A health monitoring method, system and device based on intelligent interaction

Abstract

The application provides a health monitoring method, system and device based on intelligent interaction, the method comprises the following steps: sensing a gesture action of a user and matching the gesture action with a service request in a preset instruction library; encapsulating the matched service request, physiological data of the user at the current moment, location information and a current timestamp into a message; and sending the message to a target robot, so that the target robot executes corresponding instructions according to an analysis result of the message. The application can sense a gesture action of a user and match the gesture action with a preset instruction library, collect real-time physiological data and location information of the user, package the physiological data and the location information into a message and send the message to a target robot, so that service demands of the user are quickly and accurately converted into specific instructions, personalized health services are provided for the user, and user experience is improved. In addition, the user can conveniently call the robot without voice or key input, and the convenience of interaction is greatly improved.

Description

Technical Field

[0001] This specification relates to the field of interactive health monitoring system technology, and in particular to a health monitoring method, system and device based on intelligent interaction. Background Technology

[0002] As people's living standards improve and their health awareness increases, the public is paying more and more attention to their daily health management. However, most physiological health monitoring systems currently on the market have the following limitations: On the one hand, these systems can mostly only detect single indicators such as blood sugar and heart rate, making it difficult to comprehensively reflect the user's health status. On the other hand, their working mode is mostly passive detection, only triggering reminders or alarms when abnormal data is collected, thus failing to proactively respond to users' more personalized needs in prevention, intervention, and other aspects; moreover, these monitoring systems usually operate independently, failing to be linked to users' daily life, behavioral habits, and real-time physiological characteristic data, making it difficult to provide users with personalized intelligent services.

[0003] In view of this, the embodiments in this specification aim to provide a health monitoring method, system, and device based on intelligent interaction. Summary of the Invention

[0004] In view of the above-mentioned problems in the prior art, the purpose of the embodiments in this specification is to provide a health monitoring method, system and device based on intelligent interaction.

[0005] The specific technical solutions of the embodiments in this specification are as follows:

[0006] In a first aspect, embodiments of this specification provide a health monitoring method based on intelligent interaction, the method being executed by a smart wearable device, the method comprising:

[0007] It senses the user's gestures and matches them with service requests in a pre-defined command library;

[0008] The matched service request is encapsulated into a message along with the user's current physiological data, location information, and current timestamp.

[0009] The message is sent to the target robot so that the target robot executes the corresponding instructions based on the parsing result of the message.

[0010] Secondly, embodiments of this specification provide a health monitoring method based on intelligent interaction, the method being executed by a robot, the method comprising:

[0011] Receive messages sent by smart wearable devices and parse them to obtain service requests, the user's current physiological data, location information, and current timestamp;

[0012] Based on the analysis results, a personalized service strategy is generated;

[0013] The personalized service strategy is executed and the execution status is fed back to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

[0014] Thirdly, embodiments of this specification also provide another health monitoring method based on intelligent interaction, including:

[0015] Smart wearable devices sense the user's gestures and match them with service requests from a pre-set command library;

[0016] The smart wearable device encapsulates the matched service request with the user's current physiological data, location information, and current timestamp into a message and sends it to the target robot.

[0017] The target robot receives the message and parses it to obtain the service request, the user's current physiological data, location information, and current timestamp;

[0018] The target robot generates a personalized service strategy based on the type of service request and physiological data.

[0019] The target robot executes the personalized service strategy and feeds back the execution status to the smart wearable device;

[0020] The smart wearable device receives the execution status and generates tactile feedback based on the execution status.

[0021] Fourthly, embodiments of this specification also provide a health monitoring system based on intelligent interaction, including a smart wearable device and a robot;

[0022] The smart wearable device is used to perform the health monitoring method performed by the smart wearable device as described in the above technical solution; the robot is communicatively connected to the smart wearable device and performs the health monitoring method performed by the robot as described in the above technical solution.

[0023] Fifthly, embodiments of this specification also provide a health monitoring device based on intelligent interaction, comprising:

[0024] The sensing unit is used to sense the user's gestures and match them with service requests in a preset instruction library;

[0025] The message encapsulation unit is used to encapsulate the matched service request, along with the user's current physiological data, location information, and current timestamp, into a message.

[0026] The sending unit is used to send the message to the target robot so that the target robot can execute corresponding instructions based on the parsing result of the message.

[0027] Sixthly, embodiments of this specification also provide a health monitoring device based on intelligent interaction, comprising:

[0028] The receiving unit is used to receive messages sent by smart wearable devices and parse them to obtain service requests, the user's current physiological data, location information and current timestamp;

[0029] The generation unit is used to generate a personalized service strategy based on the type of the service request and physiological data.

[0030] An execution and feedback unit is used to execute the personalized service strategy and feed back the execution status to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

[0031] By employing the above technical solution, the health monitoring method, system, and device based on intelligent interaction provided in this specification can sense user gestures and match them with a preset command library. It can also collect real-time physiological data and location information of the user, package and generate messages, and send them to the target robot. This allows for the rapid and accurate conversion of user service needs into specific instructions, providing personalized health services and improving user experience. Furthermore, users can conveniently summon the robot without voice or typing, greatly enhancing the convenience of interaction.

[0032] To make the above and other objects, features and advantages of the embodiments of this specification more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0034] Figure 1 This specification illustrates a step diagram of a health monitoring method based on intelligent interaction provided in an embodiment of the present specification.

[0035] Figure 2 A schematic diagram illustrating the steps of sensing and matching user gestures is provided.

[0036] Figure 3 This diagram illustrates the steps involved in extracting motion features corresponding to hand gestures.

[0037] Figure 4a and Figure 4bThis illustration shows a scenario where a user makes a gesture while wearing a single smart wearable device.

[0038] Figure 5 A schematic diagram illustrating the steps for verifying a striking action based on an acceleration signal is shown.

[0039] Figure 6 This illustration shows a scenario where a user wears multiple smart wearable devices.

[0040] Figure 7 This diagram illustrates the steps involved in gesture recognition and service request matching when multiple smart wearable devices are being worn.

[0041] Figure 8 A schematic diagram illustrating the steps for determining the gesture triggering method based on the difference between the first vibration signal and the second vibration signal is shown.

[0042] Figure 9 This diagram illustrates the steps involved in sending a message to the target robot.

[0043] Figure 10 This diagram illustrates another step of a health monitoring method based on intelligent interaction provided in an embodiment of this specification.

[0044] Figure 11 This diagram illustrates another step of a health monitoring method based on intelligent interaction provided in an embodiment of this specification.

[0045] Figure 12 This specification shows a schematic diagram of the structure of a health monitoring system based on intelligent interaction provided in an embodiment;

[0046] Figure 13 A schematic diagram of the structure of a smart wearable device is shown;

[0047] Figure 14 The front view of the smart wearable device is shown;

[0048] Figure 15 A schematic diagram showing the setup of various components such as the physiological data acquisition module and the processor is provided.

[0049] Figure 16 A schematic diagram illustrating the communication between the processor and other components is shown.

[0050] Figure 17 A schematic diagram of the power supply module is shown;

[0051] Figure 18 This specification shows a schematic diagram of the structure of a health monitoring device based on intelligent interaction provided in an embodiment;

[0052] Figure 19This specification shows a schematic diagram of the structure of another health monitoring device based on intelligent interaction provided in the embodiments of this specification;

[0053] Figure 20 A schematic diagram of the structure of a computer device provided in an embodiment of this specification is shown.

[0054] Explanation of reference numerals in the attached figures:

[0055] 100. Smart wearable devices; 200. Robots;

[0056] 10. Inner casing; 11. Boss; 20. Outer casing; 30. Processor;

[0057] 40. Physiological data acquisition module; 41. Blood glucose detection unit; 42. Blood oxygen and heart rate detection unit; 43. Temperature detection unit;

[0058] 50. Vibration sensor;

[0059] 60. Power supply module; 61. Housing; 62. Battery cell; 63. End cap; 64. Electrode assembly;

[0060] 70. Communication module; 80. Inertial sensor; 90. Actuation module;

[0061] 1810. Sensing unit; 1820. Message encapsulation unit; 1830. Transmission unit;

[0062] 1910. Receiving unit; 1920. Generating unit; 1930. Execution and feedback unit;

[0063] 2002, Computer equipment; 2004, Central processing unit; 2006, Memory; 2008, Drive mechanism; 2010, Input / output module; 2012, Input device; 2014, Output device; 2016, Presentation device; 2018, Graphical user interface; 2020, Network interface; 2022, Communication link; 2024, Communication bus. Detailed Implementation

[0064] The technical solutions in the embodiments of this specification will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this specification, and not all embodiments. Based on the embodiments in this specification, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this specification.

