Phototherapy physiotherapy instrument intelligent control system based on multi-mode feedback
The intelligent control system of the phototherapy device, which utilizes multimodal feedback, adjusts the therapy parameters in real time based on user information and status, solving the problem that existing therapy devices cannot achieve optimal results and enabling a personalized and safe therapy process.
Patent Information
- Application Number
- CN202610413540.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-03-31
- Publication Date
- 2026-05-15
AI Technical Summary
Existing physiotherapy device control systems cannot achieve optimal physiotherapy results based on the age, gender, weight, and physical condition of different users.
The intelligent control system of the phototherapy physiotherapy device adopts multimodal feedback. It obtains basic user information and physiotherapy needs information through the information input terminal, matches the user model using the user model matching module, obtains user status information in combination with the sensor group, and the microprocessor makes real-time adjustments to the physiotherapy control strategy based on the user model, including the execution modules of phototherapy and electrotherapy.
It enables real-time adjustment of physiotherapy parameters based on the user's personalized physiological parameters and changes in condition, ensuring the safety and effectiveness of the physiotherapy process.
Smart Images
Figure CN122031941A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of intelligent control of physiotherapy devices, and in particular to an intelligent control system for a phototherapy physiotherapy device based on multimodal feedback. Background Technology
[0002] Physiotherapy devices are key equipment in physical therapy. They apply physical factors such as light, electricity, sound, magnetism, and heat to the human body to achieve therapeutic goals such as relieving pain, promoting tissue repair, and restoring muscle function. Their development has evolved from simple energy output devices to intelligent, interactive, and adaptive medical devices. Early physiotherapy device control systems were extremely simple, often using analog circuits for fixed or roughly adjustable energy output. The treatment process heavily relied on the operator's experience, lacking quantification, standardization, and personalization. With the rapid development of microelectronics, sensor technology, embedded systems, and digital signal processing technology, the control systems of modern physiotherapy devices have undergone a fundamental transformation. Their core objective has shifted from "achieving energy output" to "achieving precise, safe, and personalized dosage control and efficacy optimization." This transformation has given rise to a comprehensive control system integrating sophisticated hardware, complex algorithms, and user-friendly interaction, becoming the core differentiator of the value and effectiveness of modern physiotherapy devices.
[0003] Existing physiotherapy device control systems can retrieve corresponding physiotherapy parameters from a database based on physiotherapy needs, such as relieving muscle soreness, postoperative rehabilitation, and treatment of neurological diseases, in order to meet the specific physiotherapy needs of different users. However, while the physiotherapy parameters set for users of different ages, genders, weights, and physical conditions can fulfill the physiotherapy needs, they cannot achieve the best physiotherapy effect for different users. Therefore, how to determine the user's condition and thus the optimal physiotherapy parameters based on existing sensor technology is the fundamental problem that this invention aims to solve. Summary of the Invention
[0004] In order to determine the user's state and thus the optimal physiotherapy parameters based on existing sensor technology, this application provides an intelligent control system for a phototherapy physiotherapy device based on multimodal feedback, which adopts the following technical solution: The intelligent control system for phototherapy devices based on multimodal feedback includes: The information input terminal is used to obtain basic user information and physiotherapy needs information; The user model matching module is used to match user models based on basic user information and physiotherapy needs information. The sensor group includes a temperature sensor, a surface electromyography signal sensor, a blood oxygen monitoring module, a tension sensor, and a cluster of user physiological signal sensors, used to acquire user status information; The microprocessor is used to obtain the corresponding physiotherapy control strategy based on the user model and to adjust the physiotherapy control strategy in real time based on the user's status information. The execution module is used to sequentially perform phototherapy and electrotherapy according to the microprocessor's physiotherapy strategy.
[0005] Optionally, the user model matching process includes: Several preset models are established based on big data. Each preset model corresponds to a range of physiotherapy needs information and user basic information. The user basic information includes user age, user gender and user BMI. Based on the physiotherapy needs information, the preset models are initially screened to obtain the preset models to be selected; The user's basic information is compared with the range of user basic information corresponding to the preset models to be selected, and the preset model with the highest degree of overlap is selected as the user model.
[0006] Optionally, the user status information includes the temperature of the user's physiotherapy area, blood oxygen saturation, muscle fatigue coefficient, and the user's basic physiological parameters; The process of making real-time adjustments to physical therapy control strategies includes: Determine whether to stop physical therapy based on the range and trend of the user's basic physiological parameters; The control parameters of the phototherapy process are adjusted according to the temperature and blood oxygen saturation of the user's treatment area; the control parameters of the phototherapy process include light wavelength, power density and irradiation time; The control parameters of the electrotherapy process are adjusted according to the muscle fatigue coefficient; the control parameters of the electrotherapy process include pulse intensity, pulse frequency, pulse width and pulse waveform type.
