A method and system for assessing the genioglossus muscle

Abstract

The present specification provides a method and system for evaluating the genioglossus muscle. The method comprises: displaying a target action of a human tongue through a display device; acquiring, from an acquisition device, an electromyography signal of a user performing a tongue movement with reference to the target action, the acquisition device comprising two electrodes arranged at least along a muscle fiber direction of a genioglossus muscle of the user and configured to acquire the electromyography signal of the genioglossus muscle; and generating feedback information related to a state of the genioglossus muscle based on the electromyography signal.

Description

Technical Field

[0001] This specification relates to the field of signal detection and evaluation technology, and in particular to a method and system for evaluating the genioglossus muscle. Background Technology

[0002] Obstructive sleep apnea (OSA) is a common sleep-disordered breathing condition affecting a large number of people and seriously impacting their health. Due to low awareness and weak perception of the condition (e.g., lack of awareness during sleep) and a shortage of medical resources, OSA has not received effective attention and treatment. OSA typically occurs when the upper airway collapses during sleep, obstructing airflow. The genioglossus muscle, the largest tongue muscle, is responsible for pushing the tongue forward to keep the airway open. When the function of the genioglossus muscle weakens, the tongue may shift backward, partially or completely obstructing the airway, thus triggering OSA. Specific exercises for the genioglossus muscle can help alleviate OSA symptoms.

[0003] Therefore, it is desirable to provide a method for assessing the genioglossus muscle, which can effectively and accurately evaluate and provide feedback on the state of the genioglossus muscle, thereby adjusting the training program according to the state of the genioglossus muscle and improving its function. Summary of the Invention

[0004] One embodiment of this specification provides a method for assessing the genioglossus muscle, comprising: displaying a target movement of the human tongue via a display device; acquiring electromyographic (EMG) signals from a user performing tongue movements in reference to the target movement via a acquisition device, the acquisition device comprising two electrodes spaced apart at least along the muscle fiber direction of the user's genioglossus muscle and configured to acquire the EMG signals of the genioglossus muscle; and generating feedback information related to the state of the genioglossus muscle based on the EMG signals. This method, by acquiring the EMG signals of the genioglossus muscle when a user performs tongue movements and assessing and / or providing feedback on the state of the genioglossus muscle based on the EMG signals, provides the user with information such as whether the performed movement was performed correctly and the training effect, helping the user to conduct more targeted training and improve training efficiency and effectiveness.

[0005] In some embodiments, generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal includes: acquiring first amplitude information of the electromyographic signal; and generating the feedback information based on the first amplitude information, wherein the feedback information includes an assessment parameter reflecting the force exertion ability of the genioglossus muscle, so that a user can judge the force exertion ability of the genioglossus muscle based on the assessment parameter.

[0006] In some embodiments, the first amplitude information includes the average electromyographic amplitude or root mean square value of the electromyographic signal over a preset time period.

[0007] In some embodiments, generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal includes: acquiring frequency information of the electromyographic signal; generating assessment parameters reflecting the fatigue resistance of the genioglossus muscle based on the frequency information; and generating feedback information related to the fatigue resistance of the genioglossus muscle based on the assessment parameters. The frequency information can reflect whether the genioglossus muscle has entered a fatigue state when the user performs the target action, thereby generating corresponding prompt information. This allows for real-time monitoring of the user's genioglossus muscle fatigue level based on electromyographic signals, enabling automatic adjustment of the training program or timely reminders to the user when muscle fatigue is detected, thus avoiding muscle damage caused by over-fatigue.

[0008] In some embodiments, the frequency information includes the median frequency of the electromyographic signal, and the evaluation parameters include parameters reflecting the changes in the median frequency over different time periods.

[0009] In some embodiments, generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal includes: acquiring second amplitude information of the electromyographic signal; generating an assessment parameter reflecting the fatigue resistance of the genioglossus muscle based on the second amplitude information; and generating feedback information related to the fatigue resistance of the genioglossus muscle based on the assessment parameter. The second amplitude information can reflect whether the genioglossus muscle enters a fatigue state when the user maintains a target movement, thereby reflecting the fatigue resistance of the genioglossus muscle.

[0010] In some embodiments, the second amplitude information includes at least one of the average amplitude of the electromyographic signal and the integrated electromyographic amplitude, and the evaluation parameters include parameters that reflect the changes of the second amplitude information over different time periods.

[0011] In some embodiments, the feedback information includes electrical stimulation applied to the genioglossus muscle by an electrical stimulation module. By applying electrical stimulation, the genioglossus muscle can be induced to contract, replacing or assisting in the voluntary contraction of the muscle to achieve a training effect, thereby establishing the user's perception of the muscle.

[0012] In some embodiments, the electrical stimulation module includes the two electrodes configured to apply the electrical stimulation to the genioglossus muscle. Using the two electrodes as the electrical stimulation module simplifies the structure and facilitates device miniaturization.

[0013] In some embodiments, the electrical stimulation module includes two second electrodes arranged at least along the muscle fiber direction of the genioglossus muscle and configured to apply the electrical stimulation to the genioglossus muscle. By using additional electrodes as the electrical stimulation module, specialization of various structures can be achieved, which simplifies the design and manufacturing process.

[0014] In some embodiments, the feedback information includes information indicating whether the user's action is performed correctly, wherein the feedback information is generated based on the amplitude or frequency information of the electromyographic signal. This ensures that the user's tongue movements effectively stimulate the genioglossus muscle without causing excessive fatigue, thus guaranteeing training effectiveness while improving the user experience.

[0015] In some embodiments, the feedback information includes information identifying the actions actually performed by the user, wherein the feedback information is generated based on one or more parameters related to the shape of the electromyographic signal. This allows for the identification and feedback of the user's actual actions based on the electromyographic signal. The user can determine whether their actual action is the same as the target action and proactively adjust their actions when they determine that the actual action differs from the target action, thereby improving training efficiency and enhancing the user experience.

[0016] In some embodiments, the method further includes dynamically adjusting the target action.

[0017] In some embodiments, dynamically adjusting the target movement includes: acquiring the user's heart rate or respiratory parameters during sleep; and adjusting the target movement based on the heart rate or respiratory parameters. The user's heart rate and respiratory parameters during sleep can reflect the improvement in the user's OSA (Excessive Stress Awareness), thereby reflecting the training effect on the genioglossus muscle. Therefore, the target movement can be adjusted based on the heart rate or respiratory parameters to improve the training effect.

[0018] Additional features will be set forth in part in the description which follows, and will become apparent to those skilled in the art upon consulting the following description and the accompanying drawings, or may be learned by the generation or operation of examples. The features of the invention can be realized and obtained by practice or use of various aspects of the methods, tools, and combinations set forth in the following detailed examples. Attached Figure Description

[0019] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0020] Figure 1 This is a schematic diagram of a system for evaluating the genioglossus muscle according to some embodiments of this specification;

[0021] Figure 2 This is a block diagram of a genioglossus muscle assessment device according to some embodiments of this specification;

[0022] Figure 3 This is a flowchart illustrating a method for evaluating the genioglossus muscle according to some embodiments of this specification;

[0023] Figure 4 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification;

[0024] Figure 5 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification;

[0025] Figure 6 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification. Specific Implementation

[0026] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. It should be understood that these exemplary embodiments are given merely to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0027] As indicated in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally, the terms "comprising" and "including" only indicate the inclusion of expressly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements. The term "based on" means "at least partially based on." The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment."

[0028] Flowcharts are used in this specification to illustrate the operations performed by the system according to embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, the steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0029] The genioglossus muscle plays a crucial role in maintaining upper airway patency during sleep. In patients with obstructive sleep apnea (OSA), genioglossus muscle activity declines more significantly and rapidly than in healthy subjects, making them more prone to upper airway obstruction. Because the genioglossus muscle is essential for maintaining upper airway patency during sleep and wakefulness, targeted training of this muscle can enhance its tone, enabling it to more effectively maintain a forward-facing tongue position during sleep, reducing tongue collapse and thus decreasing the risk of airway obstruction. Furthermore, targeted training of the genioglossus muscle can engage other related muscles (e.g., tongue muscles, soft palate muscles, laryngeal muscles), improving muscle coordination, helping to maintain airway patency, and ultimately alleviating OSA symptoms.