[0065] It should be noted that the terms "first," "second," etc., used in this specification, claims, and the foregoing drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0066] To address the aforementioned issues, embodiments of this specification provide a health monitoring method, system, and device based on intelligent interaction, capable of recognizing user gesture commands and coordinating with a robot to execute personalized health services. Figure 1 This is a schematic diagram illustrating the steps of a health monitoring method based on intelligent interaction provided in the embodiments of this specification. This specification provides the operational steps of the method described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel. Specifically, as shown in the attached drawings... Figure 1 As shown, the method is executed by the processor of a smart wearable device, and the method may include:

[0067] S110: Sensing the user's gestures and matching them with service requests in the preset instruction library.

[0068] The user's gestures can be detected by a vibration sensor integrated into the smart wearable device, which is communicatively connected to the processor.

[0069] S120: Encapsulate the matched service request, along with the user's current physiological data, location signal, and current timestamp, into a message.

[0070] The user's physiological data can be collected through the physiological data acquisition module integrated into the smart wearable device, the location signal can be collected through the inertial measurement unit or global satellite navigation system integrated into the smart wearable device, and the current timestamp can be provided by the system clock of the smart wearable device.

[0071] S130: The message is sent to the target robot so that the target robot executes the corresponding instructions based on the parsing result of the message.

[0072] In the embodiments described in this specification, the smart wearable device also integrates a wireless communication module to establish a wireless communication connection with the target robot and transmit signals. The wireless communication module may include at least one of a Bluetooth module, a WiFi module, and a UWB ultra-wideband module. Preferably, the wireless communication module may be a UWB ultra-wideband module to achieve centimeter-level spatial positioning.

[0073] Furthermore, in the embodiments of this specification, a communication protocol can be pre-established between the smart wearable device and the target robot to transmit signals in a specific message format. The message includes a header and a message body, wherein the message body includes various data identifiers and corresponding data, including various physiological data identifiers and corresponding physiological data values ​​(e.g., blood glucose identifier and corresponding blood glucose value, heart rate identifier and corresponding heart rate value, body temperature identifier and corresponding body temperature value, etc.), location identifiers and corresponding location information, timestamp identifiers and corresponding timestamps, user ID identifiers and corresponding user IDs, and service request identifiers and corresponding service request types. In addition, it may also include a data quality identifier and a checksum. The data quality identifier is generated based on signal strength, motion artifact detection results, etc., and is used to indicate the reliability of the current data. The checksum can be used by the target robot to determine whether the received message is complete.

[0074] This specification provides an intelligent interactive health monitoring method that can sense user gestures and match them with a preset command library. It also collects real-time physiological data and location information, packages and generates messages, and sends them to a target robot. This quickly and accurately translates user service needs into specific service instructions. Users can conveniently summon the robot without voice or typing, improving the ease of interaction. Furthermore, the messages generated based on the user's real-time physiological state and gesture commands provide personalized health services, significantly enhancing the user experience.

[0075] Specifically, such as Figure 2 As shown in the embodiments of this specification, step S110: sensing the user's gesture and matching it with service requests in a preset command library may further include:

[0076] S210: Receives the vibration signal corresponding to the gesture and performs peak detection to obtain the time node and intensity corresponding to each peak.

[0077] S220: Extract the motion features corresponding to the gesture based on the time node and the intensity.

[0078] S230: Based on the action characteristics, match the gesture action with service requests in a preset instruction library.

[0079] Using the above method, motion characteristics are identified based on the time-domain features of vibration signals, and then the user's gestures are accurately mapped to service requests. The method is simple, convenient, and has a fast response speed, providing a reliable data foundation for the robot's subsequent precise services.

[0080] Specifically, such as Figure 3 As shown in the embodiment of this specification, step S220: extracting the motion features corresponding to the gesture based on the time node and the intensity may include the following steps:

[0081] S310: Determine whether the intensity of the peak value is greater than a preset intensity threshold.

[0082] That is, the intensity of each detected peak is judged sequentially.

[0083] S320: If not, ignore the peak value.

[0084] S330: If so, determine whether the time interval between two adjacent peaks is greater than the first time threshold.

[0085] S340: If not, then determine that the two adjacent peak values ​​correspond to a single tapping action.

[0086] For a single tapping action, the vibration sensor may detect not only the main peak of the vibration signal, but also secondary peaks caused by aftershocks or other clutter peaks caused by environmental noise. Failure to distinguish between these secondary peaks and other clutter peaks can lead to misjudgments of the number of taps. In this embodiment, a peak intensity threshold and a first time threshold (denoted as T1) are introduced as filtering conditions to exclude the influence of secondary peaks and other clutter peaks. For example, if a user taps once, and the vibration sensor detects a main peak and a small aftershock secondary peak, and the time interval between them is less than the first time threshold T1 and the intensity of the aftershock secondary peak is less than a preset intensity threshold, then the aftershock secondary peak will be determined not to correspond to the tapping action; that is, the main peak and the aftershock secondary peak correspond to only one tap.

[0087] S350: If so, determine whether the time interval between two adjacent peaks is less than or equal to the second time threshold.

[0088] S360: If so, then the two adjacent peak values ​​are determined to correspond to two tapping actions.

[0089] S370: If not, then determine that the two adjacent peak values ​​belong to two different service requests.

[0090] This specification also introduces a second time threshold (denoted as T2). When the time interval between two peaks is greater than the first time threshold T1 and less than or equal to the second time threshold T2, the two peaks are considered to correspond to two independent tapping actions in a single gesture. For example, the user taps twice consecutively (i.e., the user's gesture is a "double tap") or the user taps three times consecutively (i.e., the user's gesture is a "triple tap").

[0091] When the time interval is greater than the second time threshold T2, the two peaks are considered to belong to two different gesture actions, meaning the user made two service requests. For example, if the user first completes a "double-tap" gesture, pauses for a period of time (the pause time is greater than T2), and then performs a "triple-tap" gesture, the two sets of taps will be identified as two independent service requests, and will not be incorrectly merged into a single five-tap action.

[0092] The embodiments provided in this specification are as follows: Figure 3 The method steps shown can filter out invalid peaks caused by unconscious slight touches or environmental noise by setting an intensity threshold; it can also merge redundant peaks such as aftershocks from a single tap into a single valid tap by setting a first time threshold, thus avoiding false counting; and it can further distinguish between continuous taps and different gestures within the same gesture action by setting a second time threshold, thereby accurately matching single taps, double taps, triple taps and even more complex command patterns, effectively improving the accuracy and robustness of tapping gesture recognition.

[0093] Furthermore, this method enables users to interact with smart wearable devices in a natural and coherent rhythm. The method only involves time interval calculations and threshold comparisons, which has low computational load and low latency, thus saving computing resources and ensuring the efficiency and stability of system operation.

[0094] It should be noted that, taking a smart ring as an example, the tapping action in the embodiments of this specification can be the user tapping an external object (such as a tabletop, etc.) with a finger wearing a smart wearable device. Figure 4a (as shown), or the user taps the smart wearable device they are wearing with another finger (such as...). Figure 4b (as shown in the image) etc.

[0095] Specifically, in the embodiments of this specification, step S230: matching the gesture action with service requests in a preset instruction library based on the action characteristics may include:

[0096] When the gesture action extracted shows two valid taps, it is matched as a normal call service;

[0097] When the gesture action extracted contains three valid taps, it is matched as an emergency call service.

[0098] Furthermore, ordinary or emergency call services can be encapsulated together with physiological data, location information, and the current timestamp into a message and sent to the target robot. After parsing the message, the target robot will obtain the user's service request and then execute the corresponding instructions.

