A sleep monitor for neurological rehabilitation and a method of using the same

Abstract

The invention discloses a sleep monitor for neurological rehabilitation and a method of using the same, and belongs to the field of sleep monitoring systems. The device designs and optimizes the internal partitions of a sleep chamber, divides the top of the inner wall of the chamber of the sleep chamber into a head monitoring area and a limb monitoring area, and reasonably arranges different types of sensors to facilitate accurate monitoring of signals of different parts. At the same time, non-contact monitoring technology is adopted, and a technology of integrating multiple sensors such as radio frequency sensors, capacitive sensors, optical infrared sensors and microphones is adopted to simultaneously monitor multiple physiological parameters such as electroencephalogram signals, heart rate, blood oxygen saturation and snoring. Power parameter data units for people of different age groups and genders are directly integrated in the system, so that corresponding sensor operating parameters can be selected according to the age and gender of the monitored person, thereby avoiding the situation in which inaccurate monitoring data is caused by the operating power and parameters of multiple sensors not being adjusted to appropriate data values ​​when monitoring different groups of people.

Description

Technical Field

[0001] The present invention relates to the field of sleep monitoring systems, and more particularly to a sleep monitoring device for neurological rehabilitation and a method of using the same. Background Art

[0002] With the rapid development of modern society, people's pace of life is accelerating, work pressure is increasing day by day, and sleep problems have gradually become a major problem that troubles many people. Accurate monitoring and effective management of sleep quality have become an urgent need to ensure people's physical and mental health.

[0003] Traditional sleep monitoring technologies mostly rely on contact devices. These devices require the sensors to be attached directly to the human body. On the one hand, this will give the user an obvious foreign body sensation during sleep, affecting the natural state of sleep and reducing the accuracy of the monitoring results. For example, some contact EEG monitoring devices require multiple electrodes to be worn on the head, which is not only uncomfortable, but may also shift during sleep due to the user's turning over and other actions, resulting in unstable monitoring signals. On the other hand, long-term use of contact devices may cause adverse reactions such as skin allergies, especially for people with sensitive skin, the user experience is greatly reduced.

[0004] In addition, although traditional sleep monitoring systems have many functions and can monitor multiple physiological parameters, the degree of integration between multiple sensors is poor, and it is not convenient to directly adjust the operating power and parameters of multiple sensors within the system. As a result, when monitoring different groups of people, inaccurate monitoring data may occur because the operating power and parameters of multiple sensors are not adjusted to appropriate data values. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the problems existing in the prior art, the purpose of the present invention is to provide a sleep monitor for neurological rehabilitation and a method of using the same, which can monitor multiple key physiological parameters simultaneously without disturbing sleep, providing people with more comprehensive and accurate sleep information, so as to better manage sleep and maintain health.

[0007] 2. Technical solution

[0008] To solve the above problems, the present invention adopts the following technical solutions.

[0009] In a first aspect, the present disclosure provides a sleep monitor for neurological rehabilitation, including hardware equipment and monitoring management system software, wherein the hardware equipment includes a sleeping chamber, a movable sleeping bed, and a system operation host, wherein the system operation host is connected to a display screen via a signal line, and the top of the inner wall of the sleeping chamber is divided into a head monitoring area and a limb monitoring area by a dividing line, wherein a plurality of radio frequency sensors are fixedly installed at equal intervals on the head monitoring area, and a pickup is fixedly installed at the center of the head monitoring area, and a plurality of capacitive sensors and optical infrared sensors are fixedly installed at equal intervals on the limb monitoring area;

[0010] The monitoring management system software is installed inside the central processing unit of the system running host, and the monitoring management system software is run through the central processing unit. The monitoring management system software includes a contactless sensor module, a signal processing module, a data analysis module and an intelligent feedback module. The contactless sensor module, the signal processing module, the data analysis module and the intelligent feedback module are connected by data communication. The monitoring management system software also includes a network communication module and a cloud model database. The cloud model database is remotely wirelessly connected to the data analysis module through the network communication module.

[0011] The radio frequency sensor, the microphone, the capacitive sensor and the optical infrared sensor are all connected to the system operating host through signal lines. The contactless sensor module receives the signals captured by each sensor, and the contactless sensor module is connected to a power parameter adjustment module for data communication. The power parameter adjustment module is internally provided with power parameter data units for people of different age groups and genders, so as to select corresponding sensor operating parameters according to the age and gender of the monitored person.