[0030] This specification provides a method for assessing the genioglossus muscle. The method displays a target tongue movement to a user via a display device and acquires electromyographic (EMG) signals from a user performing the tongue movement in reference to the target movement via a data acquisition device. Feedback information related to the state of the genioglossus muscle is then generated based on the EMG signals. The data acquisition device may include two electrodes spaced apart at least along the muscle fiber direction of the user's genioglossus muscle and configured to acquire EMG signals from the genioglossus muscle. This method can guide users in performing oral muscle exercises, particularly exercises of the genioglossus muscle, by displaying a target movement, thereby enhancing the function of the genioglossus muscle. Furthermore, this method can also assess and / or provide feedback on the state of the genioglossus muscle by acquiring the EMG signals of the genioglossus muscle during tongue movements, providing information such as whether the performed movement was performed correctly and the training effect, helping users to conduct more targeted training and improve training efficiency and effectiveness.

[0031] Figure 1 This is a schematic diagram of a system for evaluating the genioglossus muscle, as shown in some embodiments of this specification. Figure 1 As shown, the system for assessing the genioglossus muscle (hereinafter referred to as system 100) may include a processing device 110, a network 120, a storage device 130, a terminal device 140, and a data acquisition device 150. The various components in system 100 can be connected in various ways. For example, the data acquisition device 150 may be connected to the storage device 130 and / or the processing device 110 via the network 120, or it may be directly connected to the storage device 130 and / or the processing device 110. As another example, the storage device 130 may be directly connected to the processing device 110 or connected via the network 120. As yet another example, the terminal device 140 may be connected to the storage device 130 and / or the processing device 110 via the network 120, or it may be directly connected to the storage device 130 and / or the processing device 110.

[0032] In some embodiments, system 100 may assess the genioglossus muscle by implementing the methods and / or processes disclosed in this specification, generating feedback information related to the state of the genioglossus muscle. For example, processing device 110 may display a target movement of the human tongue on a display device and acquire electromyographic signals of a user performing tongue movements in reference to the target movement, thereby generating feedback information related to the state of the genioglossus muscle based on the electromyographic signals. As another example, processing device 110 may acquire heart rate or respiratory parameters of a user during sleep and adjust the target movement based on the heart rate or respiratory parameters.

[0033] The processing device 110 can process data and / or information obtained from the acquisition device 150, storage device 130, terminal device 140, and / or other components of the system 100. In some embodiments, the processing device 110 can obtain a user's physiological signals (e.g., electromyographic signals) from any one or more of the acquisition device 150, storage device 130, or terminal device 140, and process the physiological signals to generate feedback information related to the state of the genioglossus muscle. In some embodiments, the processing device 110 can retrieve pre-stored computer instructions from the storage device 130 and execute the computer instructions to implement the method for assessing the genioglossus muscle described herein.

[0034] In some embodiments, the processing device 110 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, the processing device 110 may be local or remote. For example, the processing device 110 may access information and / or data from the acquisition device 150, storage device 130, and / or terminal device 140 via network 120. Alternatively, the processing device 110 may be directly connected to the acquisition device 150, storage device 130, and / or terminal device 140 to access information and / or data. In some embodiments, the processing device 110 may be implemented on a cloud platform. For example, the cloud platform may include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, inter-cloud cloud, multi-cloud, etc., or any combination thereof.

[0035] Network 120 can connect various components of system 100 and / or connect system 100 to external resources. Network 120 enables communication between the various components of system 100 and with other parts outside system 100, facilitating the exchange of data and / or information. For example, processing device 110 can obtain physiological signals (e.g., electromyographic signals, respiratory signals, heart rate signals, etc.) from acquisition device 150 and / or storage device 130 via network 120. As another example, processing device 110 can obtain user operation instructions from terminal device 140 via network 120; exemplary operation instructions may include, but are not limited to, setting user information (e.g., gender, age, height, weight, medical history, etc.) and selecting training courses.

[0036] In some embodiments, network 120 can be any form of wired or wireless network, or any combination thereof. By way of example only, network 120 may include cable networks, wired networks, fiber optic networks, telecommunications networks, internal networks, the Internet, local area networks (LANs), wide area networks (WANs), wireless local area networks (WLANs), metropolitan area networks (MANs), public switch telephone networks (PSTNs), Bluetooth networks, ZigBee networks, near field communication (NFC) networks, etc., or any combination thereof. In some embodiments, network 120 may include at least one network access point, through which at least one component of system 100 can connect to network 120 to exchange data and / or information. For example, data such as physiological signals and feedback information related to the state of the genioglossus muscle can be transmitted via network 120.

[0037] Storage device 130 can store data, instructions, and / or any other information. In some embodiments, storage device 130 can store data obtained from acquisition device 150, processing device 110, and / or terminal device 140. For example, storage device 130 can store physiological signals acquired by acquisition device 150. In some embodiments, storage device 130 can store data and / or instructions used by processing device 110 to perform or use in order to complete the exemplary methods described herein. In some embodiments, storage device 130 can include mass storage, removable storage, volatile read-write storage, read-only storage (ROM), etc., or any combination thereof. Exemplary mass storage can include disks, optical disks, solid-state drives, etc. In some embodiments, storage device 130 can be implemented on a cloud platform. By way of example only, the cloud platform can include private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-layer cloud, etc., or any combination thereof.

[0038] In some embodiments, storage device 130 may be connected to network 120 to communicate with at least one other component of system 100 (e.g., acquisition device 150, processing device 110, terminal device 140). At least one component of system 100 may access data, instructions, or other information stored in storage device 130 via network 120. In some embodiments, storage device 130 may be directly connected to or communicate with one or more components of system 100 (e.g., acquisition device 150, terminal device 140). In some embodiments, storage device 130 may be part of acquisition device 150 and / or processing device 110.

[0039] Terminal device 140 can receive, send, and / or display data. The received data may include data collected by acquisition device 150, data stored by storage device 130, and feedback information related to the state of the genioglossus muscle generated by processing device 110. For example, the data received and / or displayed by terminal device 140 may include target movements of the human tongue. As another example, the data received and / or displayed by terminal device 140 may include physiological signals collected by acquisition device 150 and feature values ​​obtained by processing device 110 from identifying physiological signals (e.g., amplitude information, average electromyographic amplitude or root mean square, integrated electromyographic amplitude, frequency information of electromyographic signals, heart rate parameters or respiratory parameters during user sleep, etc.). Yet another example, the data received and / or displayed by terminal device 140 may include feedback information related to the state of the genioglossus muscle generated by processing device 110 based on physiological signals. The sent data may include user input data and commands. For example, terminal device 140 can send user-inputted operation commands to acquisition device 150 via network 120 to control acquisition device 150 to perform corresponding data acquisition. For example, terminal device 140 can send user-inputted information (e.g., gender, age, height, weight, medical history, etc.) to processing device 110 via network 120. Processing device 110 can then recommend training courses (e.g., target movements of the human tongue) based on the user information. Alternatively, terminal device 140 can send user-inputted training course selection information to processing device 110. Processing device 110 can then generate corresponding training courses based on the user's input training course selection information for display on the terminal device's interface.

[0040] In some embodiments, terminal device 140 may include mobile device 141, tablet computer 142, laptop computer 143, etc., or any combination thereof. For example, mobile device 141 may include mobile phone, personal digital assistant (PDA), medical mobile terminal, etc., or any combination thereof. In some embodiments, terminal device 140 may include input devices (such as keyboard, touch screen), output devices (such as display, speaker, etc.). In some embodiments, processing device 110 may be part of terminal device 140.