[0099] In some preferred embodiments, the method further includes:

[0100] Obtain the acceleration signal corresponding to the gesture, and verify the tapping action based on the acceleration signal.

[0101] Specifically, such as Figure 5 As shown, the steps may include the following:

[0102] S510: Calculate instantaneous change characteristics based on the acceleration signal, wherein the instantaneous change characteristics include at least the peak acceleration amplitude, the pulse width of the peak acceleration, and the rate of change of acceleration.

[0103] The acceleration signal can be acquired by an inertial sensor integrated into a smart wearable device.

[0104] S520: Determine whether the peak amplitude exceeds a preset amplitude threshold, whether the pulse width is less than a preset width threshold, and whether the acceleration change rate is greater than a preset change rate threshold.

[0105] Conscious tapping actions typically have high instantaneous energy, manifesting as a large instantaneous peak in the acceleration signal; while unconscious tapping actions have lower instantaneous energy. Furthermore, conscious tapping actions usually have extremely short acceleration pulse durations, appearing as a steeply rising, rapidly decaying peak on the acceleration waveform; unconscious tapping actions, on the other hand, have longer durations and more gradual acceleration changes. Therefore, the full width at half maximum (FWHM) of the peak can be calculated for this purpose. Additionally, the rate of change of acceleration, the first derivative of acceleration with respect to time, reflects the drastic change in the tapping force. Conscious tapping actions produce extremely high rates of change in acceleration; while unconscious tapping actions have a slow change in force over time and a smaller rate of change in acceleration. Therefore, these three criteria can be used to further verify whether a user's tapping action is intentional or unintentional.

[0106] S530: If so, the tapping action is deemed valid.

[0107] If the above three conditions are met, it corresponds to a valid tapping action, and the number of valid taps in the gesture is then mapped to the corresponding service request. Conversely, if the conditions are not met, the action is determined to be an unintentional touch, and therefore invalid.

[0108] In the embodiments of this specification, in addition to identifying the tapping action through the peak intensity and time interval between the vibration signals, further verification is performed using acceleration signals. This effectively distinguishes between intentional taps with high transient impact characteristics and unintentional touches with slow, low energy, significantly reducing false gesture commands caused by environmental interference or users' daily actions, thus improving system reliability and user experience. Furthermore, the verification method is simple and easy to implement, reducing the learning cost and operational burden for users; it also makes gesture interaction more natural and accurate, enhancing the naturalness and robustness of the interaction.

[0109] Furthermore, in the embodiments of this specification, multiple smart wearable devices may be provided. Taking a smart ring as an example, a user may wear a first smart ring on the index finger of their left hand and a second smart ring on the middle finger of their left hand; or wear a first smart ring on the index finger of their left hand and a second smart ring on the index finger of their right hand, etc. (e.g.) Figure 6 (As shown). Multiple smart wearable devices have built-in vibration sensors and inertial sensors and can interact via wireless communication modules. When wearing multiple smart wearable devices, one of them can be predefined as the master smart wearable device, and the others as auxiliary smart wearable devices.

[0110] Then as Figure 7 As shown in the embodiments of this specification, step S110: sensing the user's gesture and matching it with service requests in a preset command library, can be executed by the main smart wearable device and may include the following method steps:

[0111] S710: Receives the first vibration signal collected by its own vibration sensor and the second vibration signal sent by the auxiliary smart wearable device.

[0112] The main smart wearable device can be worn on any finger of the user, while the auxiliary smart wearable device can be worn on different fingers of the same hand or on fingers of another hand. The second vibration signal is collected by the vibration sensor of the auxiliary smart wearable device and transmitted to the main smart wearable device through a wireless communication module.

[0113] S720: Determine the triggering method of the gesture action based on the time difference and intensity difference between the first vibration signal and the second vibration signal.

[0114] When a user taps an external object with a finger, the mechanical vibration generated by this tapping action is detected and received by both the primary and secondary smart wearable devices via bone conduction. Because the bone conduction distance between the tapping point and the two smart wearable devices differs, the arrival time of the vibration signal on each device varies (i.e., the closer to the tapping point, the earlier the signal arrives). Simultaneously, the energy of the vibration signal attenuates with distance during bone conduction (i.e., the closer to the tapping point, the stronger the received signal, such as the peak amplitude). Therefore, based on these physical principles, the triggering method for the tapping action on the two smart wearable devices can be determined by the time difference of arrival (TDOA) and the intensity difference between the vibration signals.

[0115] S730: Match the gesture action with the service request in the preset instruction library according to the triggering method.

[0116] The method provided in the embodiments of this specification can also utilize the difference between the first vibration signal and the second vibration signal to recognize the triggering mode of the user's gesture action, which is beneficial for enriching application scenarios and expanding the instruction library. In addition, the collaboration of multiple smart wearable devices can also be used to eliminate misjudgments caused by the positional shift or posture change of a single device, and enhance the recognition ability of complex gestures such as finger combinations, tapping order, and gradual changes in force.

[0117] Similar to the method for extracting motion features when wearing only a single smart wearable device, when a user wears multiple smart wearable devices, the main smart wearable device can also extract motion features corresponding to gestures based on the first vibration signal and the second vibration signal.

[0118] Specifically, the main smart wearable device performs peak detection on the first vibration signal and the second vibration signal to obtain the time node and intensity corresponding to each peak. When the intensity of a certain peak (regardless of which smart wearable device it comes from) is less than the preset intensity threshold, it is determined that the peak corresponds to environmental clutter or unintentional touch and is ignored to avoid interfering with the recognition and positioning of effective tapping actions.

[0119] When the time interval between a peak in the first vibration signal and a peak in the second vibration signal is less than a first time threshold, these two peaks are determined to be caused by a single tapping action (i.e., a single tapping action by the user is received sequentially by two smart wearable devices), rather than two tapping actions. This is because differences in bone conduction paths can cause a slight time difference when the vibration signal of the same tapping action reaches different devices, but this time difference is much smaller than the minimum interval between two consecutive taps by the user. Conversely, if only one of the two devices detects a peak, or if the time interval between two adjacent peaks is too large, the two peaks are determined to correspond to different tapping actions.

[0120] Specifically, such as Figure 8 As shown in the embodiment of this specification, step S720: determining the triggering method of the gesture action based on the time difference and intensity difference between the first vibration signal and the second vibration signal may further include:

[0121] S810: Determine whether the time difference is less than a preset third time threshold and whether the intensity difference is less than a preset intensity threshold.

[0122] S820: If so, then the triggering method is determined to be that the finger or hand on which the user is located and the finger or hand on which the auxiliary smart wearable device is located are triggered simultaneously.

[0123] This refers to the simultaneous tapping of fingers or hands wearing both the primary and secondary smart wearable devices. For example, a user might wear the primary and secondary smart wearable devices on their right index and middle fingers respectively, and extend and tap an external object with both fingers simultaneously. Another example is a user wearing the primary and secondary smart wearable devices on their right and left index fingers respectively, and clapping their hands together to make the primary and secondary smart wearable devices collide.

[0124] S830: If not, determine whether the time difference and the intensity difference are both greater than zero.

[0125] S840: If so, then the triggering method is determined to be triggered by the finger or hand on which the user is located.

[0126] For example, a user wears a main smart wearable device and a secondary smart wearable device on the index finger of their right hand and left hand, respectively. When the time corresponding to the first vibration signal is earlier than the time of the second vibration signal (the second vibration signal can carry the timestamp of the second vibration signal when it is sent from the secondary smart wearable device to the main smart wearable device), and the intensity of the first vibration signal is greater than the intensity of the second vibration signal, it can be determined that the tapping action was made by the user's right hand.

[0127] S850: If not, then the triggering method is determined to be triggered by the finger or hand where the smart wearable device is located.

[0128] Continuing with the previous example, if the timing of the first vibration signal is later than that of the second vibration signal, and the intensity of the first vibration signal is less than that of the second vibration signal, it can be determined that the tapping action was initiated solely by the user's left hand.

