Self-adaptive vomiting stopping device and regulation and control method

Abstract

The invention provides a self-adaptive vomiting stopping device and a regulation and control method, the self-adaptive vomiting stopping device comprises a shell, a pulse wave detection unit, a discharge circuit, an electrode plate and a control unit, wrist strap assemblies are arranged at the two ends of the shell, the electrode plate is arranged at the bottom of the shell and connected with the discharge circuit, the electrode plate is tightly attached to the wrist acupuncture points of a user through the wrist strap assemblies, and the control unit is connected with the pulse wave detection unit. The pulse wave detection unit detects pulse signals of a user, and the control unit receives, analyzes and judges the detected pulse signals, controls the discharge circuit to change pulse current and acts on acupuncture points of the user through the electrode plates. According to the self-adaptive vomiting stopping device and the control method, the pulse wave detection unit is used for detecting the state of the user, the stimulation intensity is adjusted according to the personal condition of the user, the pulse wave of the user can be detected in the long-time using process, the heart rate information of the user is obtained, the stimulation intensity is adjusted in real time, and the self-adaptive vomiting stopping device can be used for stopping vomiting. The emesis symptom is effectively relieved, and the use effect is improved.

Description

Technical Field

[0001] This invention relates to the field of antiemetic devices, and in particular provides an adaptive antiemetic device and control method. Background Technology

[0002] Nausea and vomiting caused by pregnancy, motion sickness, seasickness, VR vertigo, surgery, cardiovascular and cerebrovascular diseases, chemotherapy drugs, etc., are common clinical symptoms. The mechanisms of these vomiting events are complex, but some scholars believe that the entire process is mainly regulated and controlled by the vomiting center. The vomiting center, located in the brainstem, is responsible for regulating the vomiting response. This area receives stimuli from multiple neural pathways, including stimuli from the pharynx, gastrointestinal tract, mediastinum, and higher cortical centers, visceral signals, visual and vestibular system signals, and stimulation from the chemoreceptor excitation area. All these pathways can stimulate the center to induce the vomiting reflex, leading to nausea and vomiting. Traditional Chinese medicine (TCM) acupuncture theory considers the Neiguan acupoint to be a highly effective point for relieving nausea and vomiting. Neiguan is an acupoint on the pericardium meridian, located on the inner forearm, approximately 2 inches above the wrist crease. TCM theory suggests that Neiguan has the following mechanisms and effects in relieving nausea and vomiting: stimulation of Neiguan can help harmonize the spleen and stomach, enhance digestive function, thereby reducing nausea and vomiting caused by spleen and stomach imbalances. Acupuncture or massage of the Neiguan acupoint can unblock the pericardium meridian, improve blood circulation, and relieve physical discomfort. Stimulation of the Neiguan acupoint helps calm the mind, reduce anxiety and tension, thereby reducing the impact of emotions on nausea and vomiting. Stimulation of the Neiguan acupoint can promote local and systemic blood circulation, enhancing the body's ability to adapt to discomfort. Stimulation of the Neiguan acupoint can effectively alleviate nausea and vomiting caused by various factors, such as pregnancy vomiting and chemotherapy-induced nausea. Clinically, many patients have experienced a significant reduction in nausea and vomiting frequency after receiving acupuncture or acupressure. In conclusion, the mechanism of vomiting is complex, and Traditional Chinese Medicine, by regulating the Neiguan acupoint, can alleviate nausea and vomiting symptoms to a certain extent, showing promising clinical application prospects.

[0003] While electronic anti-nausea devices have emerged on the market that use simulated bioelectric signals to periodically stimulate acupoints to alleviate vomiting symptoms, they are complex to use, inconvenient for single users, and offer limited intensity settings. Furthermore, they cannot adjust the stimulation intensity to suit individual user needs, potentially leading to adverse effects. Prolonged use can also cause desensitization to the constant stimulation intensity, rendering the desired effect ineffective. Summary of the Invention

[0004] Based on this, the present invention provides an adaptive antiemetic device and control method, which can adjust the stimulation intensity according to the user's individual situation and adjust the stimulation intensity in real time to effectively relieve vomiting symptoms and provide a better effect.

