face-identity" binding relationship at the instant of data exchange, anchoring digital information to a specific biological individual. In subsequent chance encounters, real-time recognition automatically triggers information prompts, transforming stored static data into dynamic, contextualized augmented reality prompts. This not only completes the full social cycle from "exchange" to "recognition" but also seamlessly integrates digital information back into the real world through AR technology, achieving a seamless connection between online management and offline social interaction. Digital business card information consists of three categories: public information, private information, and information that requires active authorization for access.

[0026] In one specific implementation, the gesture recognition and contact determination process is executed precisely according to the following steps. First, the gesture recognition engine deployed on the AR smart glasses processing unit continuously receives video streams from the built-in camera and locates 21 hand key points in real time based on a pre-trained hand key point detection model (e.g., based on the MediaPipeHands model). The model can stably output the two-dimensional pixel coordinates of the 21 key points of each hand, which precisely correspond to the finger joints and fingertips. Subsequently, based on the spatial distribution relationship of these key points, when it is detected that all fingertip key points converge towards the palm and the knuckle key points exhibit a specific bending configuration, it is determined that the hand is in a closed fist (Fist) state. Based on the recognition that both users' hands are in a closed fist state, the system initiates a multimodal spatial positioning process: On the one hand, using the binocular or depth camera of the AR smart glasses, the two-dimensional image coordinates of specific key points on both users' fists (such as the metacarpophalangeal joint of the middle finger) are converted into three-dimensional spatial coordinates relative to the glasses coordinate system through the principle of stereo vision; on the other hand, the six-axis inertial data (including three-axis acceleration and three-axis angular velocity) provided by the inertial measurement unit are fused simultaneously, and the coordinates calculated by vision are corrected and smoothed in real time through sensor fusion algorithms (such as complementary filtering or Kalman filtering in existing technologies) to overcome the problem of visual blurring or occlusion during rapid movement, thereby obtaining a high-frequency and high-reliability three-dimensional coordinate sequence of both users' fists.

[0027] Once the contact determination logic is activated, the system continuously calculates the Euclidean distance between the two fists in 3D space at each frame. To effectively filter out accidental close-range brushes or false alarms caused by sensor noise, the system sets a strict distance threshold (e.g., 5 cm) and a time duration threshold (e.g., 5 consecutive frames, corresponding to approximately 0.1 seconds). Only when the calculated Euclidean distance is detected to be less than the preset distance threshold in multiple consecutive video frames—that is, when the two fists maintain a brief but stable contact or very close proximity state in 3D space—is the system finally confirmed as a valid fist bump event. This design effectively excludes unintentional waving or hand gestures. This determination mechanism demonstrates good environmental adaptability. For example, in scenarios such as corridors with changing lighting or people lightly brushing past each other, due to the integration of trajectory prediction by the inertial measurement unit, even if the visual signal is briefly lost, the system can still maintain the continuity of coordinate estimation based on motion inertia, thus ensuring the robustness of the contact determination and providing accurate and reliable trigger signals for subsequent collaborative verification and data exchange.

[0028] In one specific implementation, the process of extracting and constructing motion sensing data features follows these steps. Upon confirmation of a fist-bump contact event, the system uses the precise timestamp of the contact as a reference point. It defines the inertial measurement unit data that traces back a set duration (e.g., 0.3 seconds) as the fist approach phase, and the data that continues forward for the same duration (e.g., 0.3 seconds) as the fist rebound phase. Data within a very short time window (e.g., 0.05 seconds) before and after the contact moment is precisely defined as the contact phase. This accurately divides the continuous six-axis motion waveform data (including three-axis acceleration and three-axis angular velocity) into three continuous and logically defined motion phases.