[0129] Based on this, matching the gesture actions with service requests in a preset command library can expand the command library for gesture actions. For example, when the gesture action is two smart wearable devices tapping simultaneously, a confirmation command can be matched; when the gesture action is two smart wearable devices tapping once in sequence, it can be matched as pausing or canceling the previous / current service request, etc. It should be noted that the above gesture actions and corresponding service commands are only exemplary and can be set according to actual application needs.

[0130] The above method can identify the gestures performed by a user when wearing multiple smart wearable devices and accurately map them into corresponding service requests. Then, the matched service requests are packaged with the user's current physiological data, location information, and current timestamp to generate a message, which is then sent to the target robot via wireless communication. The target robot then parses the message and performs the corresponding service operation.

[0131] This method can fully utilize the spatial resolution capabilities brought about by the collaboration of multiple smart wearable devices, expanding the gesture command library from simple single-finger actions to rich modes of multi-finger and multi-hand combinations. At the same time, by combining the time difference and intensity difference of vibration signals to determine the tapping position and method, it can effectively avoid misjudgment, which is conducive to improving the naturalness and reliability of user-target robot interaction, and facilitates the target robot's rapid and accurate response to user intentions, thereby enhancing the proactive service level of intelligent health monitoring.

[0132] It should be noted that in the embodiments of this specification, the smart wearable device can passively execute the user's health monitoring commands to collect the user's physiological data. For example, when the user makes a specific gesture (e.g., a click action corresponds to "wake up and collect physiological data once"), the smart wearable device works and collects physiological data. This passive working mode allows the smart wearable device to be in a low-power standby state for most of daily life, which can significantly reduce power consumption and thus extend the device's battery life. In addition, the smart wearable device can also operate actively, that is, the smart wearable device can collect physiological data at regular intervals or according to a preset collection strategy (such as automatic measurement every 30 minutes) without user intervention.

[0133] The two working modes can be dynamically switched or complemented according to actual application needs. For example, in daily use, the passive working mode is mainly used to reduce power consumption; while during high-demand periods such as health monitoring, sports monitoring, or sleep analysis, the active working mode is activated to ensure the timeliness and completeness of physiological data. By setting different working modes, the low power consumption and battery life requirements of smart wearable devices can be balanced with the timeliness and continuity requirements of health monitoring data.

[0134] Furthermore, such as Figure 9 As shown in the embodiments of this specification, sending the message to the target robot in step S130 may further include:

[0135] S910: Determine whether the message to be sent is the first communication message.

[0136] S920: If so, obtain the pre-agreed key to encrypt the current message to be sent, and send the encrypted message to the target robot. The current message to be sent contains the collected physiological data.

[0137] In some feasible embodiments, the message can be encrypted using symmetric encryption, for example, the smart wearable device can encrypt the message using a pre-agreed ASE key; the target robot can then decrypt the message using the same key after receiving it. Alternatively, the message can be encrypted using asymmetric encryption, for example, the smart wearable device can encrypt the message using a pre-agreed public key; the target robot can then decrypt the message using the corresponding private key after receiving it. In the embodiments described in this specification, the encryption method for the initial communication message is not specifically limited.

[0138] S930: If not, obtain the physiological data from the previous communication message as the encryption key to encrypt the current message to be sent, and send the encrypted current message to the target robot.

[0139] For example, the heart rate variability sequence from physiological data can be used as an encryption key to encrypt the message. In the embodiments of this specification, using the user's physiological data (e.g., heart rate variability sequence) as a key to encrypt the message ensures the real-time dynamics and individual uniqueness of the key, effectively resisting replay attacks and forged device access, and greatly improving the security of information transmission. For the first communication, since the robot has not yet obtained any physiological data from the user, it cannot directly decrypt using the physiological key. Therefore, an initial handshake is completed using a pre-agreed symmetric key or asymmetric public key to securely transmit the first communication message and use the physiological data therein as the seed for subsequent chain encryption.

[0140] In summary, the above encryption strategy combines the unique physiological characteristics of each user with dynamic key derivation, avoiding the overhead of frequent key negotiation, achieving an extremely high level of security with "one-time pad", and implicitly verifying the legitimacy of the user's identity. This provides a lightweight, highly reliable, and highly secure data protection solution for long-term interaction between low-power smart wearable devices and robots.

[0141] Furthermore, once the target robot receives the message (and successfully parses it), it can send an ACK (acknowledgment response) to the smart wearable device. If the smart wearable device does not receive an ACK from the target robot within a preset time period after sending the message, it can be determined that the message to be sent may have been lost, corrupted, or the system may have restarted, requiring retransmission. In this case, the chain communication between the target robot and the smart wearable device is interrupted, and therefore, it will revert to encrypting the message to be sent using a pre-defined key before retransmission. In this way, the smart wearable device and the target robot can still maintain communication, and new physiological data can be carried in the message to restore the chain encryption.

[0142] Furthermore, in the embodiments of this specification, the method further includes:

[0143] The system receives the execution status of the service request from the target robot and generates tactile feedback based on the execution status.

[0144] For example, when a robot receives a message and executes the corresponding service request, it can send a "received, executing" status message to the smart wearable device. The smart wearable device then generates a short vibration or a specific tactile feedback pattern to let the user know that their service request has been successfully delivered and is being processed. If the robot cannot execute for any reason (such as insufficient resources or invalid instructions), it can send an "execution failed" status message to the smart wearable device. The smart wearable device can then inform the user through different tactile feedback patterns (such as two long vibrations) so that the user can resend the service request or modify it. In this way, it can effectively prevent users from repeatedly sending the same request due to uncertainty about whether their service instructions have been received, which helps reduce confusion caused by communication congestion, system load, and repeated execution of instructions, and at the same time improves the user's interactive experience.

[0145] In some feasible embodiments, the robot's execution status feedback can correspond to different tactile feedback methods for different service requests. For example, when the target robot is performing a normal call service, the smart wearable device can generate tactile feedback of two consecutive vibrations based on its execution status; when the target robot is performing an emergency call service and sends a confirmation signal, the smart wearable device can generate tactile feedback of three consecutive vibrations based on its execution status.

[0146] In some feasible embodiments, there can be multiple target robots. When multiple target robots receive messages sent by the smart wearable device, one or more target robots closest to the user's current location can respond and execute corresponding instructions based on the location information in the message parsing result.

[0147] Furthermore, in the embodiments of this specification, the method may further include:

[0148] Receive an authentication request from the target robot and perform user authentication based on gesture features and / or physiological features.

[0149] The gesture features can be acquired by an inertial sensor and may include at least one of the following: tapping pattern, air gesture trajectory, etc. The tapping pattern may include the rhythm pattern, intensity sequence, and time interval distribution of the tapping action. For example, a double tap may be performed with a heavier first tap and a lighter second tap, and a triple tap may be performed with the first two taps completed at a faster and more even rhythm, followed by a slight pause before the third tap.

[0150] The physiological characteristics can be acquired by the physiological data acquisition module and may include at least one of the following biometric fingerprint information: heart rate variability sequence, blood oxygen waveform characteristics, pulse wave morphology, etc.

[0151] In the embodiments described in this specification, the user's unique gesture habits and physiological characteristics, which are difficult for others to imitate or steal in a short time, are used for identity verification. This can effectively prevent impersonation attacks and unauthorized use of devices, and the identity verification is convenient and seamless. At the same time, the verification process between the smart wearable device and the robot does not rely on external networks or third-party services. It can respond quickly while protecting user privacy, and is particularly suitable for local closed-loop authentication scenarios.

[0152] Furthermore, the method performed by the smart wearable device may also include:

[0153] Compare the user's current physiological data with the pre-stored physiological data from previous historical moments;

[0154] When the comparison results indicate that a user is abnormal, the user's current physiological data and location information will be sent to the emergency contact and / or an emergency call will be sent to the emergency contact.

[0155] Emergency contacts include, but are not limited to, family members, friends, doctors, and nurses. When a user experiences an abnormality, they may be unable to call for help independently. Therefore, abnormal physiological data and the user's location information can be sent to emergency contacts, who can then call for help or provide on-site assistance based on the received information. This can solve the problem of delayed rescue due to the user's impaired consciousness, limited mobility, or inability to use communication devices. In some specific embodiments, the smart wearable device can also initiate calls to emergency contacts sequentially according to a preset priority order until a confirmation response is received.