[0005] To achieve the above objectives, the present invention provides an adaptive antiemetic device, comprising a housing, a pulse wave detection unit, a discharge circuit, electrode pads, and a control unit; wristband assemblies are provided at both ends of the housing, the electrode pads are disposed at the bottom of the housing and connected to the discharge circuit, and the electrode pads are in close contact with the user's wrist acupoints through the wristband assemblies; the pulse wave detection unit detects the user's pulse signal, and the control unit receives and controls the discharge circuit to release an adjustable pulse current according to the detected pulse signal, which is then applied to the user's acupoints through the electrode pads.

[0006] Furthermore, the present invention provides an adaptive antiemetic control method, wherein the pulse wave measurement method using the aforementioned adaptive antiemetic device includes the following steps:

[0007] S110. Secure the housing to the user's wrist with a wristband so that the two electrodes are located on both sides of the Neiguan acupoint;

[0008] S120. Obtain effective body data of the user through the pulse wave detection unit and select appropriate stimulation intensity through the set calibration range. Use the pulse wave detection unit to measure the peak and trough of the pulse wave and save the data in the data storage chip.

[0009] S130. The device samples the pulse wave and analyzes the positions of the peaks and troughs, records them, and stores them in the data storage chip;

[0010] S140. Real-time acquisition of the user's heart rate variability. and rate of change of heart rate completion time ;

[0011] S150. User long press of button or user's heart rate variability. and rate of change of heart rate completion time If the range of variation exceeds 10% of the set threshold, the device will stop working.

[0012] Optionally, the pulse wave detection unit detects changes in blood caused by heartbeat using photoplethysmography (PPG) scanning, generating different LED light reflection signals. The pulse wave beats are converted into electrical signals by a photoelectric sensor, and then amplified, shaped, and filtered before being sent to a microcontroller for processing. During digital signal processing, the pulse wave signal is sampled at multiple points for fast Fourier transform calculation, converting the pulse wave data from the time domain to the frequency domain and processing it.

[0013] Optionally, the pulse wave measurement method includes the following steps:

[0014] S210. Pulse wave measurement detects changes in blood caused by heartbeat through photoplethysmography pulse wave scanning. The raw pulse wave signal processing circuit includes three parts: filtering, signal amplification, and waveform shaping.

[0015] The S220 microcontroller then performs digital signal processing on the hardware-processed data to reduce the influence of ambient light. It converts the pulse wave data from the time domain to the frequency domain for processing, uses a bandpass filter to filter out noise, and uses an inverse Fourier transform to convert the frequency domain data back to the time domain data.

[0016] S230. The peak reflects the state after one cardiac contraction, and the trough reflects the state after one cardiac diastole. Scale-invariant feature transformation is used to obtain the local features of pulse wave data and to find the positions of the peak and trough within the range.

[0017] S240. Construct four sets of scale spaces for pulse wave data, each with five layers, to help detect features at different scales;

[0018] S250. For each group of five layers, construct a Gaussian difference scale space. Subtract the adjacent scale spaces of these five layers to obtain a four-layer Gaussian difference scale space.

[0019] S260. Select locations where signal intensity changes significantly in the Gaussian difference scale space;

[0020] S270. When a continuous function is sampled, its true maximum or minimum value may actually lie between the sample points; therefore, it is necessary to fit an interpolation function to discrete numbers and then find the location of the extreme values ​​within it after improving accuracy.

[0021] Optionally, analysis can be performed using historical data and the most recently sampled data. The heart rate variability was obtained in real time at the intervals between the occurrences of each peak. The time from one peak to the next trough is equivalent to the time it takes for one heartbeat to complete. (Analysis) The rate of change of heartbeat completion time is obtained from the time it takes for each heartbeat to complete. .