[0041] The acquisition device 150 can be a device for acquiring a user's physiological signals. Exemplary physiological signals may include electromyography (EMG) signals, electrocardiogram (ECG) signals, respiratory signals, etc. Accordingly, the acquisition device 150 may include an EMG signal acquisition device 151, an ECG signal acquisition device 152, a respiratory signal acquisition device 153, etc. The EMG signal acquisition device 151 may include one or more electrodes. For example, the EMG signal acquisition device 151 may include two electrodes that can be attached to the area between the user's chin and neck and spaced apart at least along the direction of the muscle fibers of the user's genioglossus muscle to acquire the EMG signals of the user's genioglossus muscle. The ECG signal acquisition device 152 may include multiple electrodes that can be attached to different parts of the user's body to acquire the user's ECG signals (or heart rate parameters). The respiratory signal acquisition device 153 may include a respiratory rate sensor, a flow sensor, etc., for detecting the user's respiratory signals. In some embodiments, the acquisition device 150 (e.g., an electromyography (EMG) signal acquisition device 151) can acquire the user's EMG signals when the user performs tongue movements, thereby evaluating the state of the genioglossus muscle and / or the training process. In some embodiments, the acquisition device 150 can be used to acquire the user's electrocardiogram (ECG) and / or respiratory signals while the user is asleep, thereby evaluating the training effect of the genioglossus muscle. For example, the processing device 110 can determine the user's heart rate parameters and respiratory parameters during sleep based on the ECG and respiratory signals, respectively, thereby determining the user's OSA improvement, which can reflect the training effect of the genioglossus muscle. Exemplary heart rate parameters may include an electrocardiogram, heart rate curves, heart rate variability (HRV) curves, etc. Exemplary respiratory parameters may include respiratory rate, gas flow rate, and Apnea-Hyponea Index (AHI). In some embodiments, the acquisition device 150 may also include a sleep heart rate monitoring device. The sleep heart rate monitoring device can be used to collect physiological signals during a user's sleep, thereby monitoring the user's OSA status. For example, the sleep heart rate monitoring device can simultaneously collect the user's electrocardiogram (ECG), respiratory signals, and blood oxygen saturation, thereby obtaining one or more parameters such as sleep quality, sleep duration, sleep stages, respiratory rate, sleep ECG, heart rate curve, HRV curve, and blood oxygen saturation, to monitor the user's OSA status. As an example only, the sleep heart rate monitoring device may include at least two electrodes and a wearable structure. At least two electrodes are spaced apart on the wearable structure, and when the wearable structure is worn, the at least two electrodes are located on either side of the midsagittal plane of the body and are used to adhere to the skin to collect the body's ECG signals.The sleep heart rate monitoring device may also include a sensor module disposed on the wearable structure for detecting changes in the direction of movement in the waist area of ​​the human body, thereby detecting human respiratory information (e.g., respiratory rate) based on changes in the undulations of the human muscles or skin.

[0042] In some embodiments, the acquisition device 150 can also be used to provide feedback information to the user. For example, the acquisition device 150 may include an electrical stimulation module, and the feedback information may be electrical stimulation applied to the user's genioglossus muscle by the electrical stimulation module. The electrical stimulation may refer to the stimulation signal applied to the user's genioglossus muscle by the electrical stimulation module. The stimulation signal can cause the genioglossus muscle to contract, thereby replacing or assisting the voluntary contraction of the genioglossus muscle to achieve a training effect, thereby establishing the user's perception of the muscle and helping users who cannot control their tongue movements to achieve the training effect. In some embodiments, the electromyography signal acquisition device 151 may be configured as an electrical stimulation module. That is, the electrical stimulation module may include two electrodes in the electromyography signal acquisition device 151, and the two electrodes may be configured to apply electrical stimulation to the user's genioglossus muscle. In some embodiments, the electrical stimulation module may include two second electrodes (not shown in the figure), which are arranged at least along the muscle fiber direction of the genioglossus muscle and are configured to apply electrical stimulation to the genioglossus muscle.

[0043] In some embodiments, the acquisition device 150 may have an independent power supply and can transmit the acquired data to other components (e.g., processing device 110, storage device 130, terminal device 140) via wired or wireless means (e.g., Bluetooth, WiFi, etc.). In some embodiments, one or more components on the system 100 may be implemented on the acquisition device 150. For example, the acquisition device 150 may include the processing device 110, the storage device 130, etc.

[0044] It should be noted that the above description of system 100 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to system 100 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0045] Figure 2 This is a block diagram of a genioglossus muscle assessment device according to other embodiments of this specification. In some embodiments, Figure 2 The genioglossus muscle assessment device 200 shown can be applied in software and / or hardware. Figure 1 The system 100 shown, for example, can be configured in the form of software and / or hardware to the processing device 110 and / or the terminal device 140 for evaluating the physiological signals acquired by the acquisition device 150.

[0046] Reference Figure 2 In some embodiments, the genioglossus muscle assessment device 200 may include a display module 210, an acquisition module 220, and a feedback module 230.

[0047] The display module 210 can be used to display target movements of the human tongue via a display device. The target movements can be various preset movements that reflect the movements of the human tongue. Users can perform tongue movements in accordance with the target movements, thereby achieving the assessment and / or training of tongue muscles (such as the genioglossus muscle).

[0048] The acquisition module 220 can be used to acquire electromyographic (EMG) signals from a user performing tongue movements in reference to the target action, obtained from the acquisition device. The target action can be displayed to the user via a display device, and the user can perform tongue movements in reference to the target action while wearing the acquisition device. The acquisition device can acquire the EMG signals when the user performs tongue movements in reference to the target action. The acquisition module 220 can acquire the EMG signals from the acquisition device.

[0049] Feedback module 230 can be used to generate feedback information related to the state of the genioglossus muscle based on the electromyographic signal. In some embodiments, feedback module 230 can acquire amplitude information of the electromyographic signal (e.g., first amplitude information, second amplitude information, etc.) and generate feedback information related to the state of the genioglossus muscle based on the amplitude information. In some embodiments, feedback module 230 can acquire frequency information of the electromyographic signal and generate feedback information related to the state of the genioglossus muscle based on the frequency information. In some embodiments, the feedback information can be presented to the user through display module 210.

[0050] For more details about the above modules, please refer to other parts of this manual (e.g., Figures 3-6 (and related descriptions), which will not be elaborated here.

[0051] It should be understood that Figure 2 The genioglossus muscle assessment device 200 and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented in hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by an appropriate instruction execution system, such as a microprocessor or dedicated hardware.

[0052] It should be noted that the above description of the genioglossus muscle assessment device 200 is provided for illustrative purposes only and is not intended to limit the scope of this specification. It will be understood that those skilled in the art can, based on the description in this specification, arbitrarily combine the various modules, or construct subsystems and connect them to other modules, without departing from this principle. For example, Figure 2The display module 210, acquisition module 220, and feedback module 230 described herein can be different modules within a single system, or a single module can implement the functions of two or more of the aforementioned modules. For example, the genioglossus muscle assessment device 200 may also include an adjustment module for adjusting the target movement. This adjustment module can acquire the user's heart rate or respiratory parameters during sleep and adjust the target movement based on these parameters. Such modifications are all within the scope of this specification.

[0053] Figure 3 This is a flowchart illustrating a method for evaluating the genioglossus muscle according to some embodiments of this specification. In some embodiments, method 300 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, one or more operations in method 300 can be performed by... Figure 1 The processing device 110 and / or terminal device 140 shown are used for implementation. For example, method 300 can be stored in storage device 130 as instructions and invoked and / or executed by processing device 110 and / or terminal device 140. The execution process of method 300 is described below using processing device 110 as an example. Refer to Figure 3 In some embodiments, the method 300 for assessing the genioglossus muscle may include the following steps.

[0054] In step 310, the processing device 110 (e.g., display module 210) can display the target movement of the human tongue through the display device.

[0055] The target movement can be any preset movement reflecting the human tongue. Users can perform tongue movements in accordance with the target movement, thereby achieving assessment and / or training of the tongue muscles (e.g., the genioglossus muscle). Exemplary target movements may include tongue pushing upwards, tongue protrusion, tongue depressing downwards, tongue circling, tongue sliding, whistling and blowing exercises, pronunciation exercises, etc. The target movement can be displayed to the user in the form of images, videos, or other formats, and the user can wear a data acquisition device (e.g., [missing information]). Figure 1 The electromyography signal acquisition device 151 shown in the figure executes tongue movements in accordance with the target action.

