Psychological regulation medical method and its electronic device and system

Abstract

The present invention provides a psychological adjustment medical method and electronic device and system thereof performed by at least one electronic device. The method includes: receiving a physiological signal obtained by sensing a user (S410); analyzing the physiological signal based on an analysis model to confirm a psychological adjustment mode (S420), wherein confirming the psychological adjustment mode further includes: extracting a user's physiological data based on the physiological signal through a network model in the analysis model; confirming a user's psychological state based on the physiological data through a matrix analysis in the analysis model; confirming the psychological adjustment mode based on the psychological state; and transmitting the psychological adjustment mode (S430).

Description

Psychological adjustment medical method and electronic device and system thereof Technical Field

[0001] The present invention relates to the field of psychological adjustment medical treatment, and in particular, to a psychological adjustment medical treatment method and an electronic device and system thereof. Background Art

[0002] Digital health is a new technology that integrates software and hardware to provide medical interventions based on clinical evidence. It can be used to prevent, control, and even treat diseases. Although digital health technology is booming and changing rapidly, the types of diseases it can support are still limited, and it still cannot provide comprehensive assistance for a wide range of diseases.

[0003] For mental health disorders (such as substance abuse, chronic insomnia caused by drug overuse, ADHD, depression, anxiety, phobias, and post-traumatic stress disorder), digital healthcare still lacks mechanisms for prevention, control, and even treatment, which prevents it from leveraging its advantages in providing remote treatment.

[0004] Therefore, mental health patients still urgently need digital health to support remote treatment options to provide mental health patients with treatment plans and methods and monitor their own disease progress. Summary of the Invention

[0005] The present invention is made in view of the above problems and provides a psychological adjustment medical method performed by at least one electronic device, and an electronic device and system thereof.

[0006] In order to solve the above problems, the psychological adjustment medical method includes: receiving a physiological signal obtained by sensing a user; analyzing the physiological signal based on an analysis model to confirm a psychological adjustment pattern, wherein the confirmation of the psychological adjustment pattern further includes: based on the physiological signal, extracting physiological data of the user through a network model in the analysis model, wherein the physiological data includes at least one of breathing data of the user or blood oxygen data in a sensing target area of ​​the user; based on the physiological data, confirming a psychological state of the user through a matrix analysis in the analysis model; and based on the psychological state, confirming the psychological adjustment pattern; and sending the psychological adjustment pattern.

[0007] In some embodiments, the confirmation of the psychological adjustment mode further includes: obtaining a motion signal of the user, wherein the motion signal is related to an action of the user; based on the motion signal, removing a motion noise generated by the action in the physiological signal to obtain a corrected physiological signal; and extracting the physiological data of the user from the corrected physiological signal through the network model.

[0008] In some embodiments, the respiratory data includes at least one of an inhalation state, an inhalation time, an inhalation amplitude, an exhalation state, an exhalation time, an exhalation amplitude, and a respiratory rate, and the blood oxygenation data includes at least one of an oxyhemoglobin concentration change, a deoxyhemoglobin concentration change, and a blood oxygenation concentration change.

[0009] In some embodiments, the method further includes: receiving an updated physiological signal and analyzing the updated physiological signal based on the analysis model to update the psychological adjustment model; and transmitting the updated psychological adjustment model to an adjustment device that receives the psychological adjustment model, wherein a sensing system that generates the physiological signal continuously senses the user to generate the updated physiological signal.

[0010] In some embodiments, the method further includes: when an adjustment device that receives the psychological adjustment mode includes a transcranial electrical stimulation device, selecting a current stimulation mode from multiple current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode, wherein the transcranial electrical stimulation device performs a cranial nerve stimulation on the user based on the selected current stimulation mode.

[0011] In some embodiments, the method further comprises: evaluating a neural activity state of the user based on the physiological signal; and evaluating a plurality of stimulation parameters of the cranial nerve stimulation based on the neural activity state.

[0012] In some embodiments, the method further includes: when an adjustment device that receives the psychological adjustment mode includes a breathing training device, selecting a breathing training mode from multiple breathing training modes of the breathing training device as the psychological adjustment mode, wherein the breathing training device guides the user to adjust his breathing based on the selected breathing training mode.

[0013] In some embodiments, the physiological signal is used to evaluate at least one physiological parameter of the user: a heart rate parameter, a respiratory rate parameter, a skin conductance parameter, and a muscle tension parameter.

[0014] In some embodiments, the method further includes: when the physiological data exceeds a data threshold, sending an alarm signal to prompt the user to execute the psychological adjustment mode through an adjustment device that receives the psychological adjustment mode.

[0015] In some embodiments, the electronic device includes at least one processor; and at least one memory coupled to the at least one processor and storing at least one instruction. When the at least one instruction is executed by the at least one processor, the at least one instruction enables the electronic device to implement the text content evaluation method.

[0016] In some embodiments, the electronic device includes: at least one processor; and at least one non-transitory computer-readable medium, which is coupled to the at least one processor and stores at least one computer-executable instruction, wherein when the at least one computer-executable instruction is executed by the at least one processor, the electronic device performs the aforementioned psychological adjustment medical method.

[0017] In some embodiments, the mind-regulating medical system includes: an analysis system comprising at least one electronic device, wherein the at least one electronic device is configured to execute the aforementioned mind-regulating medical method.

[0018] Through the above-described method, the psychological adjustment medical treatment method can analyze the user's physiological signals through an analytical model to determine the psychological adjustment mode suitable for the user. Therefore, the technology of the present invention can provide a psychological adjustment medical treatment method through at least one electronic device, remotely provide treatment plans and methods, and monitor the user's disease progression. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the implementation methods or the description of the prior art. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] FIG1 is a block diagram of a psychological adjustment medical system for performing a psychological adjustment medical method through at least one electronic device according to the present invention.

[0021] FIG2A is a block diagram of an analysis system for performing a psychological adjustment medical method according to the present invention.

[0022] FIG2B is a block diagram of another analysis system for performing a psychological adjustment medical method according to the present invention.

