A method and system for controlling essential tremor, wearable smart device

Abstract

This application relates to the field of neuroelectric stimulation modulation technology, and further to a method and system for controlling essential tremor, and a wearable smart device. The method includes: acquiring a tremor signal from a user's wrist; performing a Hilbert transform on the tremor signal to determine its instantaneous phase; calculating the root mean square (RMS) value of the tremor signal within a current sliding window, where the RMS value characterizes the instantaneous energy level of the tremor signal; determining a target tremor amplitude level based on the RMS value and a pre-constructed tremor amplitude level table; determining the stimulation amplitude of the electrical stimulation based on the target tremor amplitude level; and controlling the stimulation electrodes to output electrical stimulation to the target peripheral nerve when the instantaneous phase is within the target phase interval, based on the stimulation amplitude. This method effectively solves the problems of limited inhibitory effect, overstimulation, or ineffective stimulation caused by using a fixed stimulation amplitude in existing technologies.

Description

Technical Field

[0001] This application relates to the field of neuroelectric stimulation modulation technology, and further to a method and system for controlling essential tremor, and a wearable smart device. Background Technology

[0002] Essential tremor, one of the most common movement disorders in clinical practice, is characterized by postural and action tremors of the upper limbs, severely limiting patients' daily living abilities such as writing and eating. Currently, routine interventions for this disease mainly rely on medication and surgery. However, drug treatment faces bottlenecks such as significant individual differences in efficacy, frequent adverse reactions from long-term use, and the tendency to develop tolerance. While deep brain stimulation (DBS) and other surgical procedures can achieve good tremor suppression for drug-resistant severe cases, their invasiveness and potential infection risks result in generally low patient acceptance.

[0003] In recent years, non-invasive peripheral nerve electrical stimulation technology has gradually become an emerging research direction in this field due to its advantages of being non-invasive and portable. However, existing technical solutions mostly use a fixed stimulation amplitude for electrical stimulation. This stimulation method not only easily leads to limited overall inhibitory effects, but also easily causes problems such as overstimulation or ineffective stimulation. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method and system for controlling essential tremor, as well as a wearable smart device, which effectively solves the problems of limited inhibition effect, overstimulation, or ineffective stimulation caused by using fixed stimulation amplitude for electrical stimulation in the prior art.

[0005] In a first aspect, this application provides a method for controlling idiopathic tremor, comprising: acquiring a tremor signal from a user's wrist; performing a Hilbert transform on the tremor signal to determine the instantaneous phase of the tremor signal; calculating the root mean square (RMS) value of the tremor signal within a current sliding window, wherein the RMS value is used to characterize the instantaneous energy level of the tremor signal; determining a target tremor amplitude level based on the RMS value and a pre-constructed tremor amplitude level table; determining the stimulation amplitude of electrical stimulation based on the target tremor amplitude level; and controlling the stimulation electrodes to output electrical stimulation to the target peripheral nerve when the instantaneous phase is within the target phase interval, based on the stimulation amplitude.

[0006] The above-described method for controlling idiopathic tremor acquires tremor signals from the user's wrist in real time and uses Hilbert transform to accurately calculate the instantaneous phase. It then combines this with the calculation of the root mean square (RMS) value of the tremor signal within the current sliding window to characterize the instantaneous energy level. Based on the RMS value and a pre-constructed tremor amplitude level table, the target tremor amplitude level is determined, and the stimulation amplitude of the electrical stimulation is determined accordingly. When the instantaneous phase is within the target phase range, the stimulation electrode is controlled to output electrical stimulation to the target peripheral nerve. This effectively solves the problems of limited inhibitory effect, overstimulation, or ineffective stimulation caused by using a fixed stimulation amplitude in existing technologies.

[0007] In one implementation, the target phase interval includes a first phase interval or a second phase interval, the stimulating electrode includes a first stimulating electrode or a second stimulating electrode, and the target peripheral nerve includes the radial nerve or the median nerve. When the instantaneous phase is within the target phase interval, the stimulating electrode is controlled to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude. Specifically, when the instantaneous phase is within the first phase interval, the first stimulating electrode is controlled to output electrical stimulation to the radial nerve according to the stimulation amplitude; when the instantaneous phase is within the second phase interval, the second stimulating electrode is controlled to output electrical stimulation to the median nerve according to the stimulation amplitude.

[0008] In one implementation, the process of constructing the tremor amplitude rating scale specifically includes: calculating the root mean square value of the tremor signal within multiple sliding windows; and determining the tremor amplitude rating scale based on the tremor assessment scale and the root mean square values ​​within the multiple sliding windows.

[0009] In one implementation, the stimulation amplitude of the electrical stimulation is determined based on the target tremor amplitude level, specifically including: when the target tremor amplitude level is the first level, the stimulation amplitude is determined to be a first preset value; when the target tremor amplitude level is not the first level, the stimulation amplitude is determined based on the target tremor amplitude level, a second preset value, and a third preset value.

[0010] The above-described method for controlling idiopathic tremor improves the accuracy of tremor state identification by constructing a tremor amplitude level table and quantifying and classifying tremor signals based on the root mean square values ​​of tremor signals within multiple sliding windows in conjunction with a tremor assessment scale. It establishes a mapping relationship between stimulus amplitude and the tremor amplitude level table, and then employs a graded control strategy. When the target tremor amplitude level is at level one, the first preset value is directly used; when it is not at level one, the stimulus amplitude is dynamically determined by combining the second and third preset values. This achieves adaptive matching of stimulus parameters, ensuring effective tremor suppression and improving the user experience.

[0011] In one implementation, a Hilbert transform is performed on the jitter signal to determine the instantaneous phase of the jitter signal. Specifically, this includes: performing a Hilbert transform on the jitter signal to obtain the transformed jitter signal; and determining the instantaneous phase based on the jitter signal before and after the transform.

[0012] In one implementation, the instantaneous phase is either the instantaneous phase after a moving average filter or the instantaneous phase without a moving average filter.