[0012] Furthermore, the contactless sensor module controls several of the RF sensors to transmit RF signals of specific frequencies to the head of the monitored person. At the same time, the activity of neurons in the brain of the monitored person generates EEG signals and forms a weak electromagnetic field around the head. The RF signal is interfered, reflected, refracted and attenuated by the weak electromagnetic field to form an EEG characteristic signal which is captured again by the RF sensor. The EEG characteristic signal is input into the signal processing module.

[0013] Furthermore, the capacitive sensor approaches the limb of the monitored person to form a capacitor, and the contactless sensor module controls a plurality of the capacitive sensors to transmit electric field band signals of specific frequencies to the limb of the monitored person. The electric field band signals encounter the limb of the monitored person to generate induced charges on the surface of the limb. Meanwhile, the physiological activities of the limb and the heartbeat activities cause the distribution of the induced charges to change, thereby changing and forming a new electric field band signal which is reflected back and received by the capacitive sensor. The new electric field band signal is input into the signal processing module.

[0014] Furthermore, the contactless sensor module controls a plurality of the optical infrared sensors to emit red light and infrared light of specific frequencies to the limbs of the monitored person, and utilizes the principle that the absorption difference between oxygenated hemoglobin and reduced hemoglobin by infrared light is smaller, while the absorption difference between oxygenated hemoglobin and reduced hemoglobin by red light to receive the reflected or transmitted light signal, and converts it into a photoelectric band signal, which is input into the signal processing module.

[0015] Furthermore, the signal processing module is internally provided with a denoising unit, an amplifying unit, a filtering unit and a feature extraction unit, and the denoising unit, the amplifying unit, the filtering unit and the feature extraction unit are operated sequentially. The signal on the denoising unit is connected to a filter, and the denoising unit uses the filter to filter out high-frequency electromagnetic interference in the EEG characteristic signal environment. The amplifying unit then amplifies the filtered EEG characteristic signal to a target amplitude range to improve the signal strength. The filtering unit filters the received amplified EEG characteristic signal to remove the EEG characteristic signal higher than 50 Hz and retain the EEG characteristic signal lower than 50 Hz. The feature extraction unit extracts the received EEG characteristic signal lower than 50 Hz and outputs the corresponding EEG signal frequency value.

[0016] Furthermore, the new electric field band signal is first denoised by the denoising unit inside the signal processing module to remove abnormal electric field band signals. The amplifying unit gradually amplifies the electric field band signal after the abnormal electric field band signal is removed in multiple stages for subsequent processing. The filtering unit filters the amplified electric field band signal to remove electric field band signals higher than 50 Hz and retain electric field band signals lower than 50 Hz. The feature extraction unit extracts the received electric field band signal lower than 50 Hz and outputs the corresponding heart rate signal frequency value.

[0017] Furthermore, the photoelectric band signal is first denoised by the denoising unit inside the signal processing module to remove abnormal photoelectric band signals, the amplifying unit amplifies the photoelectric band signal after the abnormal photoelectric band signal is removed, the filtering unit filters the amplified photoelectric band signal to remove photoelectric band signals higher than 50 Hz and retain photoelectric band signals lower than 50 Hz, and the feature extraction unit extracts the received photoelectric band signal lower than 50 Hz and outputs the corresponding blood oxygen saturation monitoring value.

[0018] Furthermore, the cloud model database is provided with an EEG signal numerical unit library, a heart rate signal numerical unit library and a blood oxygen saturation numerical unit library that are pre-trained and stored through an artificial neural network. The data analysis module obtains the numerical value of the EEG signal frequency, the numerical value of the heart rate signal frequency and the numerical value of the blood oxygen saturation monitoring value, and compares them with the numerical values ​​of the EEG signal numerical unit library, the heart rate signal numerical unit library and the blood oxygen saturation numerical unit library respectively. The comparison result is displayed and fed back through the intelligent feedback module. The display and feedback results include the specific monitoring values ​​of the EEG signal frequency, the heart rate signal frequency and the blood oxygen saturation monitoring value. The display and feedback results also include whether each monitoring value is within the corresponding numerical unit library of the cloud model database. If it is within the corresponding numerical unit library, it indicates that the data monitoring is normal. If it is not within the corresponding numerical unit library, it indicates that the data monitoring is abnormal.

[0019] Furthermore, a snoring frequency value unit library within a specific time period is also provided inside the cloud model database. After the microphone receives the snoring signal within the specific time period, it is processed and analyzed by the signal processing module and the data analysis module, and then a snoring detection value is generated to be compared with the snoring frequency value unit library, and a comparison result is generated through the intelligent feedback module.