[0023] FIG3 is a block diagram of a mental state warning program of a mental state warning method according to the present invention.

[0024] FIG4 is a flow chart of a psychological adjustment medical method performed by at least one electronic device according to the present invention.

[0025] FIG5 is a flow chart of a method for providing a mental state warning through at least one electronic device according to the present invention.

[0026] FIG6 is a flow chart of a method for analyzing psychological adjustment performed by at least one electronic device according to the present invention.

[0027] 7A and 7B are schematic diagrams respectively showing a NIRS signal for a sensing target area and a respiration-related signal captured by the network model. DETAILED DESCRIPTION

[0028] The following description contains specific information related to exemplary embodiments of the present invention. The drawings and accompanying detailed descriptions herein are exemplary embodiments only. However, the present invention is not limited to these exemplary embodiments. Other variations and embodiments of the present invention will occur to those skilled in the art. Unless otherwise indicated, identical or corresponding components in the drawings may be indicated by identical or corresponding reference numerals. In addition, the drawings and illustrations herein are generally not drawn to scale and are not intended to correspond to actual relative dimensions.

[0029] For the purpose of consistency and ease of understanding, the same features are indicated by reference numerals in the exemplary drawings (although not so indicated in some examples). However, features in different embodiments may differ in other aspects and should not be narrowly limited to the features shown in the drawings.

[0030] The phrases "at least one embodiment", "an embodiment", "multiple embodiments", "different embodiments", "some embodiments", "this embodiment", etc., may indicate that the embodiment of the invention so described may include certain features, structures or characteristics, but not every possible embodiment of the invention must include the certain features, structures or characteristics. In addition, repeated use of the phrases "in some embodiments" and "in this embodiment" does not necessarily refer to the same embodiment, although they may be the same. In addition, phrases such as "embodiments" used in connection with "the present invention" do not mean that all embodiments of the invention must include certain features, structures or characteristics, and it should be understood that "at least some embodiments of the present invention" include the said certain features, structures or characteristics. The term "coupled" is defined as connected, whether directly or indirectly through intermediate components, and is not necessarily limited to physical connections, but can also be wireless connections. When the term "including" is used, it means "including but not limited to", which clearly indicates the open inclusion or relationship of the stated combinations, groups, series and equivalents.

[0031] In addition, for purposes of explanation and non-limiting, specific details such as functional entities, technologies, protocols, standards, etc. are set forth to provide an understanding of the described technology. In other instances, detailed descriptions of well-known methods, techniques, systems, architectures, etc. are omitted to avoid obscuring the description with unnecessary detail.

[0032] The terms "first," "second," and "third," etc., in the present description and accompanying drawings are used to distinguish between different items, not to describe a specific order. Furthermore, the terms "comprise," "comprising," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules not listed, or may optionally include other steps or modules inherent to the process, method, product, or apparatus.

[0033] Those skilled in the art will immediately recognize that any computing function or algorithm described in this disclosure may be implemented by hardware, software, or a combination of software and hardware. The modules corresponding to the described functions may be software, hardware, firmware, or any combination thereof. Software implementations may include computer-executable instructions stored on a computer-readable medium such as a memory or other type of storage. For example, one or more microprocessors or general-purpose computers with communication processing capabilities may be programmed with corresponding executable instructions to execute the described network functions or algorithms. The processor, microprocessor, or general-purpose computer may be formed using an application-specific integrated circuit (ASIC), a programmable logic array, and / or one or more digital signal processors (DSP). Although several exemplary embodiments described in this specification tend to be implemented as software installed and executed on computer hardware, alternative exemplary embodiments implemented as firmware, hardware, or a combination of hardware and software are also within the scope of this disclosure.

[0034] The storage device may be a non-transitory computer-readable media, including but not limited to random-access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory, compact disc read-only memory (CD ROM), magnetic cassettes, magnetic tapes, disk storage, or any other equivalent media capable of storing computer-readable instructions.

[0035] The coupling between the various devices of the present invention may adopt customized protocols or follow existing standards or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, Wireless USB, or telecommunications standards (including but not limited to Global System for Mobile Communications (GSM), Code Division Multiple Access (CDMA) 2000, Time Division-Synchronization Code Division Multiple Access (TD-SCDMA), World Interoperability for Microwave Access (WiMAX), 3GPP-Long Term Evolution (LTE) technology, or Time Division Long Term Evolution (TD-LTE) technology). In addition, the various devices of the present invention may also each include any device configured to transmit and / or store data to a computer-readable medium and to receive data from a computer-readable medium. Furthermore, the various devices of the present invention may include a computer system interface that enables data to be stored on or received from a storage device. For example, the devices of the present invention may include a chipset that supports Peripheral Component Interconnect (PCI) and Peripheral Component Interconnect Express (PCIe) bus protocols, a dedicated bus protocol, a Universal Serial Bus (USB) protocol, I2C, or any other logical and physical structure that can be used to interconnect peer devices.

[0036] The present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

[0037] 1 is a block diagram of a psychological adjustment medical system 1 for performing a psychological adjustment medical method through at least one electronic device according to the present invention. In some embodiments, the psychological adjustment medical system 1 includes a sensing system 11 , an analysis system 12 , and an adjustment device 13 .

[0038] The sensing system 11 is used to sense a user of the mental adjustment medical system 1 to obtain a physiological signal. In some embodiments, the sensing system 11 may further include at least one sensor. Each sensor may be used to sense the user of the mental adjustment medical system 1 to obtain a physiological signal.

[0039] In some embodiments, the number of the at least one sensor may be n, where n may be a positive integer greater than zero, such as 1, 2, or 3. In some embodiments, the at least one sensor may include a first sensor 111, a second sensor 112, ..., and an nth sensor 11n. Each sensor may include a sensing unit and a sensor transmission unit. For example, the first sensor 111 may include a first sensing unit 1111 and a first sensor transmission unit 1112. The sensing unit may be used to sense the user of the psychological adjustment medical system 1 to obtain the physiological signal. The sensor transmission unit may be used to transmit the physiological signal sensed by the sensing unit from the user to the analysis system 12.