[0013] In one implementation, the jitter signal is either a pre-processed jitter signal or an unprocessed jitter signal; the pre-processing includes bandpass filtering, detrending, and outlier removal.

[0014] In one implementation, the method further includes: performing power spectral density analysis on the tremor signals within all sliding windows to determine the power spectral density curve corresponding to each sliding window; and determining the dominant tremor frequency within each sliding window based on the power spectral density curve and the target frequency band of physiological tremor.

[0015] In one implementation, the method further includes: calculating the tremor inhibition rate within the current tremor cycle based on the first tremor power and the second tremor power within the current tremor cycle; adjusting the target phase interval and stimulation amplitude when the tremor inhibition rate within multiple consecutive tremor cycles is less than a fourth preset value; the first tremor power and the second tremor power are both integral values ​​of the power spectral density of the tremor signal in the target frequency band, wherein the time period corresponding to the first tremor power is the time period during which no electrical stimulation is output, and the second tremor power is the time period during which electrical stimulation is output.

[0016] The above-described idiopathic tremor control method calculates the tremor suppression rate by comparing the first tremor power during the non-electrical stimulation period and the second tremor power during the electrical stimulation period within the current tremor cycle. This allows for real-time quantitative evaluation of the tremor suppression effect of electrical stimulation. When the tremor suppression rate is less than a fourth preset value in multiple consecutive tremor cycles, the target phase interval and stimulation amplitude are automatically adjusted. This adaptive adjustment mechanism based on tremor suppression rate feedback can dynamically optimize stimulation parameters, ensuring effective suppression under different tremor states and further improving the accuracy and robustness of tremor control.

[0017] In one implementation, it also includes: real-time monitoring of the contact impedance between the stimulation electrode and the user's skin; when the contact impedance is greater than a reference value and the difference between the contact impedance and the reference value is greater than a fifth preset value, adjusting the stimulation amplitude.

[0018] In one implementation, the electrical stimulation is in the form of a pulse train; the stimulation frequency of the pulse train is 150 Hz, and the type of the pulse train is a symmetrical biphasic rectangular pulse.

[0019] Secondly, this application provides an idiopathic tremor control system, comprising: an acquisition module configured to acquire a tremor signal from a user's wrist; a processing module configured to: perform a Hilbert transform on the tremor signal to determine the instantaneous phase of the tremor signal; calculate the root mean square value of the tremor signal within a current sliding window, the root mean square value being used to characterize the instantaneous energy level of the tremor signal; determine a target tremor amplitude level based on the root mean square value and a pre-constructed tremor amplitude level table; determine the stimulation amplitude of electrical stimulation based on the target tremor amplitude level; and an electrical stimulation module configured to, when the instantaneous phase is within the target phase interval, control the stimulation electrodes to output electrical stimulation to the target peripheral nerve based on the stimulation amplitude.

[0020] Thirdly, this application also provides a wearable smart device, comprising: a sensor for acquiring tremor signals from a user's wrist; a stimulation electrode whose stimulation points correspond to the surface stimulation points of the user's radial and median nerves; and a controller connected to the sensor and the stimulation electrode, and configured to perform the steps of any of the above-implemented idiopathic tremor control methods.

[0021] In one implementation, it also includes a voltage management module configured to control the stimulation electrodes and controller to enter a sleep state when the user is in a non-vibration state.

[0022] In one implementation, the voltage management module is configured to monitor the remaining power of the wearable smart device in real time, and control the sensors, stimulation electrodes and controller to enter a sleep state when the remaining power is less than a preset power.

[0023] The wearable smart devices mentioned above monitor the remaining power of the wearable smart devices in real time through the voltage management module. When the remaining power is less than the preset power, the module automatically sends instructions to the sensors, stimulation electrodes and controller to put them into a sleep state. This low power protection mechanism can effectively prevent abnormal shutdown or data loss of the device due to power depletion, ensure the safe shutdown of the system, significantly reduce unnecessary power consumption, extend the standby time of the device under low power conditions, and improve the user's wearing experience and device reliability.

[0024] Fourthly, this application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory, which, when executed by the processor, implements the steps of any of the above-described idiopathic tremor control methods.

[0025] Fifthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described methods for controlling idiopathic tremor.

[0026] Sixthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described idiopathic tremor control methods.

[0027] Compared with the prior art, the present invention has any of the following beneficial effects: 1. By acquiring the tremor signal from the user's wrist in real time and accurately calculating its instantaneous phase using Hilbert transform, the instantaneous energy level is characterized by the root mean square value of the tremor signal within the current sliding window. Then, based on the root mean square value and a pre-constructed tremor amplitude level table, the target tremor amplitude level is determined, and the stimulation amplitude of the electrical stimulation is determined accordingly. When the instantaneous phase is within the target phase range, the stimulation electrode is controlled to output electrical stimulation to the target peripheral nerve. This effectively solves the problems of limited inhibition effect, overstimulation, or ineffective stimulation caused by using a fixed stimulation amplitude in the existing technology.

[0028] 2. By constructing a tremor amplitude level table, and using the root mean square value of tremor signals within multiple sliding windows in conjunction with a tremor assessment scale for quantitative grading, the accuracy of tremor state identification is improved. A mapping relationship between stimulus amplitude and the tremor amplitude level table is established, and a graded control strategy is adopted on this basis. When the target tremor amplitude level is the first level, the first preset value is directly used; when it is not the first level, the stimulus amplitude is dynamically determined by combining the second and third preset values. This achieves adaptive matching of stimulus parameters, ensures effective tremor suppression, and improves the user experience.

[0029] 3. By calculating the first tremor power during the period without electrical stimulation and the second tremor power during the period with electrical stimulation in the current tremor cycle, the tremor inhibition rate is obtained to quantitatively evaluate the tremor inhibition effect of electrical stimulation in real time. When the tremor inhibition rate is less than the fourth preset value in multiple consecutive tremor cycles, the target phase interval and stimulation amplitude are automatically adjusted. This adaptive adjustment mechanism based on tremor inhibition rate feedback can dynamically optimize stimulation parameters to ensure that an effective inhibition effect can be maintained in different tremor states, further improving the accuracy and robustness of tremor control.