[0029] For each stage of motion waveform data (e.g., the resultant acceleration amplitude sequence), a refined extraction of energy envelope features is performed: First, the time series data of this stage is divided into a series of continuous time intervals at fixed time intervals (e.g., every 0.02 seconds); then, the geometric area enclosed by the motion waveform curve and the horizontal axis of time in each time interval is calculated, and this area value quantifies the motion energy intensity within that time period; simultaneously, the motion waveform of the entire stage is scanned to identify its global peaks and valleys, and the sequence number of the specific time interval where these two extreme points are located is recorded. Thus, the energy envelope feature of each stage is represented as a vector composed of the area values ​​of each time interval, with an index of the extreme value location.

[0030] Frequency domain analysis is performed on the motion waveform data of each stage to extract the dominant frequency feature: by performing a fast Fourier transform on the time series data of this stage, its spectrum is obtained. Then, within the effective physiological motion frequency range (e.g., 0.5 Hz to 10 Hz), the frequency component with the largest spectral energy is found, and this frequency value is determined as the dominant frequency feature of the motion in this stage. This feature reflects the periodic motion rhythm of the user in this action stage.

[0031] The energy envelope features (including area vectors and extreme value indices) extracted from the three stages of approach, contact, and rebound in the above steps are structured and encapsulated with the dominant frequency features to form a comprehensive motion sensing data feature set for subsequent calculation of the coordination degree. For example, in a typical rapid punching action, the area vector in the approach stage may show an increasing trend and a high dominant frequency, the area vector in the contact stage may have a peak and a concentrated extreme value indices, while the area vector in the rebound stage shows a decreasing pattern. These features together provide a rich data foundation for judging the coordination of the action.

[0032] In one specific implementation, the motion sensing data features obtained from the exchange between the two parties undergo preprocessing and intermediate index calculation before collaborative analysis. First, the overall motion waveform similarity score is calculated. This step aligns the original motion waveform sequences of the two users within the complete interaction time window, i.e., the combined acceleration amplitude-time sequence continuously spliced ​​together in the three stages of approach, contact, and rebound, on the time axis. After alignment, mature algorithms in the field of signal processing are used to calculate the morphological similarity between the two sequences. For example, the normalized cross-correlation coefficient of the two sequences under zero hysteresis is calculated. The closer this coefficient is to 1, the more synchronized the waveforms are in terms of shape and trend of change. Alternatively, when there is a slight stretching or contraction in the actions of the two parties on the time scale, a dynamic time warping algorithm is used to nonlinearly align the two sequences and calculate the minimum cumulative distance between them. Then, the reciprocal of this distance is taken and normalized to convert it into a similarity metric between 0 and 1, which is the overall motion waveform similarity score.

[0033] The overall energy envelope similarity score is calculated based on the energy envelope features extracted from the three independent phases of each user's action. Specifically, the feature vectors obtained by each user in the three phases of punch approach, contact, and rebound, which are composed of the area values ​​of each time interval, are concatenated into a high-dimensional overall energy envelope feature vector according to the order of the action. Then, the similarity between the two overall energy envelope vectors from user A and user B is calculated. A common method is to calculate the Euclidean distance between the two vectors. The smaller the distance, the more consistent the rhythm and intensity distribution of energy release between the two users during the entire action. This distance is then converted into a similarity score by passing the reciprocal function and normalizing it. Alternatively, the cosine value of the angle between the two vectors can be calculated to directly obtain the cosine similarity in the range of -1 to 1, which is then mapped to the range of 0 to 1 to obtain the overall energy envelope similarity score.

[0034] The overall dominant frequency difference score is calculated. This step focuses on the rhythm consistency analysis of the movement. For the three stages of punch approach, contact, and rebound, the dominant frequency characteristic value of the motion waveform data of both users in each stage is extracted. This value represents the main frequency of the periodic movement in that stage. For each stage, the absolute value of the difference between the dominant frequency values ​​of user A and user B in that stage is calculated. This absolute value directly quantifies the instantaneous difference in rhythm between the two parties in that movement segment. Finally, the three absolute values ​​of dominant frequency difference calculated in the approach, contact, and rebound stages are arithmetically summed to obtain a scalar value, namely the overall dominant frequency difference score. The lower the score, the better the coordination of the movement rhythm of the two parties in the entire punch-bumping action. Finally, the three intermediate indicators obtained from the above steps—overall motion waveform similarity score, overall energy envelope similarity score, and overall dominant frequency difference score—are normalized and output to prepare data for the subsequent calculation of a comprehensive coordination matching score. For example, in a successful coordinated fist bump, the waveform similarity score and energy similarity score may both be higher than 0.8, while the dominant frequency difference score may be lower than 2 Hz. This set of data will help to significantly distinguish it from the data characteristics generated by an accidental and uncoordinated contact.