[0022] Optionally, when the rate of change in heart rate Exceeding its upper threshold When the pulse frequency is reduced by 5% of the original pulse frequency, the heart rate variability is... Exceeding its lower threshold At this point, the pulse frequency is increased in steps of 5% of the original pulse frequency until the heart rate variability rate is reached. Within the normal range; when the rate of change of heart rate completion time Exceeding its upper threshold When the pulse width is reduced by 5% of the original pulse width, the rate of change of the heartbeat completion time is... Exceeding its lower threshold The pulse width is increased in increments of 5% of the original pulse width until the rate of change of heartbeat completion time is reached. It is within the normal range.

[0023] Optionally, the process of constructing the multi-scale space of pulse wave data includes:

[0024] First, the signal data is downsampled, so that the resolution of the data is gradually reduced.

[0025] Then, the data was sampled three times to obtain four sets of data including the original data;

[0026] Then, the four sets of data were smoothed, and this process was repeated four times to obtain 4*5 sets of data. Each of the original four sets of data was increased to five layers, resulting in four sets of five-layer scale spaces.

[0027] Optionally, Gaussian filtering is used to smooth the four sets of data, where, As a smoothing factor, Multiply by a scaling factor A new smoothing factor is obtained.

[0028] .

[0029] Optionally, locations where signal intensity changes significantly in the Gaussian difference scale space are selected; that is, places that may be peaks or troughs. At each data location in the difference scale, the value at that location is compared with its two adjacent values ​​in the current layer and its six values ​​in the upper and lower layers. During the comparison of adjacent scales, each group of four Gaussian difference layers is divided, and extreme point detection is performed on the two middle layers. If the value is greater than all its adjacent values, the location is selected as a peak; if the value is less than all its adjacent values, the location is selected as a trough.

[0030] Optionally, the location of the feature value in the entire sample is determined, the feature value location vectors at each scale are normalized to obtain four sets of normalized signal data feature value location vectors, and the locations of the peaks and troughs can be obtained by weighted bisecting the four sets of vectors, thus combining the feature values ​​at multiple scales.

[0031] Compared with the prior art, the technical advantages of the adaptive anti-emetic device and control method provided by the present invention are at least reflected in the following aspects:

[0032] Firstly, the magnetic wristband makes it easy for a single person to wear, and the adjustable tightness of the wristband ensures the electrode pads fit snugly against acupoints. It offers five adjustable levels for easy operation. A pulse wave detection unit monitors the user's condition, allowing for selection of an appropriate stimulation intensity, which can then be adjusted according to the user's individual needs to provide optimal results.

[0033] Secondly, during use, analysis is conducted using historical data and the latest sampled data. The heart rate variability was obtained in real time at the intervals between the occurrences of each peak. The time from one peak to the next trough is equivalent to the time it takes for one heartbeat to complete. (Analysis) The rate of change of heartbeat completion time is obtained from the time it takes for each heartbeat to complete. It accurately obtains the user's heart rate information and adjusts the stimulation intensity in real time to achieve the desired effect, making it suitable for long-term use. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a block diagram of the adaptive anti-emetic device provided by the present invention;

[0036] Figure 2 This is a flowchart of the adaptive antiemetic device control method provided by the present invention;

[0037] Figure 3 This is a flowchart of a preferred embodiment of the pulse wave measurement method provided by the present invention. Detailed Implementation