[0020] In a second aspect, the present disclosure provides a method for using a sleep monitor for neurological rehabilitation, the method being applied to a sleep monitor for neurological rehabilitation provided in the first aspect, the method for using the sleep monitor for neurological rehabilitation comprising: an electroencephalogram information monitoring and management method, a heart rate information monitoring and management method, a blood oxygen saturation information monitoring and management method, and a snoring information monitoring and management method;

[0021] Wherein, the EEG information monitoring and management method comprises:

[0022] Step 1: The subject lies flat on the bed board of the movable sleeping bed, moves the bed board into the sleeping chamber, and makes the head face the head monitoring area;

[0023] Step 2: Enter the monitoring management system software through the system operation host and select the power parameters suitable for the age and gender of the subject;

[0024] Step 3: The contactless sensor module controls the RF sensor to transmit a specific frequency RF signal to the subject's head to capture the EEG characteristic signal;

[0025] Step 4: The EEG characteristic signal is input into the signal processing module and undergoes denoising, amplification, filtering and feature extraction in sequence;

[0026] Step 5: The data analysis module compares the extracted EEG signal frequency value with the EEG signal value unit library and displays the result through the intelligent feedback module;

[0027] The heart rate information monitoring and management method comprises:

[0028] Step 1: The contactless sensor module controls the capacitive sensor to transmit a specific frequency electric field band signal to the subject's limbs;

[0029] Step 2: Receive the electric field band signal reflected back due to changes in limb movement and heartbeat;

[0030] Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction;

[0031] Step 4: The data analysis module compares the heart rate signal frequency value with the heart rate signal numerical unit library and displays the result through the intelligent feedback module;

[0032] The blood oxygen saturation information monitoring and management method comprises:

[0033] Step 1: The contactless sensor module controls the optical infrared sensor to emit red light and infrared light of specific frequencies to the subject’s limbs;

[0034] Step 2: Receive the reflected or transmitted light signal and convert it into a photoelectric band signal;

[0035] Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction;

[0036] Step 4: The data analysis module compares the blood oxygen saturation monitoring value with the blood oxygen saturation numerical unit library and displays the result through the intelligent feedback module;

[0037] The snoring information monitoring and management method comprises:

[0038] Step 1: The microphone receives snoring signals within a specific period of time;

[0039] Step 2: The snoring signal is processed and analyzed by the signal processing module and the data analysis module;

[0040] Step 3: Generate a unit library for comparing snoring detection values ​​and snoring frequency values, and display the results through the intelligent feedback module.

[0041] 3. Beneficial effects

[0042] Compared with the prior art, the advantages of the present invention are:

[0043] (1) In this scheme, the internal partition design and layout of the sleeping chamber are optimized. The top of the inner wall of the sleeping chamber is divided into the head monitoring area and the limb monitoring area. Different types of sensors are reasonably arranged to facilitate accurate monitoring of signals from different parts, reduce signal interference, and improve monitoring efficiency and quality.

[0044] (2) This solution uses non-contact sensor monitoring technology. The sensor does not need to be tied or pasted to the body of the monitored person, thus avoiding the discomfort caused by binding or pasting, improving the sleep quality of the monitored person, and reducing the problems of skin allergies caused by the pasting of sensors of traditional contact monitoring equipment. At the same time, it uses a technology that integrates multiple sensors such as radio frequency sensors, capacitive sensors, optical infrared sensors and microphones, which can simultaneously monitor multiple physiological parameters such as EEG signals, heart rate, blood oxygen saturation and snoring, providing more comprehensive sleep monitoring data and providing a solid data basis for sleep monitoring.

[0045] (3) In this solution, the system directly integrates power parameter data units for different age groups and genders, which facilitates the selection of corresponding sensor operating parameters according to the age and gender of the monitored person, avoiding the situation where inaccurate monitoring data occurs when monitoring different groups of people due to the failure to adjust the operating power and parameters of multiple sensors to appropriate data values;

[0046] (4) This solution, through the artificial neural network pre-training and setting up the cloud model database for storage of various numerical unit libraries, can compare and analyze the monitored data with the various numerical unit libraries, making the comparative analysis more convenient, and the intelligent feedback module will display the comparison results to the user, so that the user can intuitively understand his or her sleep status, discover problems in time and take corresponding measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the hardware device structure of the present invention;

[0048] Figure 2 This is a schematic diagram of the installation and distribution structure of various sensors inside the sleeping chamber of the present invention;

[0049] Figure 3 The system architecture of the sleep monitor of the present invention is shown in FIG. Figure 1 ;

[0050] Figure 4 The system architecture of the sleep monitor of the present invention is shown in FIG. Figure 2 ;

[0051] Figure 5 The system architecture of the sleep monitor of the present invention is shown in FIG. Figure 3 ;

[0052] Figure 6 It is a schematic diagram of the process of the EEG information monitoring and management method of the present invention;

[0053] Figure 7 It is a flowchart of the heart rate information monitoring and management method of the present invention;

[0054] Figure 8 It is a flow chart of the blood oxygen saturation information monitoring and management method of the present invention;

[0055] Fig. 9 It is a flow chart of the snoring information monitoring and management method of the present invention.