[0035] In one specific implementation, the calculation of the synergy matching degree is achieved through an explicit mathematical fusion process that combines three intermediate indicators—the overall motion waveform similarity score (Sw), the overall energy envelope similarity score (Se), and the positive consistency score (Cf) derived from the overall dominant frequency difference score (Df)—into a single scalar value for decision-making.

[0036] The frequency difference score (Df) is converted into a frequency consistency score (Cf) using a pre-defined exponential decay function. A typical form of this exponential decay function is: Cf = e^(-k*Df). Here, k is a decay coefficient greater than zero, used to control the sensitivity of the difference to the score; Df is the frequency difference score calculated in the previous step (in Hertz). The value of k is preset, for example, it can be determined based on statistical analysis of a large amount of real fist-bump interaction data, so that typical coordinated movements (smaller Df) can obtain a higher Cf value (close to 1), while obviously uncoordinated movements (larger Df) cause the Cf value to decay sharply to close to 0. For example, if k = 1.0, when the measured frequency difference score Df = 1.5Hz, Cf = e^(-1.0*1.5) ≈ 0.223 is calculated, indicating low rhythm consistency; if Df = 0.2Hz, then Cf = e^(-1.0*0.2) ≈ 0.819, indicating high rhythm consistency.

[0037] The three positive metrics, Sw, Se, and Cf, are weighted and summed to calculate the final coordination matching degree (C). The formula is: C = α*Sw + β*Se + γ*Cf. Here, α, β, and γ are the weight coefficients assigned to motion waveform similarity, energy envelope similarity, and frequency consistency, respectively. These weight coefficients satisfy α + β + γ = 1 and are all non-negative. The weights can be set based on domain knowledge or experimental data: for example, if the synchronization of motion patterns is considered most critical, a higher α (e.g., 0.5) can be set; if the coordination of energy release is considered equally important, β can be set to 0.3; correspondingly, the rhythm consistency weight γ is set to 0.2. These weight parameters are set during system initialization and can be stored in a configuration file, allowing for calibration and optimization according to different application scenarios or user groups. Through the above calculation, the final coordination matching degree C is a value between 0 and 1, which can be directly compared with a preset coordination threshold to determine the effectiveness of the interaction.

[0038] In the AR smart glasses-based fist-bump interactive business card exchange method, a strategy of hierarchical management and dynamic exchange of digital business card information is implemented. The following is an illustration using a specific example. Assume user Zhang San's digital business card contains the following information: Public information: Name "Zhang San", Company name "Technology Consulting Company"; Privacy information: Personal mobile phone number, Private email address; Actively authorized access information: A link to an electronic document containing details of recent cooperation cases.

[0039] When Zhang San and Li Si trigger their interaction through a fist bump, the information exchange and display proceeds in the following layered process: Public information exchange: Simultaneously with the fist bump being recognized and both parties' AR smart glasses initially exchanging motion sensor data features via a low-power Bluetooth link, Zhang San's name and company name, pre-marked as "public," are encapsulated and sent to Li Si's device. This process occurs before or simultaneously with the collaborative verification and represents a lightweight, preliminary information exchange. Even if subsequent collaborative verification fails, this information can serve as a brief social identifier.