[0156] The health monitoring method based on intelligent interaction provided in this specification uses the fusion of vibration and inertial sensors to recognize user gestures. Users can summon the robot without using voice or touching the screen, making it particularly suitable for emergency situations or scenarios where speaking is inconvenient. Furthermore, the efficient data interaction established between the smart wearable device and the service robot enables the robot to provide personalized services based on the user's real-time physiological state and gesture commands, achieving a service upgrade from "passive response" to "proactive care" in health monitoring.

[0157] like Figure 10 As shown in the embodiments of this specification, a health monitoring method based on intelligent interaction is also provided. The method is executed by a robot and includes:

[0158] S1010: Receives messages sent by smart wearable devices and parses them to obtain service requests, the user's current physiological data, location information, and current timestamp.

[0159] S1020: Generate a personalized service strategy based on the type of the service request and physiological data.

[0160] S1030: Execute the personalized service strategy and feed back the execution status to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

[0161] The method provided in the embodiments of this specification can generate personalized service strategies based on the parsing results (i.e., the type of user service request and real-time physiological data), enabling it to provide users with suitable health services. Simultaneously, the robot will provide real-time feedback on its execution status to the smart wearable device, allowing users to perceive through touch that their service instructions have been received and processed, effectively alleviating user anxiety. This constructs an intelligent service process encompassing service request reception, strategy formulation and execution, and closed-loop feedback, improving the accuracy of the robot's response and the comfort of the user's interactive experience.

[0162] Specifically, in some feasible embodiments, step S1020: generating a personalized service strategy based on the type of the service request and physiological data may include:

[0163] If the type of the service request is a normal call service, then obtain the user's physiological data at historical moments;

[0164] Compare the physiological data from the historical moments with the physiological data from the current moment;

[0165] The target item is determined based on the comparison results, and the target item is moved to the target location corresponding to the location information.

[0166] Furthermore, in the embodiments of this specification, determining the target item based on the comparison results may include:

[0167] If the blood glucose level in the current physiological data is lower than the preset first blood glucose threshold, the target item is determined to be a sugary beverage and / or food.

[0168] In other words, when a user's current blood sugar level is lower than a preset hypoglycemic threshold, the robot determines that the user may be experiencing hypoglycemia symptoms. At this time, the robot prioritizes bringing sugary drinks (such as juice or sugar water) and sugary foods (such as candy or cookies) to the user's location to quickly raise the user's blood sugar and prevent confusion or fainting.

[0169] If the blood glucose level in the current physiological data is higher than the preset second blood glucose threshold, the target items are identified as hypoglycemic drugs and drinking water. The second blood glucose threshold is higher than the first blood glucose threshold. Both the first and second blood glucose thresholds are determined from the blood glucose data in the historical physiological data.

[0170] In other words, when a user's current blood glucose level is higher than a preset hyperglycemic threshold, the robot determines that the user may be in a hyperglycemic state. At this time, to avoid the risks of dehydration or ketoacidosis caused by continuously rising blood glucose, the robot determines the target items as hypoglycemic medication and drinking water. The hypoglycemic medication is used to help the user control their blood glucose levels according to medical advice, while drinking water makes it easier to take the medication and can also be used to replenish fluid loss that may be caused by elevated blood glucose, promote drug absorption, and dilute blood glucose.

[0171] And if the heart rate in the current physiological data is higher than the preset heart rate threshold and the body temperature is within the preset normal body temperature range, the target item is determined to be drinking water. The heart rate threshold is determined by the heart rate data in the physiological data at historical moments.

[0172] That is, when a user's current heart rate is higher than a preset heart rate threshold and their body temperature is within the normal range (36.0℃~37.5℃), the robot can rule out fever as the cause of the increased heart rate and determine that it may be due to emotional stress, mild dehydration, or failure of the heart rate to recover after exercise. At this time, the robot will bring drinking water to help the user alleviate the problem of an excessively fast heart rate.

[0173] The method provided in the embodiments of this specification can determine the required items based on the user's current real-time physiological data, so that the robot's service behavior can match the user's current physical condition, significantly enhancing the practical value of health monitoring and user experience.

[0174] Furthermore, in some feasible embodiments, step S1020: generating a personalized service strategy based on the type of the service request and physiological data may further include:

[0175] If the service request is an emergency call service, then move to the target location based on the current location information and activate the emergency plan.

[0176] Specifically, in the embodiments of this specification, the emergency plan includes at least one of the following operations:

[0177] When an abnormal heart rate is determined based on current and historical physiological data, or when the user remains stationary for an extended period, the abnormal data is sent to the emergency contact and a medical call is triggered.

[0178] When fall characteristics are detected, the fall emergency procedure is activated.

[0179] In some feasible embodiments, physiological data may also include posture data collected by inertial sensors, as well as visual recognition modules such as cameras integrated into the robot itself. This allows the robot to determine if the user has fallen, and then provide assistance, deliver items (such as ice packs, bandages, cushions, etc.), and make emergency calls. Emergency contacts include, but are not limited to, family members, friends, doctors, and nurses. When a user experiences an abnormality, they may not be able to call for help independently; therefore, the abnormality data can be sent to emergency contacts, who can then call for help or provide on-site assistance based on the received information. Simultaneously, while waiting for professional rescue, the smart wearable device continuously monitors the user's physiological data. The robot can remain near the user and maintain communication with the connected emergency contacts, enabling them to remotely assess the user's condition, provide on-site guidance, and perform other initial treatments, thus improving the reliability of the emergency response and the timeliness of the rescue.

[0180] In the embodiments described in this specification, when a regular service call is received, the robot not only responds to the user's location but also proactively integrates current and historical physiological data to autonomously decide which items to carry, providing more accurate and personalized health services. When an emergency call is detected, the robot directly proceeds to the user's location and simultaneously initiates emergency procedures such as medical calls or fall prevention, which helps to buy valuable time for medical rescue. This method can significantly improve the practicality, timeliness, and humanized service level of service robots in health monitoring scenarios.

[0181] Furthermore, when the service request is a regular call, and the user's physiological data at the current moment is determined to be normal based on historical data, the user can be directly moved to the target location using their current location information. In this case, open-ended questions can be used to inquire about the user's specific needs (e.g., "How can I help you?") to avoid disturbing them due to excessive intervention.

[0182] Furthermore, in some preferred embodiments, the target item can be determined by combining the user's historical daily routine data (such as meal times, exercise times, medication times, etc.). For example, the robot can determine whether the current time is within the medication window; if so, it will bring the medicine and drinking water and remind the user that "it is time to take the medication"; if not (e.g., the interval since the last medication is too short), it will not provide the medicine and will output a reminder that "do not take the medication repeatedly" to avoid the risk of overdose.

[0183] like Figure 11 As shown in the embodiments of this specification, a health monitoring method based on intelligent interaction is also provided. This method is applicable to a health monitoring system composed of a smart wearable device and a target robot, and includes the following steps:

[0184] S1110: The smart wearable device senses the user's gestures and matches them with service requests in the preset instruction library;

[0185] S1120: The smart wearable device encapsulates the matched service request with the user's current physiological data, location information and current timestamp into a message and sends it to the target robot;

[0186] S1130: The target robot receives the message and parses it to obtain the service request, the user's current physiological data, location information and current timestamp;

[0187] S1140: The target robot generates a personalized service strategy based on the analysis results;

[0188] S1150: The target robot executes the personalized service strategy and feeds back the execution status to the smart wearable device;

[0189] S1160: The smart wearable device receives the execution state and generates tactile feedback based on the execution state.

[0190] This instruction manual implements, for example Figure 11 The method shown achieves the same or similar technical effects as the health monitoring methods executed by smart wearable devices and robots, and will not be elaborated further here.

[0191] like Figure 12As shown, corresponding to the above-described health monitoring method based on intelligent interaction, this specification also provides a health monitoring system based on intelligent interaction. The system includes a smart wearable device 100 and a robot 200;

[0192] The smart wearable device 100 is used to perform, for example... Figures 1 to 3 , Figure 5 , Figures 7 to 9 The method shown; the robot 200 is communicatively connected to the smart wearable device 100, and the robot 200 is used to perform, for example... Figure 10 The method shown.