[0056] Description of the numbers in the figure:

[0057] Hardware equipment:

[0058] 1. Sleeping chamber; 101. Head monitoring area; 102. Limb monitoring area; 2. Movable sleeping bed; 3. System operation host; 4. Display screen; 5. Radio frequency sensor; 6. Pickup; 7. Capacitive sensor; 8. Optical infrared sensor;

[0059] Monitoring management system software:

[0060] 9. Contactless sensor module; 901. Power parameter adjustment module; 10. Signal processing module; 1001. De-noising unit; 1002. Amplification unit; 1003. Filtering unit; 1004. Feature extraction unit; 11. Data analysis module; 12. Intelligent feedback module; 13. Network communication module; 14. Cloud model database; 1401. EEG signal numerical unit library; 1402. Heart rate signal numerical unit library; 1403. Blood oxygen saturation numerical unit library; 1404. Snoring frequency numerical unit library. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the specification of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0062] See also Figure 1-Figure 5The present disclosure provides a sleep monitor for neurological rehabilitation, including hardware equipment and monitoring management system software, the hardware equipment includes a sleep chamber 1, a movable sleep bed 2 and a system operation host 3, the system operation host 3 is connected to a display screen 4 through a signal line, the top of the inner wall of the chamber of the sleep chamber 1 is divided into a head monitoring area 101 and a limb monitoring area 102 by a dividing line, a plurality of radio frequency sensors 5 are fixedly installed at equal intervals on the head monitoring area 101, and a pickup 6 is fixedly installed at the center of the head monitoring area 101, and a plurality of capacitive sensors 7 and optical infrared sensors 8 are fixedly installed at equal intervals on the limb monitoring area 102;

[0063] The monitoring management system software is installed inside the central processing unit of the system operation host 3, and the monitoring management system software is run through the central processing unit. The monitoring management system software includes a contactless sensor module 9, a signal processing module 10, a data analysis module 11 and an intelligent feedback module 12. The contactless sensor module 9, the signal processing module 10, the data analysis module 11 and the intelligent feedback module 12 are connected by data communication. The monitoring management system software also includes a network communication module 13 and a cloud model database 14. The cloud model database 14 is remotely wirelessly connected to the network between the network communication module 13 and the data analysis module 11;

[0064] The radio frequency sensor 5, the microphone 6, the capacitive sensor 7 and the optical infrared sensor 8 are all connected to the system operation host 3 through signal lines, so that the contactless sensor module 9 can receive the signals captured by each sensor, and the contactless sensor module 9 is also connected to the power parameter adjustment module 901 for data communication. The power parameter adjustment module 901 is internally provided with power parameter data units for people of different age groups and genders, so as to facilitate the selection of corresponding sensor operation parameters according to the age and gender of the monitored person.

[0065] Among them, the signal processing module 10 is internally provided with a denoising unit 1001, an amplifying unit 1002, a filtering unit 1003 and a feature extraction unit 1004, and the denoising unit 1001, the amplifying unit 1002, the filtering unit 1003 and the feature extraction unit 1004 are operated in sequence, and the signal on the denoising unit 1001 is connected to a filter; the cloud model database 14 is internally provided with an electroencephalogram signal numerical unit library 1401, a heart rate signal numerical unit library 1402, a blood oxygen saturation numerical unit library 1403 and a snoring frequency numerical unit library 1404 which are pre-trained and stored through an artificial neural network.