[0030] 4. The voltage management module monitors the remaining power of the wearable smart device in real time. When the remaining power is less than the preset power, it automatically sends instructions to the sensor, stimulation electrode and controller to put them into a sleep state. This low power protection mechanism can effectively prevent abnormal shutdown or data loss of the device due to power depletion, ensure the safe shutdown of the system, significantly reduce unnecessary power consumption, extend the standby time of the device under low power conditions, and improve the user's wearing experience and device reliability. Attached Figure Description

[0031] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0032] Figure 1 This diagram illustrates a structural block diagram of a wearable smart device according to an embodiment of this application. Figure 2 This document illustrates a flowchart of constructing a tremor amplitude level table according to an embodiment of this application. Figure 3 A flowchart illustrating a mapping between stimulus amplitude and tremor amplitude level table provided in an embodiment of this application is shown; Figure 4 A flowchart of an essential tremor control method provided in an embodiment of this application is shown; Figure 5 A flowchart illustrating an embodiment of this application for adjusting a target phase range and stimulus amplitude is shown. Detailed Implementation

[0033] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0034] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0035] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0036] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0037] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0038] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0039] Essential tremor, one of the most common movement disorders in clinical practice, is characterized by postural and action tremors of the upper limbs, severely limiting patients' daily living abilities such as writing and eating. Currently, routine interventions for this disease mainly rely on medication and surgery. However, drug treatment faces bottlenecks such as significant individual differences in efficacy, frequent adverse reactions from long-term use, and the tendency to develop tolerance. While deep brain stimulation (DBS) and other surgical procedures can achieve good tremor suppression for drug-resistant severe cases, their invasiveness and potential infection risks result in generally low patient acceptance.

[0040] In recent years, non-invasive peripheral nerve electrical stimulation technology has gradually become an emerging research direction in this field due to its advantages of being non-invasive and portable. However, existing technical solutions mostly use a fixed stimulation amplitude for electrical stimulation. This stimulation method not only easily leads to limited inhibitory effects, but also easily causes problems such as overstimulation or ineffective stimulation.

[0041] Therefore, there is an urgent need in this field for a method to control idiopathic tremor that can dynamically adjust the amplitude of stimulation in order to solve problems such as limited inhibition, overstimulation, and ineffective stimulation.

[0042] The following is a detailed explanation with reference to the accompanying diagram: Reference Appendix Figure 1 The diagram illustrates a structural block diagram of a wearable smart device according to an embodiment of this application. The wearable device employs a wristband structure, allowing it to be worn on the user's wrist and provide electrical stimulation to suppress the user's essential tremor. Figure 1 As shown, the wearable smart device 100 includes: a sensor 110, a stimulation electrode 120, a controller 130, and a voltage management module 140.

[0043] Sensor 110 is used to collect tremor signals from the user's wrist and send them to controller 130. Sensor 110 can be an inertial sensor or a gyroscope (the gyroscope can be a three-axis microelectromechanical system gyroscope), meaning that sensor 110 actually collects the angular velocity signal of the user's wrist when it is in a tremor state. The sampling rate of sensor 110 is not less than 100Hz to ensure effective capture of the typical frequency range of 4~12Hz for essential tremor. When sensor 110 is a gyroscope, the sensitive axis of the gyroscope is aligned with the wearing direction of the wearable smart device 100 to ensure that the angular velocity signal truly reflects the wrist flexion, extension, pronation, and supination movements, that is, truly reflects the tremor state of the user's wrist.

[0044] The stimulation electrode 120 can be configured as a first stimulation electrode (or radial nerve surface stimulation electrode pair) and / or a second stimulation electrode (or median nerve surface stimulation electrode pair) as needed. The stimulation point of the first stimulation electrode corresponds to the surface stimulation point of the user's radial nerve, and the stimulation point of the second stimulation electrode corresponds to the surface stimulation point of the user's median nerve. That is, the first stimulation electrode is attached to the skin surface on the radial side of the flexor carpi radialis muscle, and the second stimulation electrode is attached to the skin surface proximal to the wrist crease between the palmaris longus tendon and the flexor carpi radialis tendon. Both the first and second stimulation electrodes can output a pulse train (a symmetrical biphasic rectangular pulse of 150 Hz frequency, 300 μs pulse width, and 50 μs phase interval) to achieve electrical stimulation of the user.

[0045] The controller 130 is connected to the sensor 110 via an SPI or I2C bus to acquire tremor signals from the user's wrist in real time, with a data transmission delay of less than 5ms. The controller 130 is also connected to the stimulation electrode 120. The controller 130 is used to execute the steps of the idiopathic tremor control method of this application and to control the stimulation electrode 120 to output electrical stimulation. The controller 130 may include a first sub-controller and a second sub-controller. The first sub-controller may be a microcontroller, which integrates a digital signal processing instruction set and a floating-point arithmetic unit to perform real-time calculation tasks such as bandpass filtering, power spectral density analysis, Hilbert transform, and root mean square value calculation. The second sub-controller is connected to the first sub-controller and the stimulation electrode 120, and is used to receive the real-time calculated tremor characteristic parameters, execute phase determination logic, generate stimulation trigger instructions, and control the stimulation amplitude (or stimulation output amplitude) of the electrical stimulation according to the target tremor amplitude level. The configuration of the first and second sub-controllers can be set according to user needs, or the controller 130 may be used alone to execute the steps of the idiopathic tremor control method of this application; this application does not limit this.

[0046] The voltage management module 140 includes a rechargeable lithium battery, a charging management circuit, and a voltage conversion circuit. In a non-vibration state where the target vibration amplitude level is A0, the voltage management module 140 can control the remaining modules or structures in the wearable smart device 100, except for the sensor 110, to enter a low-power sleep mode, thereby significantly extending the battery life of the wearable smart device 100 on a single charge.