[0038] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0039] like Figure 1As shown, this invention presents an adaptive anti-nausea device and control method. The anti-nausea device includes a housing, which is divided into an upper housing and a lower housing. A cavity in the middle of the housing houses the control unit. The upper and lower housings are fixed together using ultrasonic welding. Detachable wristband assemblies are snapped onto both ends of the housing. The outer surface of the upper housing has a display interface and buttons for five position indicator lights, a charging indicator light, and a status indicator light. The inner surface of the upper housing has a limiting slot and screw holes for fixing the control unit. The control unit controls the wristband's operation and is fixed to the inner surface of the upper housing with screws. The control unit includes an MCU, a lithium battery, a power supply circuit, a charging circuit, a boost circuit, a voltage detection circuit, a discharge circuit, a pulse wave detection unit, a data storage chip, position indicator lights, a charging indicator light, and a status indicator light. The lower housing has two semi-circular electrode plates with protrusions connected to the control unit via spring pins, a charging interface for magnetic charging, and a pulse wave detection unit. The electrode plates are connected to the discharge circuit and act on the user's acupoints, stimulating them with an adjustable pulse current. Furthermore, the tightening of the magnetic wristband ensures the electrode plates are in close contact with the user's wrist acupoints. The control unit, located within the housing, analyzes and judges pulse wave data to control the pulse intensity. This invention features ease of operation, multiple intensity levels, and adaptability to the user's physical condition. A magnetic wristband facilitates single-person wear, and the adjustable wristband ensures the electrode pads adhere tightly to acupoints. Five adjustable intensity levels facilitate operation. A pulse wave detection unit monitors the user's condition, selects an appropriate stimulation intensity, and adjusts the intensity according to the user's individual needs to improve the effectiveness.

[0040] like Figure 2 As shown, the present invention provides an adaptive antiemetic control method. Using the aforementioned adaptive antiemetic device, the pulse wave measurement method includes the following steps:

[0041] S110. Secure the housing to the user's wrist with a wristband so that the two electrodes are located on both sides of the Neiguan acupoint;

[0042] S120. Obtain effective body data of the user through the pulse wave detection unit and select appropriate stimulation intensity through the set calibration range. Use the pulse wave detection unit to measure the peak and trough of the pulse wave and save the data in the data storage chip.

[0043] S130. The device samples the pulse wave and analyzes the positions of the peaks and troughs, records them, and stores them in the data storage chip;

[0044] S140. Real-time acquisition of the user's heart rate variability. and rate of change of heart rate completion time ;

[0045] S150. User long press of button or user's heart rate variability. and rate of change of heart rate completion time If the range of variation exceeds 10% of the set threshold, the device will stop working.

[0046] Specifically, during implementation, the casing is secured to the user's wrist with a wristband, positioning the two electrodes on either side of the Neiguan acupoint. Pressing and holding the button for 3 seconds powers on the device, activating the MCU microcontroller. After powering on, the gear settings can be configured via the button. Pressing and holding the button for 3 seconds powers on or off the device; after powering on, the gear settings can be configured via the button.

[0047] When powered on, the MCU obtains valid user data through the pulse wave detection unit and selects an appropriate stimulation intensity within a set calibration range. The pulse wave detection unit measures the peaks and troughs of the pulse wave and stores the data in the data storage chip.

[0048] The status indicator light on the right side of the button illuminates whenever the button is pressed and turns off when the button is released. When the device is powered on, the indicator light on the left side of the button remains constantly lit. The control unit periodically monitors the lithium battery level. When the lithium battery is low, the indicator light on the left side of the button will flash to remind the user to charge, and the device will not operate. During charging, the charging indicator light will illuminate red to indicate that charging is in progress, and will illuminate green to indicate that the lithium battery is fully charged when charging is complete.

[0049] The display screen has five indicator lights, each corresponding to a different stimulation voltage level, from 1 to 5. The level indicator is set as a Flag, with an initial value of 0. A single click increases the level and increments the Flag by one; a double click decreases the level and decrements the Flag by one. The Flag is set to zero when increasing the level from 5 and decreasing it from 1.