[0007] Optionally, the process of determining whether to stop physical therapy includes: Determine if the user's basic physiological parameters exceed the corresponding numerical range: If a user's baseline physiological parameters exceed the corresponding numerical range, the physiotherapy process will be stopped. If there are no user basic physiological parameters exceeding the corresponding numerical range, first obtain the absolute value of the difference between each user basic physiological parameter at the current time point and the median value of the corresponding numerical range, △p1. Then obtain the maximum change △p2 of each user basic physiological parameter during the △t period before the current time point. After normalizing △p1 and △p2, perform a weighted sum to obtain the state value of the user basic physiological parameter. Perform a weighted sum of the state values of all user basic physiological parameters and the corresponding influence coefficients to obtain the user state value. Compare the user state value with the preset value. When the user state value exceeds the preset value, stop the physiotherapy process.
[0008] Optionally, the process of adjusting the control parameters of the phototherapy process based on the temperature and blood oxygen saturation of the user's treatment area includes: The temperature difference ΔT and blood oxygen saturation difference ΔBo of the user's treatment area and the corresponding reference values are obtained respectively. The adjustment strategy of the phototherapy process control parameters is obtained according to the range of the temperature difference ΔT and the range of the blood oxygen difference ΔBo.
[0009] Optionally, the process of obtaining the muscle fatigue coefficient includes: The total action potentials generated by muscle fibers under nerve electrical signal stimulation are detected in real time by a surface electromyography signal sensor. The first muscle fatigue coefficient is obtained according to the value of the total action potentials. The value range of the first muscle fatigue coefficient is 0~1. A constant pressure is applied to the muscle using a tension sensor, and its deformation resistance is measured. The second muscle fatigue coefficient is obtained based on the range of the static muscle tension value, which is 0 to 1. The reliability ratio of the first muscle fatigue coefficient to the second muscle fatigue coefficient is obtained based on the user model. : , Through computational model: Muscle fatigue coefficient = First muscle fatigue coefficient Second muscle fatigue coefficient The muscle fatigue coefficient was calculated.
[0010] Optionally, the process of adjusting the control parameters of the electrotherapy procedure based on the muscle fatigue coefficient includes: Compare the muscle fatigue coefficient with the preset fatigue coefficient benchmark value: If the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value is less than the allowable error value, then the existing control parameters of the electrotherapy process are maintained. Otherwise, an adjustment strategy for the electrotherapy process control parameters is obtained based on the range of the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value.
[0011] Optionally, the user's basic physiological parameters include blood pressure, heart rate, and blood oxygen saturation.
[0012] In summary, this application includes at least one of the following beneficial technical effects: This invention, through a user model matching process, can initially obtain relatively accurate control parameters based on the user's physiotherapy needs and basic information. Combined with subsequent physiotherapy control strategies, the process is adjusted in real time, thereby accurately obtaining control parameters most suitable for the user's condition for physiotherapy. This invention can more sensitively determine whether to stop the physiotherapy process based on changes in the user's physiological parameters, thus ensuring the user's physical condition during the physiotherapy process. Attached Figure Description
[0013] Figure 1 This is a logic diagram of the intelligent control system for a multimodal feedback phototherapy device. Detailed Implementation
[0014] The embodiments of this application are described in detail below, and examples of the embodiments are shown in the accompanying drawings.
[0015] In the description of this specification, the references to "certain embodiments," "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples" refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.
[0016] This application discloses an intelligent control system for a phototherapy device based on multimodal feedback, applicable to physiotherapy equipment with phototherapy and electrotherapy capabilities, as described above. Figure 1The system includes an information input terminal, a user model matching module, a sensor group, a microprocessor, and an execution module. In this embodiment, the control system acquires basic user information and physiotherapy needs information through the information input terminal. This process is initiated before physiotherapy by the user or physiotherapy personnel selecting information on the physiotherapy device's display screen. Afterward, the user model matching module matches a user model based on the basic user information and physiotherapy needs information. The user model matching process includes: firstly, establishing several preset models based on big data, primarily usage data of the same model of equipment; secondly, classifying and organizing the data according to the corresponding physiotherapy needs and user basic information range, thereby dividing the data into multiple preset models. Each preset model corresponds to a range of physiotherapy needs information and user basic information. Since there are control parameters for the phototherapy and pre-electrotherapy process, the preset models are first screened based on the physiotherapy needs information, and the preset models corresponding to the physiotherapy needs information are selected as the candidate preset models. Then, the user's basic information is compared with the range of user basic information corresponding to the candidate preset models, and the preset model with the highest degree of overlap is selected as the user model. It should be noted that since the user model is integrated using big data during the establishment process, the comprehensiveness of the user model is ensured, that is, the user's basic information is all within the range of the user basic information corresponding to the user model. Therefore, by matching the user's basic information item by item, the preset model with the highest degree of overlap means that every item in the user's basic information is within its range.