[0056] In some embodiments, the processing device 110 can set the frequency, duration, and execution order of multiple target actions to generate tongue training courses for different users. For example, different action frequencies and / or durations can be set to generate training courses of varying intensities, which can then be used to train users with different symptoms. In some embodiments, the processing device 110 can display the information through a display device (e.g., Figure 1The terminal device 140 shown displays a list of different training courses and displays one or more target movements from the corresponding training courses based on the user's selection information. In some embodiments, the processing device 110 may acquire user information (e.g., gender, age, height, weight, medical history, etc.) and recommend training courses or directly display one or more target movements from the recommended training courses based on the user information.

[0057] In some embodiments, the processing device 110 can display a target movement before the user begins a training session and generate feedback information related to the state of the genioglossus muscle to assess the pre-training state of the genioglossus muscle. For example, the target movement may include tongue protrusion. The processing device 110 can acquire the electromyographic (EMG) signals of the genioglossus muscle when the user performs the tongue protrusion movement and generate feedback information related to the muscle's ability to exert force based on the EMG signals, thereby assessing the muscle's ability to exert force. As another example, the target movement may include tongue depression. The processing device 110 can acquire the EMG signals of the genioglossus muscle when the user performs the tongue depression movement and generate feedback information related to the muscle's fatigue resistance based on the EMG signals, thereby assessing the muscle's fatigue resistance.

[0058] In some embodiments, the processing device 110 can display target movements during a user's training course and generate feedback information related to the state of the genioglossus muscle to assess the state of the genioglossus muscle during training. For example, the processing device 110 can display one or more target movements included in the training course and generate information prompting the user on whether the movement was performed correctly or information identifying the movement actually performed by the user.

[0059] In some embodiments, the processing device 110 can display the target movement after the user completes a training course and generate feedback information related to the state of the genioglossus muscle to assess the state of the genioglossus muscle after training. The target movement displayed after the training course can be the same as the target movement displayed before the training course, so that the training effect can be determined by comparing the state of the genioglossus muscle before and after training.

[0060] In step 320, the processing device 110 (e.g., acquisition module 220) can acquire electromyographic signals from the acquisition device of the user performing tongue movements in reference to the target action.

[0061] As described above, the target action can be displayed to the user via a display device, and the user can perform tongue movements in reference to the target action while wearing the acquisition device. The acquisition device can acquire electromyographic (EMG) signals when the user performs tongue movements in reference to the target action. Furthermore, the processing device 110 can acquire the EMG signals from the acquisition device.

[0062] The acquisition device may include a wearable body and two electrodes. The wearable body is configured to at least partially cover the area corresponding to the user's genioglossus muscle. For example, the wearable body may be worn on the surface of the body and at least partially cover the area between the user's chin and neck. Alternatively, the wearable body may be worn inside the user's mouth and at least partially cover the area between the lower jaw and the root of the tongue. The two electrodes may be fixed to the wearable body and in contact with the user's skin or oral mucosa. The two electrodes may be spaced apart at least along the muscle fiber direction of the user's genioglossus muscle and configured to acquire electromyographic signals of the user's genioglossus muscle. For example, the two electrodes may be spaced apart along the muscle fiber direction of the user's genioglossus muscle and extend in a direction perpendicular to the muscle fiber direction, thereby acquiring the potential of the skin surface at their respective locations. The potential difference between the acquired potentials can be used to reflect the electromyographic signals of the genioglossus muscle.

[0063] In step 330, the processing device 110 (e.g., feedback module 230) can generate feedback information related to the state of the genioglossus muscle based on the electromyographic signal.

[0064] The feedback information can reflect the state of the genioglossus muscle when the user performs tongue movements with reference to the target action. In some embodiments, the processing device 110 can display the feedback information to the user through a display device, and the user can obtain the state of the genioglossus muscle (e.g., before and after training) or the execution of tongue movements (e.g., during training) based on the feedback information, thereby increasing the user's motivation and improving the training effect.

[0065] In some embodiments, the feedback information may include feedback information related to the genioglossus muscle's ability to exert force. For example, the processing device 110 may acquire amplitude information (or first amplitude information) of an electromyographic signal and use the amplitude information as feedback information. The amplitude information can serve as an assessment parameter reflecting the genioglossus muscle's ability to exert force, allowing the user to determine the genioglossus muscle's ability to exert force based on this assessment parameter. For more information on generating feedback information based on the first amplitude information, please refer to [link to relevant documentation]. Figure 4 The details and related descriptions will not be repeated here.

[0066] In some embodiments, the feedback information may include feedback information related to the fatigue resistance of the genioglossus muscle. In some embodiments, the feedback information related to the fatigue resistance of the genioglossus muscle may be generated based on amplitude information of electromyographic signals. For example, the processing device 110 may acquire amplitude information (or second amplitude information) of electromyographic signals and generate assessment parameters reflecting the fatigue resistance of the genioglossus muscle based on the amplitude information, thereby generating feedback information related to the fatigue resistance of the genioglossus muscle based on the assessment parameters. For more details on generating feedback information based on second amplitude information, please refer to [link to relevant documentation]. Figure 5The relevant descriptions are not repeated here. In some embodiments, the feedback information related to the anti-fatigue ability of the genioglossus muscle can be generated based on the frequency information of electromyographic signals. For example, the processing device 110 can acquire the frequency information of electromyographic signals and generate assessment parameters reflecting the anti-fatigue ability of the genioglossus muscle based on the frequency information, thereby generating feedback information related to the anti-fatigue ability of the genioglossus muscle based on the assessment parameters. For more information on generating feedback information based on frequency information, please refer to [link to relevant documentation]. Figure 6 The details and related descriptions will not be repeated here.

[0067] In some embodiments, the feedback information may include information prompting the user whether the performed action was performed correctly. The feedback information may be generated based on the amplitude or frequency information of the electromyographic (EMG) signal. The amplitude information may include the amplitude of the EMG signal, average EMG amplitude, maximum amplitude, minimum amplitude, root mean square amplitude, etc. In some embodiments, the processing device 110 may compare the amplitude information of the EMG signal with reference amplitude information to determine whether the performed action was performed correctly, thereby generating information prompting the user whether the performed action was performed correctly. The reference amplitude information may be the amplitude information corresponding to the user achieving maximum voluntary contraction (MVC) when performing the target action. For example, the reference amplitude information may be the amplitude information generated when the user performs the target action to the maximum extent to achieve MVC before the start of the training process. This reference amplitude information can serve as the limit of the amplitude information that the user can reach when performing the target action, and thus can serve as a reference standard for the current amplitude information (e.g., the amplitude information generated during training) to determine whether the user's current target action was performed correctly.

[0068] Taking the target action as tongue protrusion and the amplitude information as average electromyographic amplitude as an example, the processing device 110 can acquire reference electromyographic signals of the genioglossus muscle when the user performs the tongue protrusion action to achieve MVC and maintain it for a certain period of time (e.g., 5s), and calculate the average electromyographic amplitude within a preset time period (e.g., the middle of the action maintenance time (e.g., 2s-4s)) as reference amplitude information. Optionally, the user can perform the target action to achieve MVC multiple times. The processing device 110 can acquire multiple corresponding reference electromyographic signals and calculate the average electromyographic amplitude of each reference electromyographic signal within a preset time period, and use the average of the average electromyographic amplitudes corresponding to multiple reference electromyographic signals as reference amplitude information. Further, the processing device 110 can acquire the amplitude information of the electromyographic signals generated when the user performs any protrusion action (e.g., a protrusion action performed during training), and compare the current amplitude information with the reference amplitude information to determine whether the user's action is in place. For example, if the current amplitude information is less than the reference amplitude information, the processing device 110 can determine that the user's action is not complete and generate corresponding feedback information. Optionally or additionally, a portion of the average electromyographic amplitude corresponding to the MVC can be used as the reference amplitude information. For example, the size of the reference amplitude information can be in the range of 50%-70% of the average electromyographic amplitude corresponding to the MVC. As an example only, if the current amplitude information is less than 50% of the average electromyographic amplitude corresponding to the MVC, the processing device 110 can determine that the user's action is not complete and generate corresponding feedback information. That is to say, the user's tongue movement only needs to reach 50% of the MVC to be considered as having completed the action. This ensures that the user's tongue movement can effectively stimulate the genioglossus muscle without causing excessive fatigue of the genioglossus muscle, ensuring training effectiveness while improving user experience.