[0047] The steps of the essential tremor control method of this application can be executed by the controller 130 of the wearable smart device 100 of the foregoing embodiment. Before describing the essential tremor control method of this application in detail, the construction of the tremor amplitude level table and the mapping between the stimulation amplitude of the electrical stimulation output by the stimulation electrode and the tremor amplitude level table will be explained first. Refer to the appendix below. Figure 2 This illustrates a flowchart of constructing a tremor amplitude level table according to an embodiment of this application. Figure 2 As shown, it includes: S200 calculates the root mean square value of the flutter signal within multiple sliding windows.

[0048] S210, determine the tremor amplitude rating scale based on the tremor assessment scale and the root mean square values ​​within multiple sliding windows.

[0049] Continue to refer to the appendix Figure 3 This illustrates a flowchart of a mapping between stimulus amplitude and tremor amplitude levels, provided in an embodiment of this application. Figure 3 As shown, it includes: S300: When the target tremor amplitude level is the first level, the stimulation amplitude is determined to be the first preset value.

[0050] S310, when the target tremor amplitude level is not the first level, determine the stimulus amplitude based on the target tremor amplitude level, the second preset value, and the third preset value.

[0051] The sensor 110 acquires tremor signals from the user's wrist, and analyzes these signals using a sliding window (or sliding time window). The sliding window length is set to 500 ms, containing 50 sampling points. The sliding window step size is set to 100 ms, advancing 10 sampling points with each update. These sliding window parameters ensure real-time computation while also considering frequency resolution and statistical stability. Of course, the sliding window length and step size can be adjusted as needed; this application does not impose any limitations on this.

[0052] This application allows for preprocessing of the acquired tremor signals, or no preprocessing is required, depending on user needs. The root mean square (RMS) values ​​of the tremor signals within multiple sliding windows are calculated. These RMS values ​​characterize the instantaneous energy level of the tremor signals and are positively correlated with the amplitude of the angular velocity. The computational complexity of the RMS values ​​is low, making them suitable for real-time execution on embedded platforms such as wearable smart devices. For example, suppose any sliding window contains N sampling points, and the sequence of tremor signals within that window is {x1, x2, ..., x...}. n}, then the root mean square value The calculation formula is: .

[0053] During the initial calibration process using a wearable smart device, the user maintains a resting posture and simulates movements with varying degrees of tremor. The wearable smart device 100 can collect the root mean square (RMS) values ​​of the tremor signals at different tremor amplitudes. For example, when the user is in a resting posture, the RMS values ​​of the tremor signals within multiple sliding windows can be calculated. Similarly, as the user's tremor amplitude increases sequentially, the RMS values ​​of the tremor signals within multiple sliding windows for each amplitude can be calculated. This results in multiple RMS values ​​for different tremor amplitudes (i.e., the distribution range of RMS values ​​for different tremor degrees). Based on these multiple RMS values ​​for different tremor amplitudes, and combined with the scoring logic of the TETRAS tremor assessment scale, the tremor amplitude can be divided into multiple levels, thus obtaining a tremor amplitude level table. The process of dividing into multiple levels essentially involves determining the grading thresholds between each tremor amplitude (i.e., each tremor amplitude corresponds to a range of RMS values) based on the multiple RMS values ​​for different tremor amplitudes, thereby obtaining the tremor amplitude level table.

[0054] The number of levels in the tremor amplitude rating table can be set according to user needs. This can be achieved by obtaining multiple root mean square values ​​for different tremor amplitudes and classifying them; this application does not limit this. Furthermore, the tremor amplitude ratings of this application can be customized for each user. That is, after using the wearable smart device, each user can customize their own personalized tremor amplitude rating table following the aforementioned process. For example, the tremor amplitude rating table of this application can include a first level, a second level, a third level, a fourth level, and a fifth level, which can be represented as A0, A1, A2, A3, and A4, respectively. The first level, A0, can represent a resting state with no tremor or extremely weak tremor, while the second levels, A1 to the fifth level, A4, can correspond to mild tremor, moderate tremor, severe tremor, and profound tremor, respectively.

[0055] During the initial calibration process using the wearable smart device 100, users can also calibrate the stimulation amplitude. For example, after the user puts on the wearable smart device 100 and enters the calibration process, the wearable smart device 100 can gradually increase the stimulation amplitude of the stimulation electrode 120. When the user just perceives a slight tingling sensation on the skin, the stimulation amplitude at this time can be set to the second preset value (or sensory threshold T). Before the user perceives subjective discomfort or experiences slight muscle twitching, the stimulation amplitude at this time can be set to the third preset value (or maximum tolerance threshold). The second and third preset values ​​can be stored in non-volatile memory as reference parameters for subsequent dynamic amplitude adjustment.

[0056] Taking the tremor amplitude rating scale, which includes levels one, two, three, four, and five, as an example, the mapping between the stimulus amplitude of this application and the tremor amplitude rating scale can be determined by the following formula: I_stim = .

[0057] Where I_stim is the stimulus amplitude, and L represents the first level A0, second level A1, third level A2, fourth level A3, and fifth level A4. Specifically, L = 0 corresponds to the first level A0; L = 1 corresponds to the second level A1; L = 2 corresponds to the third level A2; L = 3 corresponds to the fourth level A3; and L = 4 corresponds to the fifth level A4. T is the second preset value. This is the third preset value. In the aforementioned formula, when L is 0, the stimulus amplitude is the first preset value, which is 0. Under the aforementioned formula, the adjustment step size of the stimulus amplitude is... .

[0058] Similarly, the process of constructing the mapping between stimulus amplitude and tremor amplitude levels in this application can also be adjusted according to user needs. For example, if the tremor amplitude level table includes levels one, two, three, four, five, and six, the corresponding formula can be: I_stim = .