[0040] In some embodiments, when the psychological adjustment medical method is performed, the at least one sensor may be coupled to the analysis system 12 via the sensor transmission unit, thereby transmitting a signal from the sensor transmission unit to the analysis system 12. For example, the sensor transmission unit transmits the physiological signal to the analysis system 12. In some embodiments, after the psychological adjustment medical system 1 completes the psychological adjustment medical method, the at least one sensor may stop coupling with the analysis system 12. In some embodiments, the at least one sensor may each have a sensor receiving unit (not shown). The sensor receiving unit can be used to receive a signal from the analysis system 12. For example, the sensor receiving unit can receive a sensing start signal or a sensing type signal from the analysis system 12. The sensing start signal can be used to start the sensing unit to sense the physiological signal. In addition, when the sensor is capable of obtaining multiple different types of physiological signals, the sensing type signal can be used to indicate which type of signal the sensing unit obtains.

[0041] In some embodiments, the at least one sensor may include a physiological electrical signal measuring device. The physiological electrical signal measuring device may include, but is not limited to, an electrocardiogram (ECG), an electromyogram (EMG), an electroencephalogram (EEG), or other electrical signal measuring devices. The physiological signal sensed by the physiological electrical signal measuring device may be a physiological electrical signal.

[0042] In some embodiments, the at least one sensor may include a physiological light signal measuring device. The physiological light signal measuring device may include, but is not limited to, a near infrared spectroscopy (NIRS) or functional near-infrared spectroscopy (fNIRS) optical signal measuring device. The physiological signal sensed by the physiological light signal measuring device may be a physiological light signal.

[0043] In some embodiments, the at least one sensor may include a motion sensor. The motion sensor may include, but is not limited to, an accelerometer and a gyroscope. The motion sensor may be used to obtain a motion signal of the user. Because the sensing result of the physiological signal is easily interfered with by the user's motion, the motion signal related to the user's motion may be obtained as a basis for adjusting the physiological signal to remove motion noise generated by the motion in the physiological signal, thereby obtaining a corrected physiological signal.

[0044] The analysis system 12 can analyze the physiological signal based on an analysis model to identify a psychological adjustment pattern. In some embodiments, while performing the psychological adjustment medical method, the analysis system 12 can be coupled to the adjustment device 13 to transmit a signal to the adjustment device 13. In some embodiments, after the psychological adjustment medical system 1 completes the psychological adjustment medical method, the analysis system 12 can be decoupled from the adjustment device 13.

[0045] In some embodiments, the analysis system 12 may evaluate at least one of the user's heart rate, respiratory rate, skin conductance, and muscle tension parameters based on the physiological signal. The analysis system 12 may receive the physiological signal and, based on the physiological signal, evaluate the user's anxiety level as input information for the analysis model.

[0046] In some embodiments, the sensing system 11 can continuously sense the user to generate updated physiological signals, so that the analysis system 12 can receive the updated physiological signals and analyze the updated physiological signals based on the analysis model to update the psychological adjustment model and transmit the updated psychological adjustment model to the adjustment device 13.

[0047] The adjustment device 13 can perform the psychological adjustment mode on the user. In some embodiments, the adjustment device 13 can include at least one device such as a transcranial electrical stimulation (tES) device, a breathing training device, or other psychological adjustment device to assist the user in performing the psychological adjustment medical treatment.

[0048] In some embodiments, when the adjustment device 13 includes the transcranial electrical stimulation device, the analysis system 12 can select a current stimulation mode from a plurality of current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode. In some embodiments, the selected current stimulation mode can also be directly selected by an operator of the transcranial electrical stimulation device from the plurality of current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode. The transcranial electrical stimulation device can perform a cranial nerve stimulation on the user based on the selected current stimulation mode. The stimulation waveforms of the transcranial electrical stimulation device include transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial random noise stimulation (tRNS), etc.

[0049] In some embodiments, when the adjustment device 13 includes the transcranial electrical stimulation device, the sensing system 11 may include the physiological light signal measuring device. The physiological light signal measuring device may include a light source and a light receiving unit. The light source is used to emit a spectral signal of at least one light wavelength, and the light receiving unit is used to receive a feedback light signal generated after the spectral signal is reflected by the user as the physiological signal. The analysis system 12 can receive the physiological signal and evaluate a neural activity state of the user based on the physiological signal. The analysis system 12 can evaluate multiple stimulation parameters of the cranial nerve stimulation based on the neural activity state. In some embodiments, the multiple stimulation parameters may include but are not limited to parameters such as a stimulation position, a stimulation intensity, a stimulation time, and a stimulation frequency. In some embodiments, when the adjustment device 13 includes the transcranial electrical stimulation device, the sensing system 11 can be integrated into the adjustment device 13 to form a head-mounted device with both sensing and adjustment functions.

[0050] In some embodiments, since the transcranial electrical stimulation device can continuously stimulate the target area of ​​the user's head, and the sensing system 11 can continuously sense the user to update the physiological signal, the analysis system 12 can receive the updated physiological signal and analyze the updated physiological signal based on the analysis model to update the current stimulation pattern and provide it to the transcranial electrical stimulation device. In some embodiments, the analysis system 12 can confirm whether the transcranial electrical stimulation device has appropriately adjusted the cerebral cortical activity of the target area based on the analysis results, and further confirm whether the current stimulation pattern needs to be adjusted. In other words, the analysis system 12 can analyze the subsequently received physiological signal based on the analysis model to determine the subsequent psychological adjustment pattern.

[0051] In some embodiments, when the adjustment device 13 includes the breathing training device, the analysis system 12 can select a breathing training mode from a plurality of breathing training modes of the breathing training device as the psychological adjustment mode. In some embodiments, the selected breathing training mode can also be directly selected by an operator of the breathing training device from the plurality of breathing training modes of the breathing training device as the psychological adjustment mode. The breathing training device can guide the user to adjust their breathing based on the selected breathing training mode.