[0069] In some embodiments, the processing device 110 can compare the frequency information of the electromyographic signal with reference frequency information to determine whether the executed action is performed correctly, thereby generating information to prompt the user whether the executed action is performed correctly. The frequency information may include the median frequency, peak frequency, etc. of the electromyographic signal. The reference frequency information may be the frequency information corresponding to when the user achieves MVC when performing the target action.

[0070] In some embodiments, the feedback information may include information indicating the degree of fatigue in the genioglossus muscle. For example, during genioglossus muscle training, the processing device 110 may generate information indicating the degree of fatigue in the genioglossus muscle, thereby reminding the user or dynamically adjusting the training course based on the information. The feedback information may be generated based on the frequency information of electromyographic signals. The frequency information may include the median frequency, peak frequency, etc., of the electromyographic signals. For example, the processing device 110 may acquire curves showing the changes of multiple median frequencies over time during different time periods during the user's training process, and the slope of the curves may reflect the changes in median frequencies over different time periods. As an example only, when the slope of the curve is greater than a preset slope threshold, it can be determined that the muscle has entered a state of fatigue. As another example, one or more differences between a later median frequency and an earlier median frequency may be acquired according to the time sequence, and the differences may be used as parameters reflecting the changes in the median frequency over different time periods. When the difference is greater than a preset difference threshold, it can be determined that the muscle has entered a state of fatigue. For example, the percentage difference between the subsequent median frequency and the first median frequency can be obtained. This percentage difference can serve as a parameter reflecting the change of the median frequency over different time periods. When the percentage difference exceeds a preset percentage threshold, it can be determined that the muscle has entered a state of fatigue. Using these parameters, it is possible to reflect whether the genioglossus muscle has entered a state of fatigue when the user performs the target action, thereby generating corresponding prompts. Therefore, this embodiment of the specification can monitor the fatigue level of the user's genioglossus muscle in real time based on electromyographic signals, thereby automatically adjusting the training plan or promptly reminding the user when muscle fatigue is detected, thus avoiding muscle damage caused by over-fatigue.

[0071] In some embodiments, the feedback information may include information identifying the action actually performed by the user. The feedback information may be generated based on one or more parameters related to the shape of the electromyographic (EMG) signal. These parameters may include frequency, peak value, RMS value, duty cycle, amplitude, phase, waveform, peak factor, bandwidth, etc. In some embodiments, multiple reference EMG signals corresponding to reference actions may be pre-acquired, and parameters related to the shape of the corresponding multiple reference EMG signals may be obtained as reference parameters. The processing device 110 may obtain parameters related to the shape of the current EMG signal and compare them with the reference parameters to identify the action actually performed by the user. For example, the processing device 110 may determine the reference action corresponding to the reference parameter with the highest similarity to the shape-related parameters of the current EMG signal as the action actually performed by the user. In some embodiments, the processing device 110 may identify the action actually performed by the user through an action recognition model. The processing device 110 may input one or more parameters related to the shape of the EMG signal into the action recognition model to output the action actually performed by the user. The action recognition model may be a trained machine learning model, the training process of which takes one or more parameters related to the shape of the EMG signal as input and the action actually performed by the user as a label. In some embodiments, the processing device 110 can identify the user's actual actions using a graphics processing algorithm. For example, the processing device 110 can generate a waveform image corresponding to the shape of an electromyographic signal and compare the waveform image with reference waveform images corresponding to multiple reference actions to identify the user's actual actions. Therefore, the embodiments of this specification can identify and provide feedback on the user's actual actions based on electromyographic signals. The user can determine whether their actual action is the same as the target action and actively adjust their actions when they determine that the actual action is different from the target action, thereby improving training efficiency and enhancing the user experience.

[0072] In some embodiments, the feedback information may further include electrical stimulation applied to the genioglossus muscle by the electrical stimulation module. For users who are unable to train their genioglossus muscle through self-control, the processing device 110 can apply electrical stimulation to the genioglossus muscle through the electrical stimulation module to induce muscle contraction, thereby replacing or assisting in the voluntary contraction of the muscle to achieve a training effect and establish the user's perception of the muscle.

[0073] In some embodiments, the processing device 110 may apply electrical stimulation to the genioglossus muscle via an electrical stimulation module in response to a user-inputted instruction. For example, a user may select or input an instruction related to receiving electrical stimulation on a display device, and the processing device 110 may, based on the instruction, cause the electrical stimulation module to apply electrical stimulation to the genioglossus muscle. In some embodiments, the processing device 110 may determine whether to apply electrical stimulation to the genioglossus muscle via the electrical stimulation module based on electromyographic (EMG) signals. For example, the processing device 110 may determine whether the amplitude information (e.g., amplitude, average EMG amplitude, maximum amplitude, minimum amplitude, root mean square amplitude, etc.) and / or frequency information (e.g., frequency, median frequency, peak frequency) of the EMG signals are less than a preset threshold. In response to the amplitude information and / or frequency information of the EMG signals being less than the preset threshold, the processing device 110 may apply electrical stimulation to the genioglossus muscle via the electrical stimulation module.

[0074] In some embodiments, the acquisition device can serve as the electrical stimulation module. That is, the two electrodes of the acquisition device can be configured to apply electrical stimulation to the user's genioglossus muscle. Using the acquisition device as the electrical stimulation module simplifies the structure and facilitates device miniaturization. In some embodiments, the electrical stimulation module may include two second electrodes, which are arranged at least along the muscle fiber direction of the genioglossus muscle and configured to apply electrical stimulation to the genioglossus muscle. By using additional electrodes as the electrical stimulation module, specialization of various structures can be achieved, which simplifies the design and manufacturing process.

[0075] In some embodiments, the processing device 110 can determine the waveform of the electrical stimulation based on the type of the target action. The electrical stimulation can be a combination signal formed by one or more of square waves, sine waves, pulse signals, etc. Different target actions require training different muscle groups and have different intensities; therefore, different waveforms of electrical stimulation need to be applied for different target actions. For example, electromyographic signals from other users performing various target actions can be acquired by a data acquisition device and corresponding reference waveforms can be generated. The processing device 110 can acquire the reference waveform corresponding to the target action based on the target action currently being performed by the user. Further, the processing device 110 can determine the waveform of the electrical stimulation based on the reference waveform. As an example only, the waveform of the electrical stimulation can be the same as or similar to the reference waveform.

[0076] During the application of electrical stimulation, the stimulation can cause the genioglossus muscle to contract, thereby generating an electrical signal. In some embodiments, the processing device 110 can acquire the electromyographic (EMG) signal of the genioglossus muscle collected by the acquisition device and adjust the intensity of the electrical stimulation based on the EMG signal. For example, the processing device 110 can determine whether the intensity of the EMG signal (e.g., based on amplitude information and / or frequency information) is greater than a preset stimulation threshold. In response to the EMG signal intensity being greater than the stimulation threshold, the processing device 110 can decrease the intensity of the electrical stimulation; conversely, it can increase the intensity of the electrical stimulation. The stimulation threshold can be determined based on a reference EMG signal of the genioglossus muscle collected when another user (e.g., a user capable of performing genioglossus muscle training normally) performs the target action. For example, the intensity of the stimulation threshold can be less than 50%, 70%, 80%, etc., of the intensity of the reference EMG signal. When the intensity of the electromyographic signal exceeds the stimulation threshold, it indicates that the genioglossus muscle has produced a relatively strong muscle contraction under electrical stimulation, meaning the intensity of the electrical stimulation received by the user may be too high. Therefore, the processing device 110 can correspondingly reduce the intensity of the electrical stimulation to avoid overstimulation of the muscle. In some embodiments, the processing device 110 can adjust the intensity of the electrical stimulation based on user commands. For example, the processing device 110 can display an adjustment window for the electrical stimulation to the user via a display device, allowing the user to adjust the intensity of the electrical stimulation according to their own perception of the stimulation. In some embodiments, the processing device 110 can set a safety threshold for the electrical stimulation to avoid overstimulation of the muscle.