[0059] Where L = 0 corresponds to level 1 A0. L = 1 corresponds to level 2 A1. L = 2 corresponds to level 3 A2. L = 3 corresponds to level 4 A3. L = 4 corresponds to level 5 A4. L = 5 corresponds to level 6 A5. Therefore, when the tremor amplitude level table includes more levels, the aforementioned formula can be adjusted using the same principle to construct a mapping between stimulus amplitude and the tremor amplitude level table. Under the aforementioned formula, the adjustment step size of the stimulus amplitude is: .

[0060] After completing the construction of the tremor amplitude level table and the mapping between stimulus amplitude and the tremor amplitude level table, the corresponding stimulus amplitude can be determined according to the target tremor amplitude level when the user experiences tremor, thereby achieving dynamic control of the stimulus amplitude.

[0061] This application embodiment constructs a tremor amplitude level table and quantifies and grades tremor states based on the root mean square values ​​of tremor signals within multiple sliding windows combined with a tremor assessment scale, thereby improving the accuracy of tremor state identification. It establishes a mapping relationship between stimulus amplitude and the tremor amplitude level table, and on this basis, adopts a graded control strategy. When the target tremor amplitude level is at the first level, the first preset value is directly used; when it is not at the first level, the stimulus amplitude is dynamically determined by combining the second and third preset values. This achieves adaptive matching of stimulus parameters, ensuring effective tremor suppression and improving the user experience.

[0062] The overall workflow of this application is described below, with reference to the appendix. Figure 4 The diagram illustrates a flowchart of an essential tremor control method provided in an embodiment of this application. Figure 4 As shown, it includes: S400 acquires the tremor signal from the user's wrist and performs a Hilbert transform on the tremor signal to determine the instantaneous phase of the tremor signal.

[0063] S410, calculate the root mean square value of the tremor signal within the current sliding window. The root mean square value is used to characterize the instantaneous energy level of the tremor signal.

[0064] S420, determine the target tremor amplitude level based on the root mean square value and a pre-constructed tremor amplitude level table; S430 determines the stimulation amplitude of electrical stimulation based on the target tremor amplitude level.

[0065] S440: When the instantaneous phase is within the target phase range, the stimulation electrode is controlled to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude.

[0066] The tremor signal from the user's wrist is acquired from sensor 110 and processed using a sliding window. Then, a Hilbert transform is applied to the tremor signal to obtain the transformed tremor signal. The instantaneous phase is determined based on the tremor signal before and after the transform. For example, let the tremor signal before the transform be... The transformed flutter signal is .according to and Constructing analytic signals .in, , The imaginary unit. Analytical signal. instantaneous amplitude and instantaneous phase Calculated using the following two formulas respectively: , .

[0067] Calculated instantaneous phase The value range of is [-π / 2, π / 2]. By analyzing... and Quadrant determination is performed based on the sign of the instantaneous phase. Mapping to the interval from 0 to 2π yields the complete instantaneous phase of the tremor motion. At this point, the instantaneous phase of the complete tremor motion can be determined. Whether to perform moving average filtering or not depends on the user's needs. The window size for moving average filtering is set to 5 sampling points, which can be used to smooth the instantaneous phase curve and eliminate phase jumps and glitches caused by local noise in the signal.

[0068] Instantaneous phase After the aforementioned processing, the instantaneous phase is established. The correspondence between the kinematic extreme points and the tremor motion cycle. For example, when the instantaneous phase... When the value is π / 2, it corresponds to the moment of maximum angular velocity during wrist flexion, i.e., the peak of the angular velocity wave of the tremor. When the instantaneous phase... When the value is 3π / 2, it corresponds to the moment of maximum angular velocity of wrist extension movement, that is, the trough of angular velocity of tremor.

[0069] Through the root mean square value The calculation formula is used to calculate the root mean square (RMS) value of the tremor signal within the current sliding window. Based on this RMS value and the tremor amplitude level table, the user's target tremor amplitude level is determined. The target tremor amplitude level is then substituted into the mapping formula between stimulation amplitude and the tremor amplitude level table from the aforementioned embodiment to determine the stimulation amplitude of the electrical stimulation. When the instantaneous phase is within the target phase interval, a stimulation trigger command is generated for the target peripheral nerve. This stimulation trigger command instructs the stimulation electrode to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude. The target peripheral nerves include the radial nerve and the median nerve; that is, the stimulation electrode can output electrical stimulation to the radial nerve and / or the median nerve according to the stimulation trigger command.

[0070] This application embodiment acquires the tremor signal from the user's wrist in real time and uses Hilbert transform to accurately calculate its instantaneous phase. It then combines the calculation of the root mean square value of the tremor signal within the current sliding window to characterize the instantaneous energy level. Based on the root mean square value and a pre-constructed tremor amplitude level table, it determines the target tremor amplitude level and accordingly determines the stimulation amplitude of the electrical stimulation. When the instantaneous phase is within the target phase range, it controls the stimulation electrode to output electrical stimulation to the target peripheral nerve. This effectively solves the problems of limited inhibition effect, overstimulation, or ineffective stimulation caused by using a fixed stimulation amplitude for electrical stimulation in the prior art.

[0071] In one embodiment of this application, the target phase interval includes a first phase interval or a second phase interval, the stimulating electrode includes a first stimulating electrode or a second stimulating electrode, and the target peripheral nerve includes the radial nerve or the median nerve. When the instantaneous phase is within the target phase interval, the stimulating electrode is controlled to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude. Specifically, when the instantaneous phase is within the first phase interval, the first stimulating electrode is controlled to output electrical stimulation to the radial nerve according to the stimulation amplitude; when the instantaneous phase is within the second phase interval, the second stimulating electrode is controlled to output electrical stimulation to the median nerve according to the stimulation amplitude.

[0072] The first phase interval is π / 2 ± 0.1π, and the second phase interval is 3π / 2 ± 0.1π. The first and second phase intervals do not overlap in the phase domain and correspond to the phase neighborhoods of the angular velocity peaks and troughs within the tremor motion cycle, respectively. The ±0.1π setting of the phase interval width is used to avoid stimulus triggering failure due to minute instantaneous phase fluctuations, thus improving the stability of stimulus delivery while ensuring phase locking accuracy.