[0050] After selecting the gear, the MCU controls the boost circuit to pre-charge the capacitor via the PWM_BOOST signal waveform and stabilizes the capacitor voltage at the target value through voltage feedback from the voltage detection circuit. The MCU can control the discharge circuit via PWM_1 and PWM_2 signals to control the frequency and width of the pulses.

[0051] like Figure 3 As shown, in a preferred embodiment, the pulse wave measurement method includes the following steps:

[0052] S210. Pulse wave measurement detects changes in blood caused by heartbeat through photoplethysmography pulse wave scanning. The raw pulse wave signal processing circuit includes three parts: filtering, signal amplification, and waveform shaping.

[0053] The S220 microcontroller then performs digital signal processing on the hardware-processed data to reduce the influence of ambient light. It converts the pulse wave data from the time domain to the frequency domain for processing, uses a bandpass filter to filter out noise, and uses an inverse Fourier transform to convert the frequency domain data back to the time domain data.

[0054] S230. The peak reflects the state after one cardiac contraction, and the trough reflects the state after one cardiac diastole. Scale-invariant feature transformation is used to obtain the local features of pulse wave data and to find the positions of the peak and trough within the range.

[0055] S240. Construct four sets of scale spaces for pulse wave data, each with five layers, to help detect features at different scales;

[0056] S250. For each group of five layers, construct a Gaussian difference scale space. Subtract the adjacent scale spaces of these five layers to obtain a four-layer Gaussian difference scale space.

[0057] S260. Select locations where signal intensity changes significantly in the Gaussian difference scale space;

[0058] S270. When a continuous function is sampled, its true maximum or minimum value may actually lie between the sample points; therefore, it is necessary to fit an interpolation function to discrete numbers and then find the location of the extreme values ​​within it after improving accuracy.

[0059] During use, the device samples the pulse wave, analyzes the positions of the peaks and troughs, and records them in a data storage chip. The MCU analyzes the historical data and the most recently sampled data in real time. Heart rate variability was obtained by measuring the interval between the occurrences of each peak. The time from peak to trough of an adjacent wave can be equivalent to the time it takes for one heartbeat to complete. (Analysis) The rate of change of heartbeat completion time is obtained from the time it takes for each heartbeat to complete. During use, analysis is conducted using historical data and the most recently sampled data. The heart rate change rate is obtained in real time by measuring the interval between peaks, accurately obtaining the user's heart rate information, and adjusting the stimulation intensity in real time to achieve the expected effect, making it suitable for long-term use.

[0060] By acquiring the user's heart rate variability in real time and rate of change of heart rate completion time When the rate of change in heart rate Exceeding its upper threshold When the pulse frequency is reduced by 5% of the original pulse frequency, the heart rate variability is... Exceeding its lower threshold At this point, the pulse frequency is increased in steps of 5% of the original pulse frequency until the heart rate variability rate is reached. Within the normal range. When the rate of change in heart rate completion time... Exceeding its upper threshold When the pulse width is reduced by 5% of the original pulse width, the rate of change of the heartbeat completion time is... Exceeding its lower threshold The pulse width is increased in increments of 5% of the original pulse width until the rate of change of heartbeat completion time is reached. It is within the normal range.

[0061] User presses and holds button or user's heart rate variability and rate of change of heart rate completion time If the variation exceeds 10% of the set threshold, the MCU will stop outputting the PWM_BOOST signal, PWM_1 signal, and PWM_2 signal, and the device will stop working.

[0062] Pulse wave measurement detects changes in blood caused by heartbeat through photoplethysmography (PPG). The raw pulse wave signal processing circuit mainly includes three parts: filtering, signal amplification, and waveform shaping.

[0063] The microcontroller then performs digital signal processing on the hardware-processed data to reduce the impact of ambient light. During digital signal processing, the pulse wave data is converted from the time domain to the frequency domain and processed using a bandpass filter to remove noise. An inverse Fourier transform is then used to convert the frequency domain data back to the time domain.