[0052] In some embodiments, since the sensing system 11 can continuously sense the user to update the physiological signals, the analysis system 12 can receive the updated physiological signals and analyze them based on the analysis model to update the breathing training pattern and provide it to the breathing training device. In some embodiments, the analysis system 12 can determine whether the existing breathing training pattern helps the user regulate their breathing based on the analysis results, and further determine whether the breathing training pattern needs to be adjusted. In other words, the analysis system 12 can analyze subsequently received physiological signals based on the analysis model to determine a subsequent psychological adjustment pattern.

[0053] Please refer to Figure 2A, which is a block diagram of an analysis system 12 for performing a psychological adjustment medical method according to the present invention. In some embodiments, the analysis system 12 may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing (Cloud Computing) consisting of a large number of hosts or network servers, a single edge computing device, and an edge computing system consisting of multiple edge computing devices. The network in which the analysis system 12 is located may include but is not limited to the Internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (VPN), etc. In other embodiments, the analysis system 12 may also include a single electronic device such as a mobile phone, a tablet computer, a desktop computer, a laptop computer, or a wearable device, or a combination of multiple electronic devices, which is not limited here.

[0054] The analysis system 12 includes an analysis receiving unit 121, an analysis transmitting unit 122, an analysis memory 123, and an analysis processor 124. The analysis processor 124 can be coupled to the analysis memory 123. The analysis memory 123 can store a plurality of instructions for the analysis processor 124 to execute the psychological adjustment medical method according to the plurality of instructions stored in the analysis memory 123. To implement the psychological adjustment medical method of the present invention, the analysis memory 123 stores a psychological adjustment medical program 1000. In the described embodiment, the psychological adjustment medical program 1000 further includes a signal correction module 1100, a deep learning module 1200, and a pattern confirmation module 1300. The signal correction module 1100 can be used to remove motion noise generated by the user's motion from the physiological signal based on the sensed motion signal to generate the corrected physiological signal. The deep learning module 1200 can be used to analyze the user's psychological or physiological state based on the corrected physiological signal using the analysis model generated by deep learning. The mode confirmation module 1300 can provide a psychological adjustment mode suitable for the user's current situation by analyzing the obtained psychological state or physiological state.

[0055] In some embodiments, when the analysis system 12 includes multiple network servers or multiple edge computing devices, one of the network servers or edge computing devices may have a program distribution module (not shown). The program distribution module can be used to determine how the psychological adjustment medical procedure 1000 is performed based on the distribution status of the signal correction module 1100, the deep learning module 1200, and the pattern confirmation module 1300 in the multiple network servers or the multiple edge computing devices. In other embodiments, the signal correction module 1100, the deep learning module 1200, and the pattern confirmation module 1300 can each be further split into multiple secondary subroutines and distributed in different network servers or edge computing devices.

[0056] The analysis receiving unit 121 and the analysis transmitting unit 122 can utilize custom protocols or comply with existing or de facto standards, including but not limited to Ethernet, IEEE 802.11 or IEEE 802.15 series, Wireless USB, or telecommunications standards (including but not limited to GSM, CDMA2000, TD-SCDMA, WiMAX, 3GPP-LTE, or TD-LTE), to transmit signals with the sensor transmitting unit and the sensor receiving unit in the sensing system 11. Furthermore, the analysis receiving unit 121 and the analysis transmitting unit 122 can also transmit signals with the adjustment device 13. In some embodiments, the analysis system 12 and different sensors in the sensing system 11 can use the same or different communication standards for signal transmission. In some embodiments, the analysis system 12 and the sensing system 11 and the adjustment device 13 can use the same or different communication standards for signal transmission.

[0057] Please refer to Figure 2B, which is a block diagram of another analysis system 12 for performing a psychological adjustment medical method according to the present invention. In some embodiments, the analysis system 12 includes an analysis device 220 and at least one computing and processing device. In some embodiments, the number of the at least one computing and processing device can be m, and the value m can be a positive integer greater than zero, such as 1, 2, 3. In some embodiments, the at least one computing and processing device can include a first computing and processing device 2241, a second computing and processing device 2242, ... and an mth computing and processing device 224m. In some embodiments, each of the at least one computing and processing devices can be an electronic device that can be used for computing, such as a network server, an edge computing device, or a general electronic device.

[0058] The analysis device 220 may further include an analysis receiving unit 221 and an analysis transmitting unit 222. In some embodiments, the analysis receiving unit 221 may be configured to receive the physiological signal while the psychological adjustment medical method is being performed. In some embodiments, the analysis receiving unit 221 and the analysis transmitting unit 222 may be coupled to the at least one computing and processing device while the psychological adjustment medical method is being performed, thereby transmitting signals from the analysis transmitting unit 222 to the at least one computing and processing device and receiving signals from the at least one computing and processing device via the analysis receiving unit 221. In some embodiments, after the psychological adjustment medical method is completed, the analysis device 220 may be decoupled from the at least one computing and processing device.

[0059] The at least one computing and processing device may each include a corresponding processing component (not shown in the figure), a storage component (not shown in the figure), a receiving component (not shown in the figure), and a transmitting component (not shown in the figure). In some embodiments, when performing the psychological adjustment medical method, each receiving component and transmitting component of the at least one computing and processing device may each be coupled to the analysis device 220, thereby receiving the information transmitted by the analysis device 220 and returning the processed results to the analysis device 220. In addition, after the respective receiving components of the at least one computing and processing device receive the signal from the analysis device 220, each processing component will use the received signal to generate the corresponding processing result according to the processing program stored in the corresponding storage component. In some embodiments, after the at least one computing and processing device completes its respective processing and returns the processing result to the analysis device 220, the at least one computing and processing device may stop coupling with the analysis device 220.

[0060] 2A and 2B , the psychological adjustment medical procedure 1000 can be divided into multiple subroutines, which are distributed across the analysis device 220 and the at least one computing and processing device. In some embodiments, the analysis device 220 may also include a program allocation module (not shown). This program allocation module is used to determine to which recipient the analysis and transmission unit 222 should transmit the desired signal based on the distribution of the multiple subroutines.