[0040] Privacy-related information exchange: After the system completes motion coordination verification and determines it as a valid fist bump interaction, the AR smart glasses of both parties will establish a high-strength point-to-point encrypted secure communication link (e.g., an AES-256 encrypted channel based on Wi-FiDirect). Subsequently, the mobile phone number and private email address marked as "private" in Zhang San's device will be transmitted to Li Si's device through this secure link and stored in the encrypted social graph on Li Si's device, ensuring the secure transmission of highly sensitive information.

[0041] Display of Actively Authorized Access Information: After a secure communication link is established and privacy information is exchanged, Li Si's AR glasses interface does not immediately display Zhang San's case document link. Instead, the link is presented as a thumbnail with an "Request Access" icon on Zhang San's virtual business card. When Li Si is interested in this information, he can send a viewing request to Zhang San through a preset, privacy-conscious confirmation gesture (such as briefly looking at the icon and lightly touching the temple of the glasses). This request is notified to Zhang San in real time through the established secure link. At this time, a clear prompt will pop up in Zhang San's AR field of vision, asking whether to allow Li Si to access the case document. Only after Zhang San gives explicit permission through a voice command "agree" or a nod will the link or preview content of the case document be unlocked and fully displayed in Li Si's field of vision. This mechanism ensures that the highest level of information control always remains in the hands of the information owner, realizing dynamic and contextualized permission management in social scenarios. Through the above-mentioned hierarchical processing, the system ensures social efficiency (rapid exchange of public information) while building a deep privacy and security barrier (encrypted transmission of private information), and ultimately entrusts the right to disclose specific sensitive information to users for real-time, contextual judgment (active authorization of access), thus achieving a balance between convenience and security.

[0042] Example 2: A fist-bump interactive business card exchange system based on AR smart glasses, comprising: The information pre-setting module is used to create and store the user's digital business card information locally or in the cloud on AR smart glasses; The gesture sensing module is used to continuously capture images of the user's hands within their field of vision via the camera; The fist-bump trigger module is used to identify the fist-clenching posture and fist contact event in the hand image, and trigger the acquisition and exchange of motion sensor data. The collaborative verification module is used to calculate the degree of collaborative matching of the arm motion waveforms of both parties based on the characteristics of the exchanged motion sensing data, and to determine that the interaction is valid and trigger pairing when the degree of matching exceeds the threshold. The secure exchange module is used to establish a secure communication link between devices and to complete the mutual transmission of digital business card information; The AR prompt module is used to bind the other party's facial features and business card information after a successful exchange, and to overlay the corresponding business card in the AR field of view during subsequent recognition.

[0043] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0044] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the 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.

[0045] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0046] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A fist-bump interactive business card exchange method based on AR smart glasses, characterized in that, Includes the following steps: S1. Users create and store their digital business card information in the local storage of AR smart glasses or in the cloud server associated with them; The S2 AR smart glasses' built-in camera continuously captures images of the user's hands within their field of vision; S3. By using the gesture recognition engine running on the processing module, the hand image is analyzed in real time. When the user makes a fist gesture, the inertial measurement unit built into the AR smart glasses is activated to collect the motion sensing data of the user's arm. When it is further recognized that the fists of both users are in contact in three-dimensional space, the AR smart glasses of both parties exchange motion sensing data features within a set time window before and after the contact event through a low-power communication link. S4. Calculate the coordination matching degree of the arm motion waveforms of both parties based on the characteristics of the exchanged motion sensor data; when the coordination matching degree exceeds the preset coordination threshold, it is determined to be a valid coordinated fist bump interaction and the pairing process is triggered. S5. After pairing is triggered, the AR smart glasses of both parties establish a point-to-point secure communication link and exchange digital business card information through the link. S6. Upon successful information exchange, capture the other party's facial image and extract facial feature vectors, then associate and store the feature vectors with the received digital business card information. In subsequent meetings, when the bound record is matched through real-time facial recognition, the associated business card information is overlaid on the other party's face or side of their body in the AR field of view.