[0193] It should be noted that, in the embodiments of this specification, the robot can be any of the legged / footed, wheeled, or tracked robots, so as to be able to move to the target location of the user based on the current location information in the message.

[0194] In the embodiments of this specification, the smart wearable device 100 is specifically a smart ring and one or more can be provided. When there is one smart wearable device 100, the smart wearable device 100 can be worn on any finger of the user; when there are multiple smart wearable devices 100, the smart wearable device 100 can be worn on different fingers of the same hand or different hands of the user.

[0195] like Figures 13 to 17 As shown, the smart wearable device 100 includes an inner shell 10, an outer shell 20, a processor 30, a physiological data acquisition module 40, a vibration sensor 50, a power supply module 60, and a communication module 70.

[0196] There is a gap between the inner shell 10 and the outer shell 20 to form a receiving cavity; the processor 30, the physiological data acquisition module 40, the vibration sensor 50, the power supply module 60 and the communication module 70 are all disposed in the receiving cavity.

[0197] The physiological data acquisition module 40, the vibration sensor 50, the power supply module 60, and the communication module 70 are all electrically connected to the processor 30; the physiological data acquisition module 40 is used to collect the user's physiological data and feed it back to the processor 30; the vibration sensor 50 is used to sense the user's gestures and feed them back to the processor 30.

[0198] The processor 30 is used to match service requests based on the gesture and to generate messages based on the matched service requests and the physiological data.

[0199] The communication module 70 is used to communicate with the robot 200 to send the message to the robot 200 and to communicate with other smart wearable devices 100; in some other embodiments, the communication module 70 can also communicate with external devices, for example, to send abnormal physiological data and location information of the user to emergency contacts and to initiate emergency calls to emergency contacts.

[0200] The power supply module 60 is used to supply power to the processor 30, the physiological data acquisition module 40, the vibration sensor 50 and the communication module 70.

[0201] Specifically, the processor 30, the physiological data acquisition module 40, the vibration sensor 50, the power supply module 60, and the communication module 70 are integrated on a flexible circuit board, which is flexible enough to bend and adapt to the annular cavity of the smart wearable device.

[0202] Furthermore, such as Figure 16 As shown, the physiological data acquisition module 40 includes a blood glucose detection unit 41, a blood oxygen and heart rate detection unit 42, and a temperature detection unit 43;

[0203] The blood glucose detection unit 41 can be a Raman spectroscopy blood glucose detection unit, which includes a laser source, a miniature spectrometer, and a detector array.

[0204] The laser source can be an 830nm VCSEL (Vertical-Cavity Surface-Emitting Laser) laser source, which can be packaged in a TO-CAN (Transistor Outline Can) package with a diameter of ≤5.6mm, an output power in the range of 50-500mW, power stability σ / μ<±0.2%, and a spectral width <0.1nm. The miniature spectrometer is a chip-level silicon nitride Raman spectrometer, which improves the Raman light signal acquisition throughput based on a multi-aperture coupling strategy. The detector array is an InGaAs detector array, which is a 256 or 512 pixel cooled type, with a response band of 0.9-1.7μm and a signal-to-noise ratio >3000:1.

[0205] The blood oxygen and heart rate detection unit 42 includes a 660nm red LED, a 940nm infrared LED, and a photodetector. The 660nm red LED emits red light of a specific wavelength to distinguish deoxygenated hemoglobin, a core variable in blood oxygen calculation along with heart rate. The 940nm infrared LED emits infrared light of another specific wavelength to distinguish oxyhemoglobin; the two wavelengths work together to detect the user's blood oxygen saturation. The photodetector detects the remaining light intensity signal after the infrared light is absorbed and scattered by human tissue, and then calculates the user's heart rate based on the periodic fluctuations of the light intensity signal.

[0206] The temperature detection unit 43 includes a miniature temperature sensor for detecting the user's body temperature.

[0207] By integrating a Raman spectroscopy blood glucose detection unit, a photoplethysmography blood oxygen and heart rate detection unit, and a temperature detection unit, the smart wearable device provided in this specification can achieve comprehensive and non-invasive monitoring of key physiological parameters of the user's human body, overcoming the shortcomings of existing single data and single monitoring functions.

[0208] Preferably, such as Figure 15 and Figure 16 As shown, the smart wearable device 100 also includes an inertial sensor 80, which is used to collect acceleration signals corresponding to user gestures and feed them back to the processor 30. The processor 30 can extract and verify the tapping action from the gesture based on the acceleration signal.

[0209] The smart wearable device 100 also includes an actuation module 90, which generates tactile feedback under the control of the processor 30. For example, after receiving the execution status from the robot, the processor 30 controls the actuation module 90 to vibrate so that the user is aware of the delivery and / or execution status of their service request.

[0210] In some feasible embodiments, the actuation module 90 can be a piezoelectric ceramic, which can generate vibration through the inverse piezoelectric effect and can be made into a thin film, making it easier to install in the receiving cavity. The actuation module 90 can also be other structural components, such as a vibration motor.

[0211] In other possible embodiments, the vibration sensor 50 can be a piezoelectric ceramic sensor, which can generate an electrical signal (i.e., a vibration signal) due to deformation when struck by a user, and can also generate vibration when a voltage is applied. That is, it can serve as both a vibration sensor and an actuation module, thereby helping to reduce the number of parts and simplify the structure.

[0212] Furthermore, the smart wearable device 100 may also include a memory, which can be used to store physiological data collected by the physiological data acquisition module 40, vibration signals corresponding to user gestures collected by the vibration sensor 50, acceleration signals corresponding to user gestures collected by the inertial sensor 80, generated messages, a pre-established instruction library, and a pre-defined encryption key for encrypting the initial communication message. During chain encryption, physiological data from the previous communication message stored in the memory is used as the encryption key.

[0213] Preferably, such as Figure 13 and Figure 14 As shown in the embodiments of this specification, the smart wearable device 100 is generally annular. That is, both the inner shell 10 and the outer shell 20 are annular, the inner shell 10 and the outer shell 20 are connected to the outer shell of the smart wearable device 100, and the receiving cavity is also annular.

[0214] The inner housing 10 is provided with a boss 11, which faces the central through hole of the smart wearable device 100. The surface of the boss 11 is a smooth plane to contact the user's finger skin.

[0215] Preferably, the physiological data acquisition module 40 is disposed within the receiving cavity and close to the protrusion 11, and the protrusion 11 is provided with a window 12. This allows the physiological data acquisition module 40 to perform physiological detection on the user through the window 12. Furthermore, because the protrusion 11 has a centrally oriented opening, it fits snugly against the finger when worn, improving the accuracy of data acquisition and detection by the physiological data acquisition module 40 and reducing the likelihood of measurement results being affected by gaps between the finger skin and the physiological data acquisition module 40. It is also more stable during wear, less prone to deflection or loosening.

[0216] Furthermore, when wearing the smart wearable device 100 in the embodiments of this specification, it is preferable to attach the protrusion 11 to one side of the fingertip. The fingertip has abundant subcutaneous blood vessels and a relatively thin stratum corneum, which is beneficial to improving the stability and reliability of the acquisition of raw data such as heart rate and blood oxygen. Moreover, the fingertip is more likely to form a close contact with the smart wearable device, which can improve the user experience and detection success rate.

[0217] Preferably, the power supply module 60 is generally arc-shaped (e.g., Figure 15 As shown in the diagram, one end of the power supply module 60 is connected to the flexible circuit board, and the two together form a roughly circular shape. This facilitates the installation of the power supply module 60; at the same time, it makes full use of the space of the receiving cavity, increasing the overall volume of the power supply module 60, thereby extending the battery life of the smart wearable device.

[0218] Furthermore, such as Figure 17As shown, the power supply module 60 includes an arc-shaped outer shell 61 and a battery cell 62 disposed within the arc-shaped outer shell 61. An electrode assembly 64 electrically connected to the battery cell 62 is disposed on an end cap 63 at one end of the arc-shaped outer shell 61. In some specific embodiments, the end cap 63 can be connected to the arc-shaped outer shell 61 by laser welding.