[0061] In some embodiments, the subroutine of the analysis device 220 may include only the program allocation module, while the at least one computational processing device may each include at least one of the signal correction module 1100, the deep learning module 1200, and the pattern confirmation module 1300. For example, when the value m is 3, the subroutine of the first computational processing device 2241 may include the signal correction module 1100, the subroutine of the second computational processing device 2242 may include the deep learning module 1200, and the subroutine of the third computational processing device 2243 may include the pattern confirmation module 1300. In other embodiments, when the value m is 2, the subroutine of the first computational processing device 2241 may include the signal correction module 1100, and the subroutine of the second computational processing device 2242 may include the deep learning module 1200 and the pattern confirmation module 1300. Furthermore, in some embodiments, when the value m is 1, the subroutine of the first computational processing device 2241 may include the signal correction module 1100, the deep learning module 1200, and the pattern confirmation module 1300. In some other embodiments, the plurality of subroutines may each be further divided into a plurality of sub-subroutines and distributed across different computing and processing devices, so that the number of the at least one computing and processing device may be greater than three.

[0062] In some embodiments, the subroutine of the analysis device 220 may include other modules in addition to the program distribution module. In other words, the signal correction module 1100, the deep learning module 1200, and the pattern confirmation module 1300 may each be distributed across the analysis device 220 and the at least one computing device. For example, when the value m is 2, the subroutine of the analysis device 220 may include the program distribution module and the pattern confirmation module 1300, the subroutine of the first computing device 2241 may include the signal correction module 1100, and the subroutine of the second computing device 2242 may include the deep learning module 1200. The above embodiments merely illustrate that the various modules of the psychological adjustment medical program 1000 can be separated and distributed across the analysis device 220 and the at least one computing device. However, the actual distribution method is not limited to the above examples; other distribution methods may also be used to distribute the load of each module across different analysis devices and computing devices.

[0063] Please refer to Figure 3, which is a block diagram of a mental state alarm program 3000 for performing a mental state alarm method according to the present invention. In some embodiments, the mental state alarm program 3000 further includes a signal correction module 3100, a deep learning module 3200, and an alarm module 3400. The signal correction module 3100 can be used to remove motion noise generated by the user's motion from the physiological signal based on the sensed motion signal to generate a corrected physiological signal. The deep learning module 3200 can be used to analyze the user's mental state or physiological state based on the corrected physiological signal using the analysis model generated by deep learning. In some embodiments, the physiological state may include physiological data. The alarm module 3400 can send an alarm signal when the physiological data exceeds a data threshold to prompt the user to pay attention to the mental state or physiological state.

[0064] 2A and 3 , the signal correction module 3100 and deep learning module 3200 in the mental state warning program 3000 can be identical to the signal correction module 1100 and deep learning module 1200 in the mental adjustment medical program 1000. Therefore, the mental state warning program 3000 can also be stored in the analysis memory 123 and executed by the analysis system 12.

[0065] Referring to FIG. 2B and FIG. 3 , the mental state alert program 3000 can also be split into multiple subroutines, which can be distributed across the analysis device 220 and the at least one computing device. In some embodiments, the analysis device 220 can also include a program distribution module to determine to which recipient the analysis transmission unit 222 should transmit the desired signal based on the distribution of the multiple subroutines.

[0066] In some embodiments, the subroutine of the analysis device 220 may include only the program distribution module, and the at least one computing and processing device may each include at least one of the signal correction module 3100, the deep learning module 3200, and the alarm module 3400. In other embodiments, the multiple subroutines may each be further divided into multiple sub-subroutines and distributed across different computing and processing devices.

[0067] In some embodiments, the subroutine of the analysis device 220 may include other modules in addition to the program distribution module. In other words, the signal correction module 3100, the deep learning module 3200, and the alarm module 3400 may each be distributed across the analysis device 220 and the at least one processing device. In all of the above embodiments, the individual modules of the mental state alarm program 3000 are merely shown as being split and distributed across the analysis device 220 and the at least one processing device. However, the actual distribution method is not limited to the above examples; other distribution methods may also be used to distribute the load of each module across different analysis devices and processing devices.

[0068] 1 , 2A , and 3 , the psychological adjustment medical procedure 1000 may further include an alarm module 3400. In other words, the analysis system 12 may continuously receive physiological signals from the user, and when the physiological data exceeds a threshold, the alarm module 3400 may send an alarm signal to prompt the user to execute the psychological adjustment mode provided by the mode confirmation module 1300 and suitable for the user's current condition through the adjustment device 13.

[0069] FIG4 is a flowchart of a psychological adjustment medical method 400 provided by the present invention and performed by at least one electronic device. Because there are multiple ways to perform the psychological adjustment medical method 400, the psychological adjustment medical method 400 shown in FIG4 is only an example. The psychological adjustment medical method 400 can be performed using the configurations shown in FIG1 , FIG2A , FIG2B and FIG3 or other configurations, and when describing the psychological adjustment medical method 400, please refer to the various components in FIG1 , FIG2A , FIG2B and FIG3 in combination. Each step shown in FIG4 may represent one or more processes, methods or subroutines performed, and the order of each step can be adjusted arbitrarily without causing the essence of the psychological adjustment medical method 400 to deviate from the scope of the technical solution of the psychological adjustment medical method 400.

[0070] In step S410 , a physiological signal obtained by sensing a user is received.

[0071] In some embodiments, the sensing system 11 can sense the user to obtain the physiological signal. The physiological signal can include at least one of a physiological electrical signal, a physiological light signal, and a motion signal. The at least one electronic device can receive the physiological signal obtained by the sensing system sensing the user.

[0072] In some embodiments, the physiological electrical signal may include at least one of an electrocardiogram (ECG) signal, an electromyogram (EMG) signal, and an electroencephalogram (EEG) signal. In some embodiments, the physiological optical signal may include a feedback optical signal. The feedback optical signal is generated by a spectral signal emitted by the sensing system 11 and reflected from a sensing target area of ​​the user. In some embodiments, the motion signal may include at least one of an acceleration signal and an angular acceleration signal.