[0066] (I) The principle of monitoring and managing the EEG information of the subject by this sleep monitor is as follows:

[0067] First, the subject lies flat on the bed board of the movable sleeping bed 2 in a state of being as naked as possible, and then the bed board of the movable sleeping bed 2 together with the subject is moved to the interior of the chamber of the sleeping chamber 1 through the control mechanism and the driving mechanism on the movable sleeping bed 2 (this is the existing known public technology, so it will not be described in detail here), and the head and limbs of the subject are respectively facing the head monitoring area 101 and the limb monitoring area 102, and then the system operation host 3 can be used to log in and enter the monitoring management system software, and the power parameter data unit suitable for the age and gender of the subject can be selected through the power parameter adjustment module 901, so as to facilitate the selection of corresponding sensor operation parameters according to the age and gender of the subject;

[0068] Then the contactless sensor module 9 controls several radio frequency sensors 5 to transmit radio frequency signals of a specific frequency to the head of the monitored person. At the same time, the brain neuron activity of the monitored person generates an electroencephalogram signal and forms a weak electromagnetic field around the head. The radio frequency signal is interfered, reflected, refracted and attenuated by the weak electromagnetic field to form an electroencephalogram characteristic signal, which is captured by the radio frequency sensor 5 again. The electroencephalogram characteristic signal is input into the signal processing module 10; the electroencephalogram characteristic signal is first denoised by the denoising unit 1001 inside the signal processing module 10. The denoising unit 1001 inside the signal processing module 10 uses a filter to filter out the high-frequency electromagnetic interference in the electroencephalogram characteristic signal environment. The amplification unit 1002 then amplifies the filtered electroencephalogram characteristic signal to a suitable target amplitude range to improve the signal strength. The filtering unit 1003 filters the received amplified electroencephalogram characteristic signal, removes the electroencephalogram characteristic signal higher than 50 Hz again, and retains the electroencephalogram characteristic signal lower than 50 Hz. The feature extraction unit 1004 extracts the received electroencephalogram characteristic signal lower than 50 Hz and outputs the corresponding electroencephalogram signal frequency value;

[0069] The data analysis module 11 obtains the EEG signal frequency value and the value in the EEG signal value unit library 1401 and compares them, and displays and feeds back the comparison results through the intelligent feedback module 12. The display and feedback results include the specific monitoring value of the EEG signal frequency value and whether the monitoring value is within the EEG signal value unit library 1401 of the cloud model database 14. If it is within the EEG signal value unit library 1401, it indicates that the data monitoring is normal; if it is not within the EEG signal value unit library 1401, it indicates that the data monitoring is abnormal, thereby facilitating subsequent treatment interventions and providing a solid data conclusion basis for sleep monitoring.

[0070] (II) The principle of monitoring and managing the heart rate information of the subject by this sleep monitor is as follows:

[0071] When the capacitive sensor 7 approaches the monitored person's limb, a capacitor can be formed. The contactless sensor module 9 controls a plurality of capacitive sensors 7 to transmit an electric field band signal of a specific frequency to the monitored person's limb. The electric field band signal encounters the monitored person's limb and generates an induced charge on the surface of the limb. At the same time, the physiological activities of the limb and the heartbeat activities cause the distribution of the induced charge to change, thereby changing and forming a new electric field band signal, which is reflected back and received by the capacitive sensor 7. The new electric field band signal is input into the signal processing module 10. The new electric field band signal is The signal processing module 10 is first denoised by the denoising unit 1001 to remove abnormal electric field band signals. The amplifying unit 1002 gradually amplifies the electric field band signals after the abnormal electric field band signals are removed in multiple stages for subsequent processing. The filtering unit 1003 performs filtering processing on the amplified electric field band signals to remove electric field band signals higher than 50 Hz and retain electric field band signals lower than 50 Hz. The feature extraction unit 1004 extracts the received electric field band signals lower than 50 Hz and outputs the corresponding heart rate signal frequency value.

[0072] The data analysis module 11 obtains the heart rate signal frequency value and the value in the heart rate signal numerical unit library 1402 and compares them, and displays and feeds back the comparison results through the intelligent feedback module 12. The displayed and feedback results include the specific monitoring value of the heart rate signal frequency value and whether the monitoring value is within the heart rate signal numerical unit library 1402 of the cloud model database 14. If it is within the heart rate signal numerical unit library 1402, it indicates that the data monitoring is normal; if it is not within the heart rate signal numerical unit library 1402, it indicates that the data monitoring is abnormal, providing a solid data conclusion basis for sleep monitoring.

[0073] (III) The principle of monitoring and managing the blood oxygen saturation information of the subject by this sleep monitor is as follows:

[0074] The contactless sensor module 9 controls a plurality of optical infrared sensors 8 to emit red light and infrared light of a specific frequency to the limbs of the monitored person, and receives the reflected or transmitted light signal by utilizing the principle that the absorption difference between oxygenated hemoglobin and reduced hemoglobin by infrared light is small, and the absorption difference between oxygenated hemoglobin and reduced hemoglobin by red light is large, and converts the reflected or transmitted light signal into a photoelectric band signal. The photoelectric band signal is input into the signal processing module 10, and the photoelectric band signal is firstly denoised by the denoising unit 1001 in the signal processing module 10 to remove abnormal photoelectric band signals. The amplifying unit 1002 amplifies the photoelectric band signal after the abnormal photoelectric band signal is removed, and the filtering unit 1003 filters the amplified photoelectric band signal to remove photoelectric band signals higher than 50 Hz and retain photoelectric band signals lower than 50 Hz. The feature extraction unit 1004 extracts the received photoelectric band signal lower than 50 Hz and outputs the corresponding blood oxygen saturation monitoring value;