[0219] Based on the intelligent interactive health monitoring method provided in the above embodiments, this specification also provides an intelligent interactive health monitoring device. The device may include a system (including a distributed system), software (application), module, component, server, client, etc., using the method described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the devices in one or more embodiments provided in this specification are as described in the following embodiments. Since the implementation schemes and methods for solving the problem by the devices are similar, the implementation of specific devices in the embodiments of this specification can refer to the implementation of the aforementioned method, and repeated details will not be repeated. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0220] like Figure 18 As shown, the health monitoring device based on intelligent interaction includes:

[0221] The sensing unit 1810 is used to sense the user's gestures and match them with service requests in a preset instruction library;

[0222] The message encapsulation unit 1820 is used to encapsulate the matched service request, the user's current physiological data, location information and current timestamp into a message;

[0223] The sending unit 1830 is used to send the message to the target robot so that the target robot can execute corresponding instructions based on the parsing result of the message.

[0224] like Figure 19 As shown in the embodiments of this specification, another health monitoring device based on intelligent interaction is also provided, including:

[0225] The receiving unit 1910 is used to receive messages sent by the smart wearable device and parse them to obtain service requests, the user's current physiological data, location information and current timestamp;

[0226] The generation unit 1920 is used to generate a personalized service strategy based on the type of the service request and physiological data.

[0227] The execution and feedback unit 1930 is used to execute the personalized service strategy and feed back the execution status to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

[0228] The beneficial effects obtained by the apparatus provided in the embodiments of this specification are consistent with the beneficial effects obtained by the methods described above, and will not be repeated here.

[0229] like Figure 20 The illustration shows a computer device provided in an embodiment of this specification. The health monitoring device based on intelligent interaction in this specification can be the computer device in this embodiment, executing the methods described above. The computer device 2002 may include one or more central processing units 2004, each of which can implement one or more hardware threads. The computer device 2002 may also include any memory 2006 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, the memory 2006 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Further, any memory can provide volatile or non-volatile retention of information. Further, any memory can represent a fixed or removable component of the computer device 2002. In one case, when the central processing unit 2004 executes associated instructions stored in any memory or combination of memories, the computer device 2002 can perform any operation of the associated instructions. The computer device 2002 also includes one or more drive mechanisms 2008 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0230] Computer device 2002 may also include an input / output module 2010 (I / O) for receiving various inputs (via input device 2012) and providing various outputs (via output device 2014). A specific output mechanism may include a presentation device 2016 and an associated graphical user interface (GUI) 2018. In other embodiments, the input / output module 2010 (I / O), input device 2012, and output device 2014 may be omitted, and the device may function solely as a computer device within a network. Computer device 2002 may also include one or more network interfaces 2020 for exchanging data with other devices via one or more communication links 2022. One or more communication buses 2024 couple the components described above together.

[0231] Communication links 2022 can be implemented in any way, such as via a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication links 2022 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0232] Corresponding to, for example Figures 1 to 3 , Figure 5 , Figures 7 to 11 In addition to the method shown, embodiments of this specification also provide a computer-readable storage medium storing a computer program that is executed by a central processing unit to perform the steps of the above-described method.

[0233] This specification also provides computer-readable instructions, wherein when a central processing unit executes the instructions, the program therein causes the central processing unit to perform the following: Figures 1 to 3 , Figure 5 , Figures 7 to 11 The method.

[0234] This specification also provides a computer program product, including at least one instruction or at least one program segment, wherein the at least one instruction or the at least one program segment is loaded and executed by a central processing unit to achieve the following: Figures 1 to 3 , Figure 5 , Figures 7 to 11 The method.

[0235] It should be understood that in the various embodiments of this specification, the sequence number of each process 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 specification.

[0236] It should also be understood that, in the embodiments of this specification, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this specification generally indicates that the preceding and following related objects have an "or" relationship.

[0237] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this specification can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described in terms of functionality in the foregoing description. 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 specification.

[0238] 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.

[0239] In the several embodiments provided in this specification, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0240] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described in this specification, depending on actual needs.

[0241] Furthermore, the functional units in the various embodiments of this specification can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0242] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this specification, in essence, or the part that contributes to the prior art, or all or part of the technical solution, 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 a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this specification. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0243] This specification uses specific embodiments to illustrate the principles and implementation methods of this specification. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this specification. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this specification. Therefore, the content of this specification should not be construed as a limitation of this specification.

Claims

1. A health monitoring method based on intelligent interaction, characterized in that, The method is performed by a smart wearable device, and the method includes: It senses the user's gestures and matches them with service requests in a pre-defined command library; The matched service request is encapsulated into a message along with the user's current physiological data, location information, and current timestamp. The message is sent to the target robot so that the target robot executes the corresponding instructions based on the parsing result of the message.

2. The method according to claim 1, characterized in that, Sensing user gestures and matching them with service requests in a pre-defined command library, including: Receive vibration signals corresponding to hand gestures and perform peak detection to obtain the time node and intensity corresponding to each peak; Based on the time point and the intensity, extract the motion features corresponding to the gesture; Based on the action characteristics, the gesture action is matched with service requests in a preset instruction library.

3. The method according to claim 2, characterized in that, Based on the time point and the intensity, the motion features corresponding to the gesture are extracted, further including: Determine whether the intensity of the peak value is greater than a preset intensity threshold; If not, then ignore the peak value; If so, determine whether the time interval between two adjacent peaks is greater than the first time threshold; If not, then the two adjacent peak values ​​are determined to correspond to one tapping action; If so, determine whether the time interval between two adjacent peaks is less than or equal to the second time threshold; If so, then the two adjacent peak values ​​are determined to correspond to two tapping actions; If not, then the two adjacent peak values ​​are determined to belong to two different service requests.

4. The method according to claim 3, characterized in that, The method further includes: Obtain the acceleration signal corresponding to the gesture, and verify the tapping action based on the acceleration signal.

5. The method according to claim 4, characterized in that, Verifying the striking action based on the acceleration signal further includes: The instantaneous change characteristics are calculated based on the acceleration signal, and the instantaneous change characteristics include at least the peak acceleration amplitude, the pulse width of the peak acceleration, and the rate of change of acceleration; Determine whether the peak amplitude exceeds a preset amplitude threshold, whether the pulse width is less than a preset width threshold, and whether the rate of change of acceleration is greater than a preset rate of change threshold; If so, the tapping action is deemed valid.

6. The method according to claim 3 or 5, characterized in that, Based on the action characteristics, the gesture action is matched with service requests in a preset command library, including: When the gesture action extracted shows two valid taps, it is matched as a normal call service; When the gesture action extracted contains three valid taps, it is matched as an emergency call service.

7. The method according to claim 1, characterized in that, Sensing user gestures and matching them with service requests in a pre-defined command library also includes: It receives a first vibration signal collected by its own vibration sensor and a second vibration signal sent by the auxiliary smart wearable device; The triggering method for the gesture action is determined based on the time difference and intensity difference between the first vibration signal and the second vibration signal. Based on the triggering method, the gesture action is matched with service requests in a preset instruction library.

8. The method according to claim 7, characterized in that, Based on the time difference and intensity difference between the first vibration signal and the second vibration signal, the triggering method of the gesture action is determined, including: Determine whether the time difference is less than a preset third time threshold and whether the intensity difference is less than a preset intensity threshold; If so, then the triggering method is determined to be triggered simultaneously by the finger or hand where the wearable device is located and the finger or hand where the wearable device is located; If not, determine whether both the time difference and the intensity difference are greater than zero; If so, then the triggering method is determined to be triggered by the finger or hand in which the user is located; If not, then the triggering method is determined to be triggered by the finger or hand where the assistive smart wearable device is located.

9. The method according to claim 1, characterized in that, Sending the message to the target robot includes: Determine whether the message to be sent is the first communication message; If so, the pre-agreed key is obtained to encrypt the current message to be sent, and the encrypted message is sent to the target robot, wherein the message contains the collected physiological data; If not, the physiological data from the previous communication message is used as the encryption key to encrypt the current message to be sent, and the encrypted message is sent to the target robot.