[0073] In some embodiments, when the analysis system 12 is composed of a single electronic device, the single electronic device can receive the physiological signal from the sensing system 11. In some embodiments, when the analysis system 12 is composed of multiple electronic devices, at least one of the multiple electronic devices can receive the physiological signal from the sensing system 11. For example, the analysis device 220 can receive the physiological signal from the sensing system 11.

[0074] In step S420 , the physiological signal is analyzed based on an analysis model to identify a psychological adjustment mode.

[0075] In some embodiments, the physiological signal may include a motion signal generated by a movement of the user. In some embodiments, after the signal correction module 1100 obtains the physiological signal including the motion signal, the signal correction module 1100 may remove motion noise generated by the movement from other physiological signals based on the motion signal to obtain a corrected physiological signal.

[0076] In some embodiments, the deep learning module 1200 may extract physiological data of the user based on the physiological signal using a network model within the analysis model. In some embodiments, the deep learning module 1200 extracts the physiological data of the user from the corrected physiological signal using the network model. In some embodiments, the physiological data may include at least one of respiratory data of the user or blood oxygenation data within the target sensing area of ​​the user. In some embodiments, the deep learning module 1200 may determine a psychological state of the user based on the physiological data using a matrix analysis within the analysis model.

[0077] In some embodiments, the respiratory data includes at least one of an inhalation state, an inhalation time, an inhalation amplitude, an exhalation state, an exhalation time, an exhalation amplitude, and a respiratory rate. In some embodiments, the blood oxygenation data includes at least one of a change in oxyhemoglobin concentration, a change in deoxyhemoglobin concentration, and a change in blood oxygenation concentration.

[0078] In some embodiments, the mode confirmation module 1300 may confirm the psychological adjustment mode based on the psychological state. In some embodiments, the adjustment device 13 may include at least one device such as a transcranial electrical stimulation device, a breathing training device, or other psychological adjustment devices.

[0079] In some embodiments, when the adjustment device 13 includes the transcranial electrical stimulation device, the mode confirmation module 1300 can select a current stimulation mode from multiple current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode. In some embodiments, the mode confirmation module 1300 can evaluate a neural activity state of the user based on the physiological signal, and based on the neural activity state, evaluate multiple stimulation parameters of the cranial nerve stimulation. In some embodiments, the multiple stimulation parameters may include, but are not limited to, parameters such as a stimulation location, a stimulation intensity, a stimulation time, and a stimulation frequency.

[0080] In some embodiments, when the adjustment device 13 includes the breathing training device, the mode confirmation module 1300 may select a breathing training mode from a plurality of breathing training modes of the breathing training device as the psychological adjustment mode.

[0081] In step S430, the psychological adjustment mode is sent.

[0082] In some embodiments, the psychological adjustment pattern can be sent to the adjustment device 13. The adjustment device 13 can include at least one device such as the transcranial electrical stimulation device, the breathing training device, or other psychological adjustment devices.

[0083] In some embodiments, the transcranial electrical stimulation device stimulates a cranial nerve of the user based on the selected current stimulation pattern. In some embodiments, the transcranial electrical stimulation device can stimulate the cranial nerve at the stimulation location of the user based on the multiple stimulation parameters.

[0084] In some embodiments, the breathing training device guides the user to regulate breathing based on the selected breathing training mode. In some embodiments, the breathing training device may be a display screen, and the display screen may guide the user to regulate breathing based on the selected breathing training mode. For example, the display screen may prompt the user when to inhale and exhale. In some embodiments, when the analysis system 12 includes a single electronic device such as a mobile phone, a tablet computer, a desktop computer, a laptop computer or a wearable device, or a combination of multiple electronic devices, the display screen may be the display screen of one of the electronic devices. In some embodiments, the breathing training device may be a breathing assistance device, and the breathing assistance device may guide the user to regulate breathing based on the selected breathing training mode.

[0085] FIG5 is a flowchart of a mental state alert method 500 provided by the present invention and performed via at least one electronic device. Because there are numerous ways to implement the mental state alert method 500, the mental state alert method 500 illustrated in FIG5 is merely an example. The mental state alert method 500 may be implemented using the configurations illustrated in FIG1 , FIG2A , FIG2B , and FIG3 , or other configurations. When describing the mental state alert method 500, please refer to the various components in FIG1 , FIG2A , FIG2B , and FIG3 . Each step shown in FIG5 may represent one or more processes, methods, or subroutines to be performed, and the order of each step may be adjusted arbitrarily without deviating from the essence of the mental state alert method 500 and the scope of the technical solution of the mental state alert method 500.

[0086] In step S510, a physiological signal obtained by sensing a user is received. In some embodiments, step S510 may be identical to step S410.

[0087] In some embodiments, the sensing system 11 can sense the user to obtain the physiological signal. The physiological signal can include at least one of a physiological electrical signal, a physiological light signal, and a motion signal. The at least one electronic device can receive the physiological signal obtained by the sensing system sensing the user.

[0088] In step S520 , the physiological signal is analyzed based on an analysis model to obtain physiological data. In some embodiments, step S520 may be identical to step S420 .

[0089] In some embodiments, the physiological signal may include a motion signal generated by a movement of the user. In some embodiments, after the signal correction module 3100 obtains the physiological signal including the motion signal, the signal correction module 3100 may remove motion noise generated by the movement from other physiological signals based on the motion signal to obtain a corrected physiological signal.

[0090] In some embodiments, the deep learning module 3200 may extract physiological data of the user based on the physiological signal using a network model within the analysis model. In some embodiments, the deep learning module 3200 extracts the physiological data of the user from the corrected physiological signal using the network model. In some embodiments, the physiological data may include at least one of respiratory data of the user or blood oxygenation data within the target sensing area of ​​the user. In some embodiments, the deep learning module 3200 may determine a psychological state of the user based on the physiological data using a matrix analysis within the analysis model.

[0091] In some embodiments, the respiratory data includes at least one of an inhalation state, an inhalation time, an inhalation amplitude, an exhalation state, an exhalation time, an exhalation amplitude, and a respiratory rate. In some embodiments, the blood oxygenation data includes at least one of a change in oxyhemoglobin concentration, a change in deoxyhemoglobin concentration, and a change in blood oxygenation concentration.