[0077] It should be noted that the above description of method 300 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to method 300 under the guidance of this specification. However, these modifications and changes remain within the scope of this specification.

[0078] In some embodiments, method 300 may further include a step of dynamically adjusting the target movement. For example, after a user performs one or more cycles of genioglossus muscle training based on a training course, processing device 110 can dynamically adjust the target movement based on the training effect. As an example only, a user could train 3-5 times per week based on a training course, with a two-week cycle. Processing device 110 acquires information related to the training effect after the user completes one training cycle, thereby dynamically adjusting the target movement.

[0079] In some embodiments, information related to training effectiveness may include one or more of the feedback information related to the state of the genioglossus muscle described above. For example, the processing device 110 may acquire feedback information related to the genioglossus muscle's exertion ability and / or fatigue resistance (e.g., assessment parameters reflecting the genioglossus muscle's exertion ability and / or fatigue resistance) and compare it with historical feedback information to determine the training effectiveness. The historical feedback information may be historical feedback information related to the genioglossus muscle's exertion ability corresponding to one or more previous training cycles. As an example only, the processing device 110 may determine the improvement in the genioglossus muscle's exertion ability by comparing the current assessment parameters reflecting the genioglossus muscle's exertion ability with historical assessment parameters, and adjust the target movement according to the improvement, for example, adjusting the type, duration, and number of repetitions of the target movement.

[0080] In some embodiments, information related to training effectiveness may include heart rate or respiratory parameters during the user's sleep. The processing device 110 can acquire the user's heart rate or respiratory parameters during sleep and adjust the target action based on these parameters. For example, the processing device 110 can acquire data from a data acquisition device (e.g., during sleep). Figure 1 The electrocardiogram (ECG) signal acquisition device 152, respiratory signal acquisition device 153, etc., collect ECG and respiratory signals, and determine heart rate and respiratory parameters during the user's sleep based on these signals. The heart rate and respiratory parameters during sleep can reflect the user's OSA improvement, and thus the training effect of the genioglossus muscle. Exemplary heart rate parameters may include electrocardiograms, heart rate curves, heart rate variability (HRV) curves, etc. Exemplary respiratory parameters may include respiratory rate, gas flow rate, respiratory disturbance index (AHI), etc. For example, the processing device 110 can acquire physiological signals collected by the sleep heart rate monitoring device during the user's sleep. The physiological signals collected by the sleep heart rate monitoring device may include the user's ECG signals, respiratory signals, blood oxygen saturation, etc. The processing device 110 can determine heart rate or respiratory parameters based on the physiological signals collected by the sleep heart rate monitoring device, thereby adjusting the target action based on these parameters.

[0081] Figure 4 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification. In some embodiments, method 400 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, one or more operations in method 400 can be performed by... Figure 1The processing device 110 and / or terminal device 140 shown are implemented. For example, method 400 may be stored in storage device 130 as instructions and invoked and / or executed by processing device 110 and / or terminal device 140. In some embodiments, step 330 in method 300 may be implemented by method 400. In some embodiments, method 400 may be used to generate feedback information related to the genioglossus muscle's ability to exert force (e.g., generating evaluation parameters reflecting the genioglossus muscle's ability to exert force). See reference... Figure 4 Method 400 may include the following steps.

[0082] Step 410: The processing device 110 can acquire the first amplitude information of the electromyographic signal.

[0083] In some embodiments, the electromyographic signal may be an electromyographic signal collected when a user performs a specific target action. The electromyographic signal corresponding to the specific target action can be used to obtain assessment parameters reflecting the force exertion ability of the genioglossus muscle. Therefore, the processing device 110 can generate feedback information related to the force exertion ability of the genioglossus muscle based on the electromyographic signal.

[0084] As an example only, the specific target action may include a tongue protrusion action. The processing device 110 can display the tongue protrusion action to the user via a display device and prompt the user to perform the tongue protrusion action to the maximum extent to achieve MVC (Multi-Version Convergence) and maintain the action for a certain period of time (e.g., 5 seconds). Further, the processing device 110 can acquire the electromyographic (EMG) signal of the genioglossus muscle collected by the acquisition device during the user's tongue protrusion action and calculate the first amplitude information of the EMG signal. The first amplitude information may include the average EMG amplitude or root mean square (RMS) of the EMG signal within a preset time period. The preset time period may be the middle of the action duration. For example, if the action duration is 5 seconds, the preset time period may be from the 2nd to the 4th second. The processing device can calculate the average EMG amplitude or RMS of the EMG signal within the 2nd to 4th second as the first amplitude information. The user's action within the preset time period can reach a relatively stable state, thereby improving the stability and accuracy of the acquired first amplitude information. Optionally, the user can perform the specific target action multiple times. The processing device 110 can acquire the corresponding reference electromyographic (EMG) signals and calculate the average EMG amplitude or root mean square (RMS) of each EMG signal within a preset time period. The average of the average EMG amplitude or RMS of multiple EMG signals is then used as the first amplitude information. By performing the specific target action multiple times and taking the average value, the stability and accuracy of the first amplitude information can be further improved.

[0085] Step 420: The processing device 110 can generate feedback information based on the first amplitude information.

[0086] In some embodiments, the feedback information may include assessment parameters reflecting the genioglossus muscle's ability to exert force. For example, as described above, the electromyographic signal is a signal collected when the user performs the target action and achieves MVC, and the first amplitude information includes the average electromyographic amplitude or root mean square (RMS) of the electromyographic signal over a preset time period. The average electromyographic amplitude or RMS can represent the limit of the genioglossus muscle's force exertion when the user performs the target action, and thus can be used as a parameter reflecting the genioglossus muscle's ability to exert force. Therefore, the processing device 110 can use the first amplitude information as feedback information (or assessment parameter) to reflect the genioglossus muscle's ability to exert force. For example, the processing device 110 can compare the first amplitude information with reference amplitude information to assess the genioglossus muscle's ability to exert force. As an example only, the reference amplitude information may be the amplitude information corresponding to other users (e.g., users with normal genioglossus muscle function) when performing the target action and achieving MVC. This amplitude information can serve as a reference standard for the current first amplitude information to determine whether the user's current ability to exert force has reached a normal level. As another example, the reference amplitude information can be historical amplitude information corresponding to when the user performs the target action and reaches MVC during historical training. This historical amplitude information can serve as a reference standard for the current first amplitude information to determine whether the user's current exertion ability has improved. As another example, the reference amplitude information can be amplitude information corresponding to when other OSA patients perform the target action and reach MVC. This amplitude information can serve as a reference standard for the current first amplitude information to determine the comparison between the user's current exertion ability and that of other OSA patients. Further, the processing device 110 can generate feedback information related to the exertion ability of the genioglossus muscle based on the evaluation results. For example, the processing device 110 can generate an exertion ability value based on the comparison result between the first amplitude information and the reference amplitude information, and use the exertion ability value as feedback information. As an example only, the exertion ability value of other users (e.g., users with normal genioglossus muscle function) can be 10 points. If the size of the first amplitude information is 80% of the reference amplitude information, then the current user's exertion ability value can be 8 points. For example, the processing device 110 can generate the proportion of the current user's exertion ability among the exertion abilities of other OSA patients based on the comparison result of the first amplitude information and the reference amplitude information, and use this proportion information as feedback information. As an example only, if the current user's first amplitude information is greater than the reference amplitude information of 80% of other OSA patients, the feedback information could be that the current user's exertion ability value exceeds that of 80% of OSA patients. For another example, the processing device 110 can determine the likelihood of the user experiencing OSA symptoms based on the comparison result of the first amplitude information and the reference amplitude information, and provide the user with feedback information regarding the likelihood of experiencing OSA symptoms. Yet another example, the processing device 110 can generate a subsequent training plan based on the comparison result of the first amplitude information and the reference amplitude information, and provide the subsequent training plan as feedback information to the user.