[0064] The peaks of the pulse wave (PPG) reflect the state after a cardiac contraction, while the troughs reflect the state after a cardiac diastole. Scale-invariant feature transform (SIFT) is used to obtain local features of the pulse wave data, and the locations of the PPG peaks and troughs are located within the specified range.

[0065] Constructing a multi-scale space for pulse wave data helps detect features at different scales: First, the signal data is downsampled, gradually reducing the data resolution. This is done three times, resulting in four sets of data including the original data. Smoothing is then applied to these four sets of data, and this process is repeated four times, resulting in 4*5 sets of data. Each of the original four sets of data now has five layers. This is the four-set, five-layer scale space configuration.

[0066] Then, construct a Gaussian difference scale space for each group of five layers, and subtract the adjacent scale spaces of these five layers to obtain a four-layer Gaussian difference scale space.

[0067] Locations showing significant signal intensity changes in the Gaussian difference scale space are selected, i.e., places that are likely peaks or troughs. At each data location in the difference scale, the value at that location is compared with its two adjacent values ​​in the current layer and its six values ​​in the upper and lower layers. Since comparisons are made at adjacent scales, a Gaussian difference layer with four layers per group can only perform extreme point detection at two scales in the middle two layers. If the value is greater than all its adjacent values, the location is selected as a peak; if the value is less than all its adjacent values, the location is selected as a trough.

[0068] When a continuous function is sampled, its true maximum or minimum value may actually lie between the sample points. Therefore, an interpolation function needs to be fitted to the discrete data, and then the positions of the extrema after improving accuracy need to be found within it. The above steps have identified eigenvalues ​​existing at different scales, but it is still necessary to combine the eigenvalues ​​from multiple scales. Since this is one-dimensional data, it is only necessary to determine the positions of the eigenvalue points within the entire sample. This is done by normalizing the eigenvalue position vectors at each scale. This yields four sets of normalized signal data eigenvalue position vectors. Weighted bisecting of these four vectors gives the positions of the peaks and troughs in the PPG.

[0069] Pulse wave measurement uses photoplethysmography (PPG) to detect changes in blood caused by heartbeats, generating different LED light reflection signals. A photoelectric sensor converts the pulse wave beats into electrical signals, which are then amplified, shaped, and filtered before being processed by a microcontroller. The raw pulse wave signal processing circuit mainly consists of three parts: signal amplification, filtering, and waveform shaping.

[0070] The microcontroller then performs digital signal processing on the hardware-processed data to reduce the impact of ambient light. During digital signal processing, the one-dimensional pulse wave signal is sampled at 256 points for FFT (Fast Fourier Transform) calculation, converting the pulse wave data from the time domain to the frequency domain for processing. Since the frequency of the pulse wave signal is approximately 0.7Hz-3.0Hz, frequencies outside this range are considered noise, so a bandpass filter is used to remove noise. Finally, an inverse Fourier transform converts the frequency domain data back to the time domain.

[0071] The peak of the PPG curve reflects the state after a heart contraction, while the trough reflects the state after a heart diastole.

[0072] Scale-invariant feature transform (SIFT) is used to obtain local features of pulse wave data, and the locations of PPG peaks and troughs are located within the range.

[0073] First, a multi-scale space for the pulse wave data is constructed, which helps in detecting features at different scales: the signal data is downsampled three times, gradually reducing the data resolution, resulting in four sets of data including the original data. These four sets of data are then smoothed using Gaussian filtering. As a smoothing factor, Multiply by a scaling factor A new smoothing factor is obtained. .

[0074]

[0075] After repeating this process four times, we finally obtain 4*5 sets of data, with each of the original four sets now having five layers. This is the setup of four sets, each with five layers of scale space.

[0076] Then, a Gaussian difference scale space is constructed for each group of five layers. Subtracting the adjacent scale spaces of these five layers yields a four-layer Gaussian difference scale space.