[0092] In step S530, it is determined whether the physiological data exceeds a data threshold. If the physiological data exceeds the data threshold, the mental state alarm method 500 proceeds to step S540. If the physiological data does not exceed the data threshold, the mental state alarm method 500 ends.

[0093] In some embodiments, the alarm module 3400 can confirm whether the physiological data exceeds the data threshold to assess whether the mental state alarm method 500 needs to proceed to step S540. For example, when the user's breathing rate exceeds a frequency threshold related to breathing in the data threshold, the alarm module 3400 can confirm that the physiological data exceeds the data threshold. In some embodiments, the frequency threshold related to breathing can be 10 seconds. In some embodiments, the frequency threshold can further include an inhalation frequency threshold and an exhalation frequency threshold. In some embodiments, the inhalation frequency threshold can be 4 seconds, and the exhalation frequency threshold can be 6 seconds. In other embodiments, the physiological data can also include an amplitude difference between a respiratory amplitude of the physiological signal related to breathing and a standard amplitude. If the amplitude difference is greater than the amplitude difference threshold, it means that the user's breathing is too short, and the physiological data can be confirmed to have exceeded the data threshold.

[0094] In some embodiments, when the alarm module 3400 confirms that the physiological data does not exceed the data threshold, the alarm module 3400 can terminate the mental state alarm method 500. In other embodiments, because the sensing system 11 can continuously sense and update the user's physiological signals, the analysis system 12 can also continuously receive the updated physiological signals. In other words, when the alarm module 3400 confirms that the current physiological data does not exceed the data threshold, the mental state alarm method 500 can still return to step S510 to continue receiving the updated physiological signals.

[0095] In step S540, an alarm signal is sent.

[0096] In some embodiments, the alarm module 3400 may send an alarm signal to remind the user to pay attention to the psychological state or the physiological state. For example, the alarm module 3400 may send the alarm signal to suggest the user to use the psychological adjustment medical method 400.

[0097] In some embodiments, the psychological adjustment medical method 400 and the psychological state alarm method 500 can be integrated. In other words, when the alarm module 3400 sends the alarm signal, the mode confirmation module 1300 can analyze the acquired psychological or physiological state and provide a psychological adjustment mode suitable for the user's current condition. Therefore, the alarm module 3400 can use the alarm signal to notify the user that the psychological adjustment mode suitable for the user's current condition can be implemented through the adjustment device 13.

[0098] In some embodiments, when the adjustment device 13 includes a transcranial electrical stimulation device, the mode confirmation module 1300 may select a current stimulation mode from multiple current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode. In some embodiments, when the adjustment device 13 includes a breathing training device, the mode confirmation module 1300 may select a breathing training mode from multiple breathing training modes of the breathing training device as the psychological adjustment mode.

[0099] Figure 6 is a flow chart of a psychological adjustment analysis method 600 performed by at least one electronic device, provided by the present invention. Because there are many ways to implement the psychological adjustment analysis method 600, the method shown in Figure 6 is merely an example. The psychological adjustment analysis method 600 can be implemented using the configurations shown in Figures 1, 2A, 2B, and 3, or other configurations. When describing the psychological adjustment analysis method 600, please refer to the various components in Figures 1, 2A, 2B, and 3. Each step shown in Figure 6 may represent one or more processes, methods, or subroutines performed, and the order of each step may be adjusted arbitrarily without deviating from the essence of the psychological adjustment analysis method 600 within the scope of the technical solution of the psychological adjustment analysis method 600. In some embodiments, the psychological adjustment analysis method 600 may include the specific process of step S420 in the psychological adjustment medical method 400 or the specific process of step S520 in the mental state alarm method 500.

[0100] In step S610 , a motion signal of a user is obtained.

[0101] In some embodiments, the physiological signal may include a motion signal generated in response to a movement of the user. In some embodiments, the signal correction module 1100 or the signal correction module 3100 may obtain the physiological signal including the motion signal. In some embodiments, the motion signal may include at least one of an acceleration signal and an angular acceleration signal.

[0102] In step S620 , based on the motion signal, a motion noise generated by the motion in the physiological signal is removed to obtain a corrected physiological signal.

[0103] In some embodiments, the signal correction module 1100 or the signal correction module 3100 may input the motion signal into an adaptive filter algorithm to obtain the motion noise. The signal correction module 1100 or the signal correction module 3100 may then subtract the motion noise from the physiological signal to obtain the corrected physiological signal.

[0104] In some embodiments, the signal correction module 1100 or the signal correction module 3100 may generate a residual signal based on the corrected physiological signal, and feed the residual signal back to the adaptive filtering algorithm to update the noise prediction model of the motion noise.

[0105] In step S630 , the physiological data of the user is extracted from the calibrated physiological signal through the network model.

[0106] In some embodiments, the physiological signal may be a near-infrared spectroscopy (NIRS) signal or a functional near-infrared spectroscopy (fNIRS) signal. The NIRS and fNIRS signals are sensitive to oxygenated and deoxygenated hemoglobin in body tissues and can therefore be used to assess changes in blood oxygen concentration in body tissues. Furthermore, because respiration also triggers a hemodynamic response, respiratory information is also reflected in the NIRS and fNIRS signals.

[0107] In some embodiments, the network model may be a Long-Short Time Memory (LSTM) network with a built-in enhanced memory mechanism to learn long-term dependencies for the purpose of capturing the physiological data.

[0108] In some embodiments, when the captured physiological data is respiratory data, the respiratory data may include at least one of an inhalation state, an inhalation time, an inhalation amplitude, an exhalation state, an exhalation time, an exhalation amplitude, and a respiratory rate.