[0073] During a single tremor cycle, the first stimulating electrode delivers electrical stimulation to the radial nerve during the first phase interval (or the first preset phase window), while the second stimulating electrode delivers electrical stimulation to the median nerve during the second phase interval (or the second preset phase window). In the remaining phase intervals, both the first and second stimulating electrodes remain dormant. This achieves precise synchronization between the stimulation delivery time and the tremor phase, avoiding ineffective energy output in non-sensitive phase intervals.

[0074] In one embodiment of this application, the method further includes: performing power spectral density analysis on the tremor signals within all sliding windows to determine the power spectral density curve corresponding to each sliding window; and determining the dominant tremor frequency within each sliding window based on the power spectral density curve and the target frequency band of physiological tremor.

[0075] After acquiring the tremor signal, power spectral density analysis can be performed on the tremor signals within all sliding windows to calculate the power spectral density curve corresponding to each sliding window. From the power spectral density curve, the peak frequency within the target frequency band of physiological tremor (4Hz to 12Hz) is extracted and recorded as the dominant tremor frequency at the current moment, thus determining the dominant tremor frequency within each sliding window. Because the sliding window step size is smaller than the window length, the frequency parameters can be smoothly and dynamically tracked and updated, ensuring that the system can still accurately lock onto the target frequency even when the tremor frequency shifts due to patient fatigue, emotional fluctuations, or changes in drug blood concentration.

[0076] Power spectral density analysis can be performed using the Thomson multicone method. This method applies a set of orthogonal discrete elongated spherical sequence window functions, which can obtain spectral estimation results with low variance and high frequency resolution under short data lengths, and is especially suitable for the analysis of non-stationary jitter signals.

[0077] Reference Appendix Figure 5 This illustrates a flowchart of an embodiment of the present application for adjusting a target phase interval and stimulus amplitude. Figure 5 As shown, it includes: S500 calculates the tremor suppression rate within the current tremor cycle based on the first and second tremor powers within the current tremor cycle.

[0078] S510: When the tremor inhibition rate is less than the fourth preset value in multiple consecutive tremor cycles, adjust the target phase interval and stimulation amplitude.

[0079] The first tremor power and the second tremor power are both integral values ​​of the power spectral density of the tremor signal in the target frequency band. The time period corresponding to the first tremor power is the time period when no electrical stimulation is output, and the second tremor power is the time period when electrical stimulation is output.

[0080] The tremor inhibition rate essentially characterizes the stimulation effect. Therefore, the power change of the tremor signal in the target frequency band can be monitored in real time before and after electrical stimulation to calculate the tremor inhibition rate. The formula for the tremor inhibition rate is: .in, The first vibration power, The first tremor power refers to the integral of the power spectral density of the tremor signal within the target frequency band of 4Hz to 12Hz during the stimulation dormancy window (or the period without electrical stimulation), representing the baseline tremor power in the uninterrupted state. The second tremor power refers to the integral of the power spectral density of the target frequency band within the same tremor cycle after stimulation is triggered (or the period of electrical stimulation), representing the residual tremor power in the intervention state.

[0081] When the tremor inhibition rate is less than a fourth preset value for multiple consecutive tremor cycles, the boundary value of the target phase interval can be automatically adjusted within an allowable range at a preset step size. The adjustment target is related to the stimulating electrode that outputs the electrical stimulation. For example, when the first stimulating electrode outputs electrical stimulation, the target phase interval is the first phase interval; similarly, when the second stimulating electrode outputs electrical stimulation, the target phase interval is the second phase interval. If both the first and second stimulating electrodes output electrical stimulation simultaneously, the target phase interval becomes both the first and second phase intervals. Finally, by adjusting the target phase interval, the inhibition rate can be restored to a more optimal trigger phase point at the desired level.

[0082] As can be seen from the formulas in the foregoing embodiments, the adjustment step size of the stimulus amplitude can include, but is not limited to, the number of levels in the tremor amplitude rating table. , , ...etc. The adjustment error is controlled within ±5%.

[0083] This application embodiment calculates the first tremor power during the non-output electrical stimulation period and the second tremor power during the output electrical stimulation period within the current tremor cycle to obtain the tremor suppression rate, thereby quantitatively evaluating the tremor suppression effect of electrical stimulation in real time. When the tremor suppression rate is less than a fourth preset value in multiple consecutive tremor cycles, the target phase interval and stimulation amplitude are automatically adjusted. This adaptive adjustment mechanism based on tremor suppression rate feedback can dynamically optimize stimulation parameters, ensuring that an effective suppression effect can be maintained under different tremor states, further improving the accuracy and robustness of tremor control.

[0084] In one embodiment of this application, the method further includes: real-time monitoring of the contact impedance between the stimulation electrode and the user's skin; and adjusting the stimulation amplitude when the contact impedance is greater than a reference value and the difference between the contact impedance and the reference value is greater than a fifth preset value.

[0085] The system monitors the contact impedance between the stimulation electrode and the user's skin in real time. If the detected contact impedance is greater than the reference value (which is the reference value of the contact impedance during initial calibration) and the difference between the contact impedance and the reference value is greater than the fifth preset value, the stimulation amplitude is automatically adjusted according to the impedance change ratio (that is, the margin of the increased output voltage of the electrical stimulation is automatically adjusted) to maintain a constant effective current intensity injected into the nerve tissue and eliminate stimulation dose drift caused by impedance changes.

[0086] In one embodiment of this application, the electrical stimulation is in the form of a pulse train. The stimulation frequency of the pulse train is 150 Hz, which is a commonly used clinical frequency for peripheral nerve sensory stimulation, effectively activating cutaneous sensory afferent nerve fibers without causing direct excitation of motor neurons. The pulse train is a symmetrical biphasic rectangular pulse, with a single-phase pulse width of 300 μs and a phase interval of 50 μs between positive and negative phases. The symmetrical biphasic pulse design ensures that the net charge injected into the nerve tissue is zero within a single stimulation cycle, effectively avoiding tissue damage and electrode polarization caused by charge accumulation.