[0075] The data analysis module 11 obtains the value of the blood oxygen saturation monitoring value and the value of the blood oxygen saturation value unit library 1403 and compares them, and displays and feeds back the comparison result through the intelligent feedback module 12, and the display and feedback results include the specific monitoring value of the blood oxygen saturation monitoring value and whether the monitoring value is within the blood oxygen saturation value unit library 1403 of the cloud model database 14. If it is within the blood oxygen saturation value unit library 1403, it means that the data monitoring is normal; if it is not within the blood oxygen saturation value unit library 1403, it means that the data monitoring is abnormal, providing a solid data conclusion basis for sleep monitoring.

[0076] (IV) The principle of monitoring and managing the snoring information of the subject by this sleep monitor is as follows:

[0077] Through the snoring frequency value unit library 1404 within a specific time period set in the cloud model database 14, the microphone 6 can receive the snoring signal within the specific time period and then process and analyze it through the signal processing module 10 and the data analysis module 11, and then generate a snoring detection value to compare with the snoring frequency value unit library 1404, and generate a comparison result through the intelligent feedback module 12, so as to monitor the snoring frequency value within the specific time period and compare whether it is consistent with the data value recorded in the snoring frequency value unit library 1404, so as to determine whether there is a problem with the snoring.

[0078] Based on the sleep monitor for neurological rehabilitation described in the above embodiment, the present disclosure provides a method for using the sleep monitor for neurological rehabilitation, and the method for using the sleep monitor for neurological rehabilitation includes: an electroencephalogram information monitoring and management method, a heart rate information monitoring and management method, a blood oxygen saturation information monitoring and management method, and a snoring information monitoring and management method;

[0079] like Figure 6 As shown, the EEG information monitoring and management method includes:

[0080] Step 1: The subject lies flat on the bed board of the movable sleeping bed, and moves the bed board into the sleeping chamber so that the head faces the head monitoring area.

[0081] Step 2: Enter the monitoring management system software through the system operation host and select the power parameters suitable for the age and gender of the subject.

[0082] Step 3: The contactless sensor module controls the RF sensor to transmit a specific frequency RF signal to the subject's head to capture the EEG characteristic signal.

[0083] Step 4: The EEG characteristic signal is input into the signal processing module and undergoes denoising, amplification, filtering and feature extraction in sequence.

[0084] Step 5: The data analysis module compares the extracted EEG signal frequency value with the EEG signal value unit library and displays the result through the intelligent feedback module.

[0085] like Figure 7 As shown, the heart rate information monitoring and management method includes:

[0086] Step 1: The contactless sensor module controls the capacitive sensor to transmit a specific frequency electric field band signal to the subject's limbs.

[0087] Step 2: Receive the electric field band signal reflected back due to changes in limb movement and heartbeat.

[0088] Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction.

[0089] Step 4: The data analysis module compares the heart rate signal frequency value with the heart rate signal numerical unit library and displays the result through the intelligent feedback module.

[0090] like Figure 8 As shown, the blood oxygen saturation information monitoring and management method includes:

[0091] Step 1: The contactless sensor module controls the optical infrared sensor to emit red light and infrared light of specific frequencies to the subject's limbs.

[0092] Step 2: Receive the reflected or transmitted light signal and convert it into a photoelectric band signal.

[0093] Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction.

[0094] Step 4: The data analysis module compares the blood oxygen saturation monitoring value with the blood oxygen saturation numerical unit library and displays the result through the intelligent feedback module.

[0095] like Fig. 9 As shown, the snoring information monitoring and management method includes:

[0096] Step 1: The microphone receives snoring signals within a specific period of time.

[0097] Step 2: The snoring signal is processed and analyzed by the signal processing module and the data analysis module.

[0098] Step 3: Generate a unit library for comparing snoring detection values ​​and snoring frequency values, and display the results through the intelligent feedback module.

[0099] Each monitoring and management method works independently, but the signal processing module and the data analysis module share resources. The timeliness and accuracy of data processing in each part are ensured through time-sharing processing and priority scheduling. At the same time, the system operation host uniformly manages and coordinates the entire monitoring process to ensure that each part starts and runs synchronously.