[0087] It should be noted that the above description of method 400 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to method 400 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the above description uses a specific target action as the tongue protrusion action; in some embodiments, the specific target action may also include other actions, such as tongue pushing upwards or pressing downwards.

[0088] Figure 5 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification. In some embodiments, method 500 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, one or more operations in method 500 can be performed by... Figure 1 The processing device 110 and / or terminal device 140 shown are implemented. For example, method 500 may be stored in storage device 130 as instructions and invoked and / or executed by processing device 110 and / or terminal device 140. In some embodiments, step 330 in method 300 may be implemented by method 500. In some embodiments, method 500 may be used to generate feedback information related to the fatigue resistance of the genioglossus muscle. (See also...) Figure 5 Method 500 may include the following steps.

[0089] Step 510: The processing device 110 can acquire the frequency information of the electromyographic signal.

[0090] In some embodiments, the electromyographic signal may be an electromyographic signal collected when a user performs a specific target action. The electromyographic signal corresponding to the specific target action can be used to obtain assessment parameters reflecting the fatigue resistance of the genioglossus muscle. Therefore, the processing device 110 can generate feedback information related to the fatigue resistance of the genioglossus muscle based on the electromyographic signal.

[0091] As an example only, the specific target action may include a tongue-down movement. The processing device 110 can display the tongue-down movement to the user via a display device, prompting the user to perform the tongue-down movement to achieve MVC, and maintaining the movement for a certain period of time (e.g., 50 seconds). Further, the processing device 110 can acquire electromyographic (EMG) signals of the genioglossus muscle collected by a acquisition device during the user's tongue-down movement, and calculate the frequency information of the EMG signals. The frequency information may include the median frequency of the EMG signals. For example, the processing device 110 can divide the EMG signals into multiple segments (e.g., one segment every 5 seconds) and calculate the median frequency of each segment. Accordingly, the frequency information may include multiple median frequencies over multiple time periods.

[0092] Step 520: The processing device 110 can generate evaluation parameters reflecting the fatigue resistance of the genioglossus muscle based on the frequency information.

[0093] In some embodiments, the evaluation parameters may include parameters reflecting the changes in the median frequency over different time periods. For example, the processing device 110 can acquire curves showing the changes in multiple median frequencies over time over different time periods, and the slope of the curves can be used as a parameter reflecting the changes in the median frequency over different time periods. As an example only, when the slope of the curve is greater than a preset slope threshold, it can be determined that the muscle has entered a state of fatigue. As another example, one or more differences between a later median frequency and a earlier median frequency can be acquired according to chronological order, and these differences can be used as parameters reflecting the changes in the median frequency over different time periods. When the difference is greater than a preset difference threshold, it can be determined that the muscle has entered a state of fatigue. As yet another example, the percentage difference between a later median frequency and the first median frequency can be acquired, and this percentage difference can be used as a parameter reflecting the changes in the median frequency over different time periods. When the percentage difference is greater than a preset percentage threshold, it can be determined that the muscle has entered a state of fatigue. Through these parameters, it is possible to reflect whether the genioglossus muscle has entered a state of fatigue when the user maintains a target movement, thereby reflecting the fatigue resistance of the genioglossus muscle.

[0094] In step 530, the processing device 110 can generate feedback information related to the fatigue resistance of the genioglossus muscle based on the evaluation parameters.

[0095] In some embodiments, the processing device 110 can use the assessment parameters as feedback information. Users can determine the genioglossus muscle's fatigue resistance based on the feedback assessment parameters. In some embodiments, the processing device 110 can determine the time when the genioglossus muscle enters a fatigue state based on the assessment parameters and use this time as feedback information related to the genioglossus muscle's fatigue resistance. For example, the processing device 110 can determine whether the genioglossus muscle has entered a fatigue state based on the assessment parameters and obtain the time of entering the fatigue state, thereby feeding back the time of entering the fatigue state to the user. As another example, similar to the method for generating feedback information described in method 400, the processing device 110 can compare the assessment parameters with reference assessment parameters to assess the genioglossus muscle's fatigue resistance, thereby generating corresponding feedback information. The reference assessment parameters may include assessment parameters corresponding to other users (e.g., users with normal genioglossus muscle function), historical assessment parameters of the current user, assessment parameters of other OSA patients, etc. The processing device 110 can generate corresponding feedback information based on the comparison results between the assessment parameters and the reference assessment parameters. The feedback information may include the user's fatigue resistance value, the proportion of the current user's fatigue resistance to that of other OSA patients, the likelihood of the user experiencing OSA symptoms, and subsequent training plans.

[0096] It should be noted that the above description of method 500 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to method 500 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the above description uses a specific target action as a downward pressing motion of the tongue; in some embodiments, the specific target action may also include other actions, such as upward pushing of the tongue, forward protrusion of the tongue, etc.

[0097] Figure 6 This is a flowchart illustrating a method for generating feedback information according to some embodiments of this specification. In some embodiments, method 600 can be executed by processing logic, which may include hardware (e.g., circuitry, dedicated logic, programmable logic, microcode, etc.), software (instructions running on a processing device to execute hardware simulations), and any combination thereof. In some embodiments, one or more operations in method 600 can be performed by... Figure 1 The processing device 110 and / or terminal device 140 shown are implemented. For example, method 600 may be stored in storage device 130 as instructions and invoked and / or executed by processing device 110 and / or terminal device 140. In some embodiments, step 330 in method 300 may be implemented by method 600. In some embodiments, method 600 may be used to generate feedback information related to the fatigue resistance of the genioglossus muscle. (See also...) Figure 6Method 600 may include the following steps.

[0098] In step 610, the processing device 110 can acquire the second amplitude information of the electromyographic signal.

[0099] In some embodiments, the electromyographic signal may be an electromyographic signal collected when a user performs a specific target action. The electromyographic signal corresponding to the specific target action can be used to obtain assessment parameters reflecting the fatigue resistance of the genioglossus muscle. Therefore, the processing device 110 can generate feedback information related to the fatigue resistance of the genioglossus muscle based on the electromyographic signal.

[0100] As an example only, the specific target action may include a tongue-down movement. The processing device 110 can display the tongue-down movement to the user via a display device, prompting the user to perform the tongue-down movement to achieve MVC, and maintaining the movement for a certain period of time (e.g., 50 seconds). Further, the processing device 110 can acquire electromyographic (EMG) signals of the genioglossus muscle collected by a acquisition device during the user's tongue-down movement, and calculate second amplitude information of the EMG signals. The second amplitude information may include at least one of the average amplitude, integrated EMG amplitude, etc., of the EMG signal. For example, the processing device 110 can divide the EMG signal into multiple segments (e.g., one segment every 5 seconds), and calculate the average amplitude and / or integrated EMG amplitude for each segment. Accordingly, the second amplitude information may include multiple average amplitudes and / or integrated EMG amplitudes over multiple time periods.

[0101] In step 620, the processing device 110 can generate evaluation parameters reflecting the fatigue resistance of the genioglossus muscle based on the second amplitude information.

[0102] In some embodiments, the evaluation parameters may include parameters reflecting the changes in the second amplitude information over different time periods. For example, the processing device 110 may acquire curves showing the changes in multiple second amplitude information over time over different time periods, and the slope of the curves may be used as a parameter reflecting the changes in the second amplitude information over different time periods. As an example only, when the slope of the curve is greater than a preset slope threshold, it can be determined that the muscle has entered a state of fatigue. As another example, one or more differences between later and earlier second amplitude information may be acquired according to chronological order, and these differences may be used as parameters reflecting the changes in the second amplitude information over different time periods. When the difference is greater than a preset difference threshold, it can be determined that the muscle has entered a state of fatigue. As yet another example, the percentage difference between later second amplitude information and a first median frequency may be acquired, and this percentage difference may be used as a parameter reflecting the changes in the second amplitude information over different time periods. When the percentage difference is greater than a preset percentage threshold, it can be determined that the muscle has entered a state of fatigue. Through these parameters, it is possible to reflect whether the genioglossus muscle has entered a state of fatigue when the user maintains the target movement, thereby reflecting the fatigue resistance of the genioglossus muscle.