[0087] In one embodiment of this application, the jitter signal is a pre-processed jitter signal or an unprocessed jitter signal; the pre-processing includes bandpass filtering, detrending, and outlier removal.

[0088] This application allows for either preprocessing or not preprocessing of the acquired tremor signal before performing any related processing (such as Hilbert transform, calculating root mean square value, constructing a tremor amplitude level table, calculating dominant tremor frequency, constructing a tremor amplitude level table and mapping to stimulus amplitude, etc.). Users can configure this according to their needs.

[0089] During preprocessing, the acquired tremor signal can first undergo bandpass filtering. The high-pass cutoff frequency of the bandpass filter is set to 3Hz to filter out low-frequency drift components introduced by slow wrist movements, gravity, and physiological activities such as breathing. The low-pass cutoff frequency of the bandpass filter is set to 15Hz to suppress high-frequency electromagnetic noise, electromyographic signal interference, and 50Hz power frequency interference, thereby extracting the effective tremor signal within the preset tremor frequency band.

[0090] Next, the tremor signal can be detrended. For example, the linear trend term in the signal can be fitted using the least squares method and then removed. This step can eliminate baseline drift caused by slight displacement of the wrist wearing position or slow changes in limb posture, avoiding its adverse effect on the accuracy of subsequent feature calculations (such as calculating instantaneous phase, calculating root mean square value, calculating dominant tremor frequency, etc.).

[0091] Then, outlier removal is performed on the vibration signal. For example, a statistical anomaly detection method based on the 3-standard-deviation criterion is used to identify abnormal sampling points caused by sudden interference such as bumps or electromagnetic pulses during the acquisition process, and the interpolation results of nearby normal sampling points are used to replace them, thereby ensuring the continuity and stability of the signal sequence.

[0092] This application also provides an idiopathic tremor control system, including: an acquisition module configured to acquire tremor signals from a user's wrist; a processing module configured to: perform a Hilbert transform on the tremor signals to determine the instantaneous phase of the tremor signals; calculate the root mean square (RMS) value of the tremor signals within a current sliding window, the RMS value being used to characterize the instantaneous energy level of the tremor signals; determine a target tremor amplitude level based on the RMS value and a pre-constructed tremor amplitude level table; determine the stimulation amplitude of electrical stimulation based on the target tremor amplitude level; and an electrical stimulation module configured to, when the instantaneous phase is within the target phase interval, control the stimulation electrodes to output electrical stimulation to the target peripheral nerve based on the stimulation amplitude.

[0093] This application embodiment acquires the tremor signal from the user's wrist in real time and uses Hilbert transform to accurately calculate its instantaneous phase. It then combines the calculation of the root mean square value of the tremor signal within the current sliding window to characterize the instantaneous energy level. Based on the root mean square value and a pre-constructed tremor amplitude level table, it determines the target tremor amplitude level and accordingly determines the stimulation amplitude of the electrical stimulation. When the instantaneous phase is within the target phase range, it controls the stimulation electrode to output electrical stimulation to the target peripheral nerve. This effectively solves the problems of limited inhibition effect, overstimulation, or ineffective stimulation caused by using a fixed stimulation amplitude for electrical stimulation in the prior art.

[0094] This application also provides a wearable smart device, including: a sensor for collecting tremor signals from a user's wrist; a stimulation electrode whose stimulation points correspond to the surface stimulation points of the user's radial and median nerves; and a controller connected to the sensor and the stimulation electrode, and configured to perform the steps of the idiopathic tremor control method of any of the above embodiments.

[0095] In one embodiment of this application, it further includes a voltage management module configured to control the stimulation electrode and the controller to enter a sleep state when the user is in a non-vibration state.

[0096] The detailed content of the embodiments of this application has been described in the foregoing embodiments, and will not be repeated here.

[0097] In one embodiment of this application, the voltage management module is configured to monitor the remaining power of the wearable smart device in real time, and control the sensor, stimulation electrode and controller to enter a sleep state when the remaining power is less than a preset power.

[0098] This application embodiment monitors the remaining power of the wearable smart device in real time through a voltage management module. When the remaining power is less than the preset power, it automatically sends instructions to the sensor, stimulation electrode and controller to put them into a sleep state. This low power protection mechanism can effectively avoid abnormal shutdown or data loss of the device due to power depletion, ensure safe system shutdown, significantly reduce unnecessary power consumption, extend the standby time of the device under low power conditions, and improve the user's wearing experience and device reliability.

[0099] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the steps of the essential tremor control method of any of the above embodiments.

[0100] The memory can be non-volatile memory (NVM), such as, but not limited to, semiconductor non-volatile memory, disk storage, or optical storage. Semiconductor non-volatile memory includes, but is not limited to, read-only memory (ROM) or flash memory, such as mask ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), NAND flash memory, or NOR flash memory.

[0101] Memory can also be volatile, including random access memory (RAM). RAM includes static random-access memory (SRAM) and dynamic random-access memory (DRAM). DRAM includes synchronous dynamic RAM (SDRAM) and double data rate SDRAM (DDR). With technological advancements, DDR includes, but is not limited to, DDR1, DDR2, DDR3, ..., DDR5, and may also include future versions such as DDR6.

[0102] A processor is a circuit with signal processing capabilities. In one example, a processor can be a circuit capable of reading and executing instructions; such as a central processing unit (CPU), microcontroller unit (MCU), microprocessor unit (MPU), graphics processing unit (GPU), or digital signal processor (DSP). In another example, a processor can achieve its processing capabilities through the logical relationships of hardware circuits, which can be fixed or reconfigurable; for example, a processor can be a dedicated processor, such as one implemented using an application-specific integrated circuit (ASIC), which achieves processing capabilities through the design of the logical relationships between components within the circuit; or a processor implemented using a programmable logic device (PLD), which achieves processing capabilities by configuring the logical relationships between logic devices through configuration files; for example, a processor implemented using a field-programmable gate array (FPGA). In another example, the processor can be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. This application is not limited to the type of processor.