[0100] The intelligent feedback module integrates and displays the monitoring results of each monitoring management method, including the frequency value of the EEG signal, the frequency value of the heart rate signal, the blood oxygen saturation monitoring value and the snoring detection value, and whether they are in the corresponding value unit library. Combining these results, a comprehensive sleep monitoring report is provided to doctors or users for subsequent treatment intervention and sleep quality improvement.

[0101] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A sleep monitor for neurological rehabilitation, comprising hardware equipment and monitoring management system software, characterized in that: The hardware device includes a sleeping chamber, a movable sleeping bed and a system operation host, the system operation host is connected to a display screen via a signal line, the top of the inner wall of the sleeping chamber is divided into a head monitoring area and a limb monitoring area by a dividing line, a plurality of radio frequency sensors are fixedly installed at equal intervals on the head monitoring area, and a pickup is fixedly installed at the center of the head monitoring area, and a plurality of capacitive sensors and optical infrared sensors are fixedly installed at equal intervals on the limb monitoring area; The monitoring management system software is installed inside the central processing unit of the system running host, and the monitoring management system software is run through the central processing unit. The monitoring management system software includes a contactless sensor module, a signal processing module, a data analysis module and an intelligent feedback module. The contactless sensor module, the signal processing module, the data analysis module and the intelligent feedback module are connected by data communication. The monitoring management system software also includes a network communication module and a cloud model database. The cloud model database is remotely wirelessly connected to the data analysis module through the network communication module. The radio frequency sensor, the microphone, the capacitive sensor and the optical infrared sensor are all connected to the system operation host through signal lines, the contactless sensor module receives the signals captured by each sensor, and the contactless sensor module is connected to a power parameter adjustment module for data communication, and the power parameter adjustment module is internally provided with power parameter data units for people of different age groups and genders, so as to select corresponding sensor operation parameters according to the age and gender of the monitored person; The contactless sensor module controls a plurality of the radio frequency sensors to transmit radio frequency signals of a specific frequency to the head of the monitored person. At the same time, the activity of the neurons in the brain of the monitored person generates electroencephalogram signals and forms a weak electromagnetic field around the head. The radio frequency signals are interfered, reflected, refracted and attenuated by the weak electromagnetic field to form electroencephalogram characteristic signals that are captured by the radio frequency sensors again. The electroencephalogram characteristic signals are input into the signal processing module. The capacitive sensor approaches the monitored person's limb to form a capacitor, and the contactless sensor module controls a plurality of the capacitive sensors to transmit electric field band signals of a specific frequency to the monitored person's limb. The electric field band signal encounters the monitored person's limb to generate induced charges on the surface of the limb. Meanwhile, the physiological activities of the limb and the heartbeat activities cause the distribution of the induced charges to change, thereby changing and forming a new electric field band signal which is reflected back and received by the capacitive sensor, and the new electric field band signal is input into the signal processing module.

2. A sleep monitor for neurological rehabilitation according to claim 1, characterized in that: The contactless sensor module controls a plurality of the optical infrared sensors to emit red light and infrared light of specific frequencies to the limbs of the monitored person, and utilizes the principle that the absorption difference between oxygenated hemoglobin and reduced hemoglobin by infrared light is smaller, while the absorption difference between oxygenated hemoglobin and reduced hemoglobin by red light is larger to receive the reflected or transmitted light signal, and converts it into a photoelectric band signal, which is input into the signal processing module.

3. A sleep monitor for neurological rehabilitation according to claim 2, characterized in that: The signal processing module is internally provided with a denoising unit, an amplifying unit, a filtering unit and a feature extraction unit, and the denoising unit, the amplifying unit, the filtering unit and the feature extraction unit are operated in sequence. The signal on the denoising unit is connected to a filter, and the denoising unit uses the filter to filter out high-frequency electromagnetic interference in the EEG characteristic signal environment. The amplifying unit amplifies the filtered EEG characteristic signal to a target amplitude range. The filtering unit filters the received amplified EEG characteristic signal to remove EEG characteristic signals higher than 50 Hz and retain EEG characteristic signals lower than 50 Hz. The feature extraction unit extracts the received EEG characteristic signal lower than 50 Hz and outputs the corresponding EEG signal frequency value.

4. A sleep monitor for neurological rehabilitation according to claim 3, characterized in that: The new electric field band signal is first denoised by the denoising unit inside the signal processing module to remove abnormal electric field band signals. The amplifying unit gradually amplifies the electric field band signal after the abnormal electric field band signal is removed in multiple stages. The filtering unit filters the amplified electric field band signal to remove electric field band signals higher than 50 Hz and retain electric field band signals lower than 50 Hz. The feature extraction unit extracts the received electric field band signal lower than 50 Hz and outputs the corresponding heart rate signal frequency value.