10. The method according to claim 9, characterized in that, Before using physiological data from the previous communication message as an encryption key to encrypt the current message to be sent, the method further includes: Determine whether an acknowledgment message from the previous communication message has been received from the target robot within a preset time. If so, the physiological data from the previous communication message is used as the encryption key to encrypt the current message to be sent; If not, then rollback will use a pre-defined key to encrypt the current message to be sent and send it.

11. The method according to claim 1, characterized in that, The method further includes: The system receives the execution status corresponding to the service request from the target robot and generates tactile feedback based on the execution status.

12. The method according to claim 1, characterized in that, The method further includes: Receive the authentication request sent by the target robot and perform user authentication based on gesture features and / or physiological features; The gesture features include at least one of tapping patterns and gesture trajectories; the physiological features include at least one of heart rate variability sequence, blood oxygen waveform features, and pulse wave morphology.

13. The method according to claim 1, characterized in that, The method further includes: Compare the user's current physiological data with the pre-stored physiological data from previous historical moments; When the comparison results indicate that a user is abnormal, the user's current physiological data and location information will be sent to the emergency contact and / or an emergency call will be sent to the emergency contact.

14. A health monitoring method based on intelligent interaction, characterized in that, The method is performed by a robot, and the method includes: Receive messages sent by smart wearable devices and parse them to obtain service requests, the user's current physiological data, location information, and current timestamp; Based on the analysis results, a personalized service strategy is generated; The personalized service strategy is executed and the execution status is fed back to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

15. The method according to claim 14, characterized in that, Based on the analysis results, a personalized service strategy is generated, including: If the type of the service request is a normal call service, then obtain the user's physiological data at historical moments; Compare the physiological data from the historical moments with the physiological data from the current moment; The target item is determined based on the comparison results, and the target item is moved to the target location corresponding to the location information.

16. The method according to claim 15, characterized in that, The target item was determined based on the comparison results, and further included: If the blood glucose level in the current physiological data is lower than the preset first blood glucose threshold, the target item is determined to be a sugary beverage and / or food; If the blood glucose level in the current physiological data is higher than the preset second blood glucose threshold, the target items are identified as hypoglycemic drugs and drinking water; If the heart rate in the current physiological data is higher than the preset heart rate threshold and the body temperature is within the preset normal body temperature range, the target item is determined to be drinking water. The first blood glucose threshold and the second blood glucose threshold are determined from blood glucose data in historical physiological data, and the second blood glucose threshold is higher than the first blood glucose threshold; the heart rate threshold is determined from heart rate data in historical physiological data.

17. The method according to claim 15, characterized in that, The method further includes: If the service request type is an emergency call service, then move to the target location based on the current location information and activate the emergency plan.

18. The method according to claim 17, characterized in that, The emergency plan includes at least one of the following operations: When an abnormal heart rate is determined based on current and historical physiological data, or when the user remains stationary for an extended period, the abnormal data is sent to the emergency contact and a medical call is triggered. When fall characteristics are detected, the fall emergency procedure is activated.

19. A health monitoring method based on intelligent interaction, characterized in that, include: Smart wearable devices sense the user's gestures and match them with service requests from a pre-set command library; The smart wearable device encapsulates the matched service request with the user's current physiological data, location information, and current timestamp into a message and sends it to the target robot. The target robot receives the message and parses it to obtain the service request, the user's current physiological data, location information, and current timestamp; The target robot generates a personalized service strategy based on the type of service request and physiological data. The target robot executes the personalized service strategy and feeds back the execution status to the smart wearable device; The smart wearable device receives the execution status and generates tactile feedback based on the execution status.

20. A health monitoring system based on intelligent interaction, characterized in that, Including smart wearable devices and robots; The smart wearable device is used to perform the method as described in any one of claims 1 to 13; The robot is communicatively connected to the smart wearable device and performs the method as described in any one of claims 14 to 18.

21. The system according to claim 20, characterized in that, The smart wearable device includes an inner shell (10), an outer shell (20), a processor (30), a physiological data acquisition module (40), a vibration sensor (50), a power supply module (60), and a communication module (70). A receiving cavity is formed between the inner shell (10) and the outer shell (20), and the processor (30), the physiological data acquisition module (40), the vibration sensor (50), the power supply module (60) and the communication module (70) are all disposed in the receiving cavity; The physiological data acquisition module (40) is used to acquire the user's physiological data and feed it back to the processor (30). The vibration sensor (50) is used to sense the user's gestures and feed them back to the processor (30). The processor (30) is used to generate a message based on the gestures and the physiological data. The communication module (70) is used to communicate with the robot or other smart wearable devices (100). The power supply module (60) is used to supply power to the processor (30), the physiological data acquisition module (40), the vibration sensor (50), and the communication module (70).

22. The system according to claim 21, characterized in that, The physiological data acquisition module (40) includes a blood glucose detection unit (41), a blood oxygen and heart rate detection unit (42), and a temperature detection unit (43). The blood glucose detection unit (41) includes a laser source, a miniature spectrometer and a detector array. The laser source can be an 830nm VCSEL laser source, the miniature spectrometer is a silicon nitride Raman spectrometer, and the detector array is an InGaAs detector array. The blood oxygen and heart rate detection unit (42) includes a 660nm red LED, a 940nm infrared LED, and a photodetector; The temperature detection unit (43) includes a miniature temperature sensor.

23. The system according to claim 21, characterized in that, The smart wearable device (100) also includes an inertial sensor (80). The inertial sensor (80) is used to collect the acceleration signal corresponding to the user's gesture and feed it back to the processor (30). The processor (30) is also used to verify the tapping action extracted from the gesture based on the acceleration signal.

24. The system according to claim 23, characterized in that, The smart wearable device (100) also includes a memory; The memory can be used to store physiological data collected by the physiological data acquisition module (40), vibration signals corresponding to user gestures collected by the vibration sensor (50), acceleration signals corresponding to user gestures collected by the inertial sensor (80), messages generated by the processor (30), a pre-established instruction library, and a pre-defined encryption key.

25. The system according to claim 21, characterized in that, The smart wearable device (100) also includes an actuation module (90). The actuation module (90) is used to generate tactile feedback under the control of the processor 30.

26. The system according to claim 21, characterized in that, Both the inner shell (10) and the outer shell (20) are annular. The inner shell (10) is provided with a boss (11) facing the center of the annulus. A window (12) is provided on the boss (11); The physiological data acquisition module (40) is located inside the receiving cavity and near the window (12) of the protrusion (11).

27. The system according to claim 21, characterized in that, The power supply module (60) includes an arc-shaped outer shell (61) and a battery cell (62) disposed inside the arc-shaped outer shell (61). An electrode assembly (64) electrically connected to the battery cell (62) is provided on the end cap (63) at the end of the arc-shaped outer shell (61).

28. A health monitoring device based on intelligent interaction, characterized in that, include: The sensing unit is used to sense the user's gestures and match them with service requests in a preset instruction library; The message encapsulation unit is used to encapsulate the matched service request, along with the user's current physiological data, location information, and current timestamp, into a message. The sending unit is used to send the message to the target robot so that the target robot can execute corresponding instructions based on the parsing result of the message.

29. A health monitoring device based on intelligent interaction, characterized in that, include: The receiving unit is used to receive messages sent by smart wearable devices and parse them to obtain service requests, the user's current physiological data, location information and current timestamp; The generation unit is used to generate a personalized service strategy based on the type of the service request and physiological data. An execution and feedback unit is used to execute the personalized service strategy and feed back the execution status to the smart wearable device, so that the smart wearable device generates haptic feedback based on the execution status.

30. A computer device comprising a memory, a central processing unit (CPU), and a computer program stored in the memory and executable on the CPU, characterized in that, When the central processing unit executes the computer program, it implements the method as described in any one of claims 1 to 19.

31. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by the central processing unit, implements the method as described in any one of claims 1 to 19.

32. A computer program product, characterized in that, It includes at least one instruction or at least one program, said at least one instruction or said at least one program being loaded and executed by a central processing unit to implement the method as claimed in any one of claims 1 to 19.

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