2. The interactive business card exchange method based on AR smart glasses according to claim 1, characterized in that, Digital business card information consists of three categories: public information, private information, and information that requires active authorization for access.

3. The interactive business card exchange method based on AR smart glasses according to claim 2, characterized in that, The gesture recognition engine locates multiple preset key points of the hands in real time and determines that both hands are in a closed fist state. At the same time, it calculates the three-dimensional coordinates of the two fists through stereo vision or inertial measurement unit data. When it detects that the Euclidean distance between the two fists is less than the preset distance threshold within N consecutive frames, it determines that contact has occurred.

4. The fist-bump interactive business card exchange method based on AR smart glasses according to claim 3, characterized in that, The motion sensing data features include motion waveform data acquired by the inertial measurement unit during the approach, contact, and rebound phases of the fist, as well as energy envelope features and dominant frequency features extracted from the motion waveform data of each phase.

5. The fist-bump interactive business card exchange method based on AR smart glasses according to claim 4, characterized in that, Before calculating the degree of coordination matching of the arm motion waveforms of both parties, the characteristics of the exchanged motion sensing data are processed as follows: The original motion waveform data of both parties in the overall stage are compared, and the similarity metric between the two waveform sequences is calculated. This metric is obtained by calculating the normalized cross-correlation coefficient or the inverse distance obtained after applying the dynamic time warping algorithm, and a motion waveform similarity score is generated for the overall stage. Energy envelope features are extracted from the motion waveform data of both parties at each stage. Then, the energy envelope vectors of the three consecutive stages, i.e. the overall stage, are constructed. The similarity between the energy envelope vectors of both parties is calculated by calculating the reciprocal or cosine similarity of the Euclidean distance, and the energy similarity score of the overall stage is obtained. The dominant frequency features are extracted from the motion waveform data of both sides in each stage. The absolute value of the difference between the dominant frequency values ​​of both sides in each stage is calculated. Then, the three stages are summed to obtain the overall dominant frequency difference score.

6. The fist-bump interactive business card exchange method based on AR smart glasses according to claim 5, characterized in that, Energy envelope characteristics refer to: dividing the approach, contact, or rebound phase of the fist into multiple time intervals according to the same time interval, obtaining the area enclosed by the motion waveform and the horizontal axis of the rectangular coordinate system for each time interval, as well as the sequence number of the peak and valley values ​​in the multiple time intervals divided in that phase.

7. The interactive business card exchange method based on AR smart glasses according to claim 6, characterized in that, The degree of synergy matching is calculated as follows: The overall stage frequency difference score is converted into a positive index using a preset exponential decay function. Then, it is weighted and summed together with the positive indexes, namely the motion waveform similarity score and the energy similarity score, to obtain the synergy matching degree.

8. The interactive business card exchange method based on AR smart glasses according to claim 7, characterized in that, Public information can be exchanged via low-power communication links, private information is exchanged after a point-to-point secure communication link is established, and information requiring active authorization for access is displayed after a point-to-point secure communication link is established and with the user's permission.

9. A fist-bump interactive business card exchange system based on AR smart glasses, used to implement the fist-bump interactive business card exchange method based on AR smart glasses as described in any one of claims 1-8, characterized in that, include: The information pre-setting module is used to create and store the user's digital business card information locally or in the cloud on AR smart glasses; The gesture sensing module is used to continuously capture images of the user's hands within their field of vision via the camera; The fist-bump trigger module is used to identify the fist-clenching posture and fist contact event in the hand image, and trigger the acquisition and exchange of motion sensor data; The collaborative verification module is used to calculate the degree of collaborative matching of the arm motion waveforms of both parties based on the characteristics of the exchanged motion sensing data, and to determine that the interaction is valid and trigger pairing when the degree of matching exceeds the threshold. The secure exchange module is used to establish a secure communication link between devices and to complete the mutual transmission of digital business card information; The AR prompt module is used to bind the other party's facial features and business card information after a successful exchange, and to overlay the corresponding business card in the AR field of view during subsequent recognition.

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