[0077] Next, locations where signal intensity changes significantly in the Gaussian difference scale space are selected, i.e., places that are likely peaks or troughs. At each data location in the difference scale, the value at that location is compared with its two adjacent values ​​in the current layer and its six values ​​in the upper and lower layers. Since comparisons are made at adjacent scales, a Gaussian difference layer with four layers per group can only perform extreme point detection at two scales in the middle two layers. If the value is greater than all its adjacent values, the location is selected as a peak; if the value is less than all its adjacent values, the location is selected as a trough.

[0078] When a continuous function is sampled, its true maximum or minimum value may actually lie between the sample points. Therefore, an interpolation function needs to be fitted to the discrete data, and then the positions of the extrema after improving accuracy need to be found within it. The above steps have identified eigenvalues ​​existing at different scales, but it is still necessary to combine the eigenvalues ​​from multiple scales. Since this is one-dimensional data, it is only necessary to determine the positions of the eigenvalue points within the entire sample. This is done by normalizing the eigenvalue position vectors at each scale. This yields four sets of normalized signal data eigenvalue position vectors. Weighted bisecting of these four vectors gives the positions of the peaks and troughs in the PPG.

[0079] When the user presses and holds the button, the MCU will stop outputting the PWM_BOOST signal, PWM_1 signal, and PWM_2 signal, and the device will stop working.

[0080] The provided adaptive antiemetic device and control method use a pulse wave detection unit to detect the user's state, adjust the stimulation intensity according to the user's individual situation, and also detect the user's pulse wave during long-term use to obtain the user's heart rate information, adjust the stimulation intensity in real time, and effectively relieve vomiting symptoms, thus providing a better user experience.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications can still be made to the specific implementation of the present invention or equivalent substitutions can be made to some technical features without departing from the spirit of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the technical solutions claimed in the present invention.

Claims

1. An adaptive antiemetic device, characterized in that: It includes a housing, a pulse wave detection unit, a discharge circuit, electrode plates, and a control unit; The housing is provided with wristband assemblies at both ends, and the electrode pads are located at the bottom of the housing and connected to the discharge circuit. The electrode pads are in close contact with the user's wrist acupoints through the wristband assemblies. The pulse wave detection unit detects the user's pulse signal, and the control unit receives, analyzes and judges the detected pulse signal, controls the discharge circuit to change the pulse current, and applies it to the user's acupoints through the electrode pads.

2. A method for adjusting an adaptive antiemetic device, using the adaptive antiemetic device as described in claim 1, characterized in that, The pulse wave measurement method includes the following steps: S110. Secure the housing to the user's wrist with a wristband so that the two electrodes are located on both sides of the Neiguan acupoint; S120. Obtain effective body data of the user through the pulse wave detection unit and select appropriate stimulation intensity through the set calibration range. Use the pulse wave detection unit to measure the peak and trough of the pulse wave and save the data in the data storage chip. S130. The device samples the pulse wave and analyzes the positions of the peaks and troughs, records them, and stores them in the data storage chip; S140. Real-time acquisition of the user's heart rate variability. and rate of change of heart rate completion time ; S150. User presses and holds button or user's heart rate variability. and rate of change of heart rate completion time If the range of variation exceeds 10% of the set threshold, the device will stop working.

3. The adaptive antiemetic device control method according to claim 2, characterized in that, The pulse wave detection unit detects changes in blood caused by heartbeats using photoplethysmography (PPG) pulse wave scanning, generating different LED light reflection signals. A photoelectric sensor converts the pulse wave beats into electrical signals, which are then amplified, shaped, and filtered before being sent to a microcontroller for processing. During digital signal processing, the pulse wave signal is sampled at multiple points for fast Fourier transform calculations, converting the pulse wave data from the time domain to the frequency domain and processing it.