[0103] In step 630, the processing device 110 can generate feedback information related to the fatigue resistance of the genioglossus muscle based on the evaluation parameters.

[0104] In some embodiments, the processing device 110 can use the evaluation parameters as feedback information. Users can determine the genioglossus muscle's fatigue resistance based on the feedback evaluation parameters. In some embodiments, the processing device 110 can determine the time when the genioglossus muscle enters a fatigue state based on the evaluation parameters, and use this time as feedback information related to the genioglossus muscle's fatigue resistance. For example, the processing device 110 can determine whether the genioglossus muscle has entered a fatigue state based on the evaluation parameters and obtain the time of entering the fatigue state, thereby feeding back the time of entering the fatigue state to the user. As another example, similar to the method for generating feedback information described in method 500, the processing device 110 can compare the evaluation parameters with reference evaluation parameters to evaluate the genioglossus muscle's fatigue resistance, thereby generating corresponding feedback information. The reference evaluation parameters may include evaluation parameters corresponding to other users (e.g., users with normal genioglossus muscle function), historical evaluation parameters of the current user, evaluation parameters of other OSA patients, etc. The processing device 110 can generate corresponding feedback information based on the comparison results of the evaluation parameters and the reference evaluation parameters. The feedback information may include the user's fatigue resistance value, the proportion of the current user's fatigue resistance to that of other OSA patients, the likelihood of the user experiencing OSA symptoms, and subsequent training plans.

[0105] It should be noted that the above description of method 600 is for illustrative purposes only and does not limit the scope of this specification. Those skilled in the art can make various modifications and changes to method 600 under the guidance of this specification. However, these modifications and changes are still within the scope of this specification. For example, the above description uses a specific target action as a downward pressing motion of the tongue; in some embodiments, the specific target action may also include other actions, such as upward pushing of the tongue, forward protrusion of the tongue, etc.

[0106] The basic concepts have been described above. It is clear that the above disclosure is merely illustrative and does not constitute a limitation of this specification. Although not explicitly stated herein, various modifications, improvements, and corrections may be made to this specification by those skilled in the art. Such modifications, improvements, and corrections are suggested in this specification and therefore remain within the spirit and scope of the exemplary embodiments described herein.

[0107] Furthermore, this specification uses specific terms to describe embodiments thereof. For example, "an embodiment," "one embodiment," and / or "some embodiments" refer to a particular feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "an embodiment," "one embodiment," or "an alternative embodiment" in different locations throughout this specification do not necessarily refer to the same embodiment. Moreover, certain features, structures, or characteristics in one or more embodiments of this specification can be appropriately combined.

[0108] Furthermore, those skilled in the art will understand that various aspects of this specification can be described and illustrated in several patentable ways, including any new and useful combinations of processes, machines, products, or substances, or any new and useful improvements thereof. Accordingly, various aspects of this specification can be implemented entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. All of the above hardware or software may be referred to as a “data block,” “module,” “engine,” “unit,” “component,” or “system.” Furthermore, various aspects of this specification may be represented as a computer product located on one or more computer-readable media, including computer-readable program code.

[0109] Furthermore, unless expressly stated in the claims, the order of elements and sequences, the use of numbers and letters, or other names in this specification are not intended to limit the order of the processes and methods described herein. Although various examples have been discussed in the foregoing disclosure of some embodiments of the invention that are currently considered useful, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments; rather, the claims are intended to cover all modifications and equivalent combinations that conform to the spirit and scope of the embodiments described herein. For example, while the system components described above can be implemented using hardware devices, they can also be implemented solely using software solutions, such as installing the described system on an existing server or mobile device.

[0110] Similarly, it should be noted that, in order to simplify the description disclosed herein and thus aid in the understanding of one or more embodiments of the invention, the foregoing description of embodiments in this specification may sometimes combine multiple features into a single embodiment, drawing, or description thereof. However, this method of disclosure does not imply that the subject matter of this specification requires more features than those mentioned in the claims. In fact, the embodiments contain fewer features than all the features of a single embodiment disclosed above.

[0111] In some embodiments, numbers describing the quantity of components and attributes are used. It should be understood that such numbers used in the description of embodiments are modified in some examples by terms such as "approximately," "approximately," or "generally." Unless otherwise stated, "approximately," "approximately," or "generally" indicates that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical data used in the specification and claims are approximate values, which may be changed depending on the characteristics required by individual embodiments. In some embodiments, the numerical data should take into account specified significant digits and employ general methods of digit reservation. Although the numerical ranges and data used to confirm their breadth of range in some embodiments of this specification are approximate values, in specific embodiments, such numerical values ​​are set as precisely as feasible.

Claims

1. A method for assessing the genioglossus muscle, comprising: Display the target movements of the human tongue using a display device; The acquisition device acquires electromyographic signals from a user performing tongue movements in reference to the target action. The acquisition device includes two electrodes that are spaced apart at least along the muscle fiber direction of the user's genioglossus muscle and are configured to acquire the electromyographic signals of the genioglossus muscle. as well as Based on the electromyographic signals, feedback information related to the state of the genioglossus muscle is generated.

2. The method according to claim 1, wherein generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal comprises: Obtain the first amplitude information of the electromyographic signal; The feedback information is generated based on the first amplitude information, and the feedback information includes assessment parameters reflecting the force exertion ability of the genioglossus muscle.

3. The method according to claim 2, wherein the first amplitude information includes the average electromyographic amplitude or root mean square of the electromyographic signal within a preset time period.

4. The method according to claim 1, wherein generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal includes: Obtain the frequency information of the electromyographic signal; Based on the frequency information, evaluation parameters reflecting the fatigue resistance of the genioglossus muscle are generated; as well as Feedback information related to the fatigue resistance of the genioglossus muscle is generated based on the evaluation parameters.

5. The method according to claim 4, wherein the frequency information includes the median frequency of the electromyographic signal, and the evaluation parameters include parameters reflecting the change of the median frequency over different time periods.

6. The method according to claim 1, wherein generating feedback information related to the state of the genioglossus muscle based on the electromyographic signal comprises: Obtain the second amplitude information of the electromyographic signal; Based on the second amplitude information, evaluation parameters reflecting the fatigue resistance of the genioglossus muscle are generated; as well as Feedback information related to the fatigue resistance of the genioglossus muscle is generated based on the evaluation parameters.

7. The method according to claim 6, wherein the second amplitude information includes at least one of the average amplitude of the electromyographic signal and the integrated electromyographic amplitude, and the evaluation parameters include parameters reflecting the changes of the second amplitude information over different time periods.

8. The method according to claim 1, wherein the feedback information includes electrical stimulation applied to the genioglossus muscle by the electrical stimulation module.

9. The method of claim 8, wherein the electrical stimulation module includes the two electrodes configured to apply the electrical stimulation to the genioglossus muscle.

10. The method of claim 8, wherein the electrical stimulation module comprises two second electrodes arranged at least along the muscle fiber direction of the genioglossus muscle and configured to apply the electrical stimulation to the genioglossus muscle.

11. The method according to claim 1, wherein the feedback information includes information prompting the user whether the action performed has been completed, wherein, The feedback information is generated based on the amplitude or frequency information of the electromyographic signal.

12. The method according to claim 1, wherein the feedback information includes information identifying the action actually performed by the user, wherein, The feedback information is generated based on one or more parameters related to the shape of the electromyographic signal.

13. The method according to claim 1, further comprising dynamically adjusting the target action.

14. The method according to claim 13, wherein dynamically adjusting the target action comprises: Obtain the user's heart rate or respiratory parameters during sleep; as well as The target action is adjusted based on the heart rate or respiratory parameters.

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