[0109] Figures 7A and 7B respectively show schematic diagrams of a NIRS signal for a target sensing area and a respiration-related signal extracted by the network model. The NIRS signal for the target sensing area is measured when the user takes a deep breath. Therefore, the NIRS signal for the target sensing area includes a blood pressure-related signal with higher-frequency signal components, such as vasodilation and vasoconstriction, and a respiration-related signal with lower-frequency signal components. Deep learning module 1200 or deep learning module 3200 can extract the respiration-related signal from the NIRS signal through the LSTM model and classify the information in the respiration-related signal to obtain the respiration data.

[0110] In some embodiments, when the captured physiological data is blood oxygen data, the blood oxygen data includes at least one of an oxygenated hemoglobin concentration change, a deoxygenated hemoglobin concentration change, and a blood oxygen concentration change.

[0111] In some embodiments, the fNIRS signal is based on the hemodynamic response (HDR) and can display physiological information between neuronal activity and local blood flow changes. When neurons are activated, if neuronal activity increases, the vascular response mechanism will provide more oxygen-rich blood to the activated area, resulting in a significant increase in the oxygenated hemoglobin concentration and a decrease in the deoxygenated hemoglobin concentration. Therefore, when the user's brain is stimulated, the fNIRS signal can record the changes in blood oxygen concentration in the sensing target area and be used to evaluate the neural activity response caused by the stimulation. The deep learning module 1200 or the deep learning module 3200 can extract a blood oxygen-related signal from the fNIRS signal through the LSTM model to obtain the blood oxygen data, and further evaluate whether the current stimulation pattern has been properly adjusted to the cerebral cortical activity of the sensing target area when the current stimulation pattern has been applied.

[0112] In step S640 , a psychological state of the user is confirmed based on the physiological data through a matrix analysis in the analysis model.

[0113] In some embodiments, the matrix analysis may be a sliding window correlation matrix operation. The deep learning module 1200 may perform correlation coefficient calculations based on the matrix analysis and input the calculation results into a pre-trained deep neural network (DNN) to generate a label corresponding to a psychological state. In some embodiments, the deep neural network is trained using a default dataset. In some embodiments, the deep neural network may use the user's past physiological signals as input to adjust network weights based on the user's own condition, thereby generating a deep neural network adapted to the user's condition.

[0114] The above embodiments are intended only to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may be modified, or some of the technical features thereof may be replaced by equivalents. Such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments of the present invention. However, the above description is merely a preferred embodiment of the present invention. Any equivalent modifications or variations made by those skilled in the art in accordance with the spirit of the present invention should be included in the following claims.

Claims

1. A psychological adjustment medical method performed by at least one electronic device, the psychological adjustment medical method comprising: Receiving a physiological signal obtained by sensing a user; Analyzing the physiological signal based on an analysis model to confirm a psychological adjustment mode, wherein the confirmation of the psychological adjustment mode further includes: Based on the physiological signal, extracting physiological data of the user through a network model in the analysis model, wherein the physiological data includes at least one of breathing data of the user or blood oxygen data in a sensing target area of ​​the user; Based on the physiological data, confirming a psychological state of the user through a matrix analysis in the analysis model; and Based on the psychological state, identifying the psychological adjustment mode; and Send this mental adjustment mode.

2. The psychological adjustment medical method as claimed in claim 1, wherein the confirmation of the psychological adjustment mode further comprises: obtaining an action signal of the user, wherein the action signal is related to an action of the user; Based on the motion signal, removing a motion noise generated by the motion in the physiological signal to obtain a corrected physiological signal; as well as The physiological data of the user is extracted from the corrected physiological signal through the network model.

3. The psychological adjustment medical method according to claim 1, wherein: The respiratory data includes at least one of an inhalation state, an inhalation time, an inhalation amplitude, an exhalation state, an exhalation time, an exhalation amplitude, and a respiratory frequency, and The blood oxygen data includes at least one of an oxyhemoglobin concentration change, a deoxyhemoglobin concentration change, and a blood oxygen concentration change.

4. The psychological adjustment medical method as claimed in claim 1, further comprising: receiving an updated physiological signal, and analyzing the updated physiological signal based on the analysis model to update the psychological adjustment mode; as well as The updated psychological adjustment mode is transmitted to an adjustment device that receives the psychological adjustment mode, wherein a sensing system that generates the physiological signal continuously senses the user to generate the updated physiological signal.

5. The psychological adjustment medical method as claimed in claim 1, further comprising: When an adjustment device that receives the psychological adjustment mode includes a transcranial electrical stimulation device, a current stimulation mode is selected from multiple current stimulation modes of the transcranial electrical stimulation device as the psychological adjustment mode, wherein the transcranial electrical stimulation device performs a cranial nerve stimulation on the user based on the selected current stimulation mode.

6. The psychological adjustment medical method as claimed in claim 5, further comprising: Based on the physiological signal, evaluating a neural activity state of the user; as well as Based on the neural activity state, a plurality of stimulation parameters for stimulation of the cranial nerve are evaluated.

7. The psychological adjustment medical method as claimed in claim 1, further comprising: When an adjustment device receiving the psychological adjustment mode includes a breathing training device, a breathing training mode is selected from multiple breathing training modes of the breathing training device as the psychological adjustment mode, wherein the breathing training device guides the user to adjust breathing based on the selected breathing training mode.

8. The psychological adjustment medical method as claimed in claim 1, wherein the physiological signal is used to evaluate at least one physiological parameter of the user's heart rate parameter, respiratory rate parameter, skin conductance parameter and muscle tension parameter.

9. The psychological adjustment medical method as claimed in claim 1, further comprising: When the physiological data exceeds a data threshold, an alarm signal is sent to prompt the user to execute the psychological adjustment mode through an adjustment device that receives the psychological adjustment mode.

10. An electronic device comprising: at least one processor; as well as At least one non-transitory computer-readable medium, which is coupled to the at least one processor and stores at least one computer-executable instruction, wherein when the at least one computer-executable instruction is executed by the at least one processor, the electronic device executes the psychological adjustment medical method as described in any one of claims 1 to 9.

11. A psychological adjustment medical system, comprising: An analysis system comprising at least one electronic device, wherein the at least one electronic device is used to execute the psychological adjustment medical method as claimed in any one of claims 1 to 9.

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