[0103] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the idiopathic tremor control method of any of the above embodiments.

[0104] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the idiopathic tremor control method of any of the above embodiments.

[0105] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling essential tremor, characterized in that, include: Acquire vibration signals from the user's wrist; The Hilbert transform is applied to the tremor signal to determine the instantaneous phase of the tremor signal; Calculate the root mean square value of the tremor signal within the current sliding window, whereby the root mean square value is used to characterize the instantaneous energy level of the tremor signal; The target tremor amplitude level is determined based on the root mean square value and the pre-constructed tremor amplitude level table; The stimulation amplitude of the electrical stimulation is determined based on the target tremor amplitude level. When the instantaneous phase is within the target phase range, the stimulation electrode is controlled to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude.

2. The method for controlling idiopathic tremor according to claim 1, characterized in that, The target phase interval includes a first phase interval or a second phase interval, the stimulating electrode includes a first stimulating electrode or a second stimulating electrode, and the target peripheral nerve includes the radial nerve or the median nerve; when the instantaneous phase is within the target phase interval, the stimulating electrode is controlled to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude, specifically including: When the instantaneous phase is within the first phase interval, the first stimulation electrode is controlled to output electrical stimulation to the radial nerve according to the stimulation amplitude; When the instantaneous phase is within the second phase interval, the second stimulation electrode is controlled to output electrical stimulation to the median nerve according to the stimulation amplitude.

3. The method for controlling idiopathic tremor according to claim 1, characterized in that, The process of constructing the tremor amplitude rating scale specifically includes: Calculate the root mean square value of the flutter signal within multiple sliding windows; The tremor amplitude rating scale is determined based on the tremor assessment scale and the root mean square values ​​within multiple sliding windows.

4. The method for controlling idiopathic tremor according to claim 1, characterized in that, The step of determining the stimulation amplitude of the electrical stimulation based on the target tremor amplitude level specifically includes: When the target tremor amplitude level is the first level, the stimulation amplitude is determined to be a first preset value; When the target tremor amplitude level is not the first level, the stimulation amplitude is determined according to the target tremor amplitude level, the second preset value, and the third preset value.

5. The method for controlling idiopathic tremor according to claim 1, characterized in that, The step of performing a Hilbert transform on the tremor signal to determine the instantaneous phase of the tremor signal specifically includes: Perform a Hilbert transform on the flutter signal to obtain the transformed flutter signal; The instantaneous phase is determined based on the flutter signal before and after the transformation.

6. The method for controlling essential tremor according to any one of claims 1-5, characterized in that, The instantaneous phase is either the instantaneous phase after being filtered by a moving average or the instantaneous phase without being filtered by a moving average.

7. The method for controlling essential tremor according to any one of claims 1-5, characterized in that, The jitter signal is either a pre-processed jitter signal or an unprocessed jitter signal; the pre-processing includes bandpass filtering, detrending, and outlier removal.

8. The method for controlling essential tremor according to any one of claims 1-5, characterized in that, Also includes: Power spectral density analysis was performed on the flutter signals within all sliding windows to determine the power spectral density curve corresponding to each sliding window; Based on the power spectral density curve and the target frequency band of physiological tremor, the dominant tremor frequency within each sliding window is determined.

9. The method for controlling idiopathic tremor according to claim 8, characterized in that, Also includes: Calculate the tremor suppression rate within the current tremor cycle based on the first and second tremor powers within the current tremor cycle. When the tremor inhibition rate is less than the fourth preset value in multiple consecutive tremor cycles, the target phase interval and the stimulation amplitude are adjusted. Both the first tremor power and the second tremor power are integral values ​​of the power spectral density of the tremor signal in the target frequency band. The time period corresponding to the first tremor power is the time period during which no electrical stimulation is output, and the second tremor power is the time period during which electrical stimulation is output.

10. The method for controlling idiopathic tremor according to claim 1, characterized in that, Also includes: Real-time monitoring of the contact resistance between the stimulation electrode and the user's skin; When the contact impedance is greater than the reference value and the difference between the contact impedance and the reference value is greater than the fifth preset value, the stimulation amplitude is adjusted.

11. The method for controlling idiopathic tremor according to claim 1, characterized in that, The electrical stimulation is in the form of a pulse train; the stimulation frequency of the pulse train is 150 Hz, and the type of the pulse train is a symmetrical biphasic rectangular pulse.

12. A control system for idiopathic tremor, characterized in that, include: The acquisition module is configured to acquire tremor signals from the user's wrist. The processing module is configured to perform a Hilbert transform on the tremor signal to determine the instantaneous phase of the tremor signal; Calculate the root mean square (RMS) value of the tremor signal within the current sliding window, where the RMS value characterizes the instantaneous energy level of the tremor signal; determine the target tremor amplitude level based on the RMS value and a pre-constructed tremor amplitude level table; and determine the stimulation amplitude of the electrical stimulation based on the target tremor amplitude level. The electrical stimulation module is configured to control the stimulation electrodes to output electrical stimulation to the target peripheral nerve according to the stimulation amplitude when the instantaneous phase is within the target phase range.

13. A wearable smart device, characterized in that, include: A sensor, the sensor being used to collect tremor signals from the user's wrist; Stimulating electrodes, wherein the stimulation points of the stimulating electrodes correspond to the surface stimulation points of the user's radial nerve and median nerve; A controller, which is connected to both the sensor and the stimulation electrode, and is configured to perform the steps of the essential tremor control method according to any one of claims 1-11.

14. The wearable smart device according to claim 13, characterized in that, Also includes: The voltage management module is configured to control the stimulation electrodes and the controller to enter a sleep state when the user is in a non-vibration state.

15. The wearable smart device according to claim 14, characterized in that, The voltage management module is configured to monitor the remaining power of the wearable smart device in real time, and when the remaining power is less than a preset power, control the sensor, the stimulation electrode and the controller to enter a sleep state.

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