5. A sleep monitor for neurological rehabilitation according to claim 4, characterized in that: The photoelectric band signal is first denoised by the denoising unit inside the signal processing module to remove abnormal photoelectric band signals. The amplifying unit amplifies the photoelectric band signal after the abnormal photoelectric band signal is removed. The filtering unit filters the amplified photoelectric band signal to remove photoelectric band signals higher than 50 Hz and retain photoelectric band signals lower than 50 Hz. The feature extraction unit extracts the received photoelectric band signal lower than 50 Hz and outputs the corresponding blood oxygen saturation monitoring value.

6. A sleep monitor for neurological rehabilitation according to claim 5, characterized in that: The cloud model database is provided with an EEG signal numerical unit library, a heart rate signal numerical unit library and a blood oxygen saturation numerical unit library which are pre-trained and stored by an artificial neural network. The data analysis module obtains the numerical values ​​of the EEG signal frequency, the heart rate signal frequency and the blood oxygen saturation monitoring value, and compares them with the numerical values ​​of the EEG signal numerical unit library, the heart rate signal numerical unit library and the blood oxygen saturation numerical unit library respectively. The comparison result is displayed and fed back through the intelligent feedback module. The display and feedback results include the specific monitoring values ​​of the EEG signal frequency, the heart rate signal frequency and the blood oxygen saturation monitoring value. The display and feedback results also include whether each monitoring value is in the corresponding numerical unit library of the cloud model database. If it is in the corresponding numerical unit library, it indicates that the data monitoring is normal. If it is not in the corresponding numerical unit library, it indicates that the data monitoring is abnormal.

7. A sleep monitor for neurological rehabilitation according to claim 1, characterized in that: The cloud model database is also provided with a snoring frequency value unit library within a specific time period. After the microphone receives the snoring signal within the specific time period, it is processed and analyzed by the signal processing module and the data analysis module, and then a snoring detection value is generated to be compared with the snoring frequency value unit library, and a comparison result is generated through the intelligent feedback module.

8. A method for using a sleep monitor for neurological rehabilitation, characterized in that: The method is applied to the sleep monitor for neurological rehabilitation according to any one of claims 1 to 7, and the method of using the sleep monitor for neurological rehabilitation includes: an electroencephalogram information monitoring and management method, a heart rate information monitoring and management method, a blood oxygen saturation information monitoring and management method, and a snoring information monitoring and management method; Wherein, the EEG information monitoring and management method comprises: Step 1: The subject lies flat on the bed board of the movable sleeping bed, moves the bed board into the sleeping chamber, and makes the head face the head monitoring area; Step 2: Enter the monitoring management system software through the system operation host and select the power parameters suitable for the age and gender of the subject; Step 3: The contactless sensor module controls the RF sensor to transmit a specific frequency RF signal to the subject's head to capture the EEG characteristic signal; Step 4: The EEG characteristic signal is input into the signal processing module and undergoes denoising, amplification, filtering and feature extraction in sequence; Step 5: The data analysis module compares the extracted EEG signal frequency value with the EEG signal value unit library and displays the result through the intelligent feedback module; The heart rate information monitoring and management method comprises: Step 1: The contactless sensor module controls the capacitive sensor to transmit a specific frequency electric field band signal to the subject's limbs; Step 2: Receive the electric field band signal reflected back due to changes in limb movement and heartbeat; Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction; Step 4: The data analysis module compares the heart rate signal frequency value with the heart rate signal numerical unit library and displays the result through the intelligent feedback module; The blood oxygen saturation information monitoring and management method comprises: Step 1: The contactless sensor module controls the optical infrared sensor to emit red light and infrared light of specific frequencies to the subject’s limbs; Step 2: Receive the reflected or transmitted light signal and convert it into a photoelectric band signal; Step 3: The signal is input into the signal processing module for denoising, amplification, filtering and feature extraction; Step 4: The data analysis module compares the blood oxygen saturation monitoring value with the blood oxygen saturation numerical unit library and displays the result through the intelligent feedback module; The snoring information monitoring and management method comprises: Step 1: The microphone receives snoring signals within a specific period of time; Step 2: The snoring signal is processed and analyzed by the signal processing module and the data analysis module; Step 3: Generate a unit library for comparing snoring detection values ​​and snoring frequency values, and display the results through the intelligent feedback module.

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