4. The adaptive antiemetic device control method according to claim 3, characterized in that, The pulse wave measurement method includes the following steps: S210. Pulse wave measurement detects changes in blood caused by heartbeat through photoplethysmography pulse wave scanning. The raw pulse wave signal processing circuit includes three parts: filtering, signal amplification, and waveform shaping. The S220 microcontroller then performs digital signal processing on the hardware-processed data to reduce the influence of ambient light. It converts the pulse wave data from the time domain to the frequency domain for processing, uses a bandpass filter to filter out noise, and uses an inverse Fourier transform to convert the frequency domain data back to the time domain data. S230. The peak reflects the state after one cardiac contraction, and the trough reflects the state after one cardiac diastole. Scale-invariant feature transformation is used to obtain the local features of pulse wave data and to find the positions of the peak and trough within the range. S240. Construct four sets of scale spaces for pulse wave data, each with five layers, to help detect features at different scales; S250. For each group of five layers, construct a Gaussian difference scale space. Subtract the adjacent scale spaces of these five layers to obtain a four-layer Gaussian difference scale space. S260. Select locations where signal intensity changes significantly in the Gaussian difference scale space; S270. When a continuous function is sampled, its true maximum or minimum value may actually lie between the sample points; therefore, it is necessary to fit an interpolation function to discrete numbers and then find the location of the extreme values ​​within it after improving accuracy.

5. The adaptive antiemetic device control method according to claim 2, characterized in that, Analysis of historical data and the latest sampled data The heart rate variability was obtained in real time at the intervals between the occurrences of each peak. The time from one peak to the next trough is equivalent to the time it takes for one heartbeat to complete. (Analysis) The rate of change of heartbeat completion time is obtained from the time it takes for each heartbeat to complete. .

6. The adaptive antiemetic device control method according to claim 5, characterized in that, Heart rate change rate Exceeding its upper threshold When the pulse frequency is reduced by 5% of the original pulse frequency, the heart rate variability is... Exceeding its lower threshold At this point, the pulse frequency is increased in steps of 5% of the original pulse frequency until the heart rate variability rate is reached. Within the normal range; when the rate of change of heart rate completion time Exceeding its upper threshold When the pulse width is reduced by 5% of the original pulse width, the rate of change of the heartbeat completion time is... Exceeding its lower threshold The pulse width is increased in increments of 5% of the original pulse width until the rate of change of heartbeat completion time is reached. It is within the normal range.

7. The adaptive antiemetic device control method according to claim 2, characterized in that, The process of constructing a multi-scale space for pulse wave data includes: First, the signal data is downsampled, so that the resolution of the data is gradually reduced. Then, the data was sampled three times to obtain four sets of data including the original data; Finally, the four sets of data were smoothed, and this process was repeated four times to obtain 4*5 sets of data. Each of the original four sets of data was increased to five layers, resulting in four sets of five-layer scale spaces.

8. The adaptive antiemetic device control method according to claim 7, characterized in that, Gaussian filtering was used to smooth the four sets of data. As a smoothing factor, Multiply by a scaling factor A new smoothing factor is obtained. ; 。 9. The adaptive antiemetic device control method according to claim 8, characterized in that, Select locations where signal intensity changes significantly in the Gaussian difference scale space; that is, places that may be peaks or troughs. At each data location in the difference scale, compare the value at that location with its two adjacent values ​​in the current layer and its six values ​​in the upper and lower layers. During the comparison of adjacent scales, each group of four Gaussian difference layers is divided, and extreme point detection is performed on the two middle layers at two scales. If the value is greater than all its adjacent values, the location is selected as a peak; if the value is less than all its adjacent values, the location is selected as a trough.

10. The adaptive antiemetic device control method according to claim 9, characterized in that, The location of the feature value is determined in the whole sample. The feature value location vectors at each scale are normalized to obtain four sets of normalized signal data feature value location vectors. The locations of the peaks and troughs can be obtained by weighted bisecting the four sets of vectors. The feature values ​​at multiple scales are then combined.

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