[0017] In this embodiment, the user's basic information includes the user's age, gender, and BMI. Through the matching process of the user model described above, relatively accurate control parameters can be initially obtained based on the user's physiotherapy needs and basic information. Combined with the subsequent real-time adjustment process of the physiotherapy control strategy, the control parameters that best match the user's condition can be accurately obtained for physiotherapy.
[0018] Furthermore, the sensor group in this embodiment includes a temperature sensor, a blood oxygen monitoring module, a surface electromyography (EMG) signal sensor, a tension sensor, and a user physiological signal sensor cluster, used to acquire user status information. The user status information includes the temperature of the user's treatment area, blood oxygen saturation, muscle fatigue coefficient, and the user's basic physiological parameters. The temperature of the user's treatment area is obtained through the temperature sensor, the blood oxygen saturation is obtained through the blood oxygen monitoring module, and the muscle fatigue coefficient is obtained by combining the surface EMG signal sensor and the tension sensor. The user's basic physiological parameters in this embodiment include blood pressure, heart rate, and blood oxygen saturation; therefore, the user physiological signal sensor cluster includes a blood pressure detection module, a heart rate detection module, and a blood oxygen monitoring module. It should be noted that the two sets of blood oxygen saturation measurements are detected independently, one for the treatment area and one for the non-treatment area; therefore, there is a certain difference between the two values. These parameters enable real-time adjustment of the physiotherapy control strategy.
[0019] Furthermore, in this embodiment, the muscle fatigue coefficient is obtained simultaneously through a surface electromyography (EMG) sensor and a tension sensor. The acquisition process includes: firstly, the surface EMG sensor detects in real-time the sum of action potentials generated by muscle fibers under nerve electrical signal stimulation; secondly, the first muscle fatigue coefficient is obtained based on the value of the sum of action potentials. The first muscle fatigue coefficient is obtained by normalizing the value of the sum of action potentials. This normalization process is achieved by: First muscle fatigue coefficient = (Value of the sum of action potentials - Minimum value of the sum of action potentials) / (Maximum value of the sum of action potentials - Minimum value of the sum of action potentials). Therefore, the first muscle fatigue coefficient... The coefficient ranges from 0 to 1. A constant pressure is applied to the muscle using a tension sensor, and its deformation resistance is measured. A second muscle fatigue coefficient is obtained based on the range of the static muscle tension value. This second muscle fatigue coefficient is obtained by normalizing the static muscle tension value. The normalization process is: Second muscle fatigue coefficient = (Static muscle tension value - Minimum static muscle tension value) / (Maximum static muscle tension value - Minimum static muscle tension value). Therefore, the second muscle fatigue coefficient ranges from 0 to 1. The reliability ratio of the first muscle fatigue coefficient to the second muscle fatigue coefficient is obtained based on the user model. : , In the process of assessing muscle fatigue, surface electromyography (SEMG) sensors and tension sensors judge the muscle fatigue state from two different perspectives. Therefore, they reflect the muscle state from different angles. However, the accuracy of the two methods varies depending on the user model. Therefore, the weights of the two detection methods are dynamically adjusted according to different user models. The model is calculated as follows: Muscle fatigue coefficient = First muscle fatigue coefficient. Second muscle fatigue coefficient This is used to calculate the muscle fatigue coefficient, thereby improving the accuracy of muscle fatigue coefficient assessment.
[0020] It should be noted that the reliability ratios for different user models vary. : The accuracy of the two detection methods is determined based on the preset model in the big data.
[0021] Furthermore, the microprocessor is used to obtain the corresponding physiotherapy control strategy based on the user model and to adjust the physiotherapy control strategy in real time based on the user's status information. The process of adjusting the physiotherapy control strategy in real time includes: firstly, determining whether to stop physiotherapy based on the numerical range and changing trend of the user's basic physiological parameters; the process includes: firstly, determining whether the user's basic physiological parameters exceed the corresponding numerical range; if there are user basic physiological parameters exceeding the corresponding numerical range, it indicates that the user's basic physiological parameter is abnormal, that is, the user's physical condition is not good and is not suitable for the physiotherapy process, therefore the physiotherapy process is stopped; if there are no user basic physiological parameters exceeding the corresponding numerical range, the existing physiotherapy system will directly allow the user to perform the physiotherapy process, while the control system in this embodiment will dynamically monitor the changing status of the user's basic physiological parameters and the numerical range of the user's basic physiological parameters. Firstly, it obtains the absolute value △p1 of the difference between each user basic physiological parameter at the current time point and the median value of the corresponding numerical range. The larger the value of △p1, the higher the degree of deviation from the center value, although the user's basic physiological parameter is within the range; then it obtains the values before the current time point. The maximum change Δp2 for each user's basic physiological parameter during the time period Δt is determined. The larger the value of Δp2, the more drastic the change in that user's basic physiological parameter. Therefore, the user's physiological state is judged by the magnitude of Δp1 and Δp2. After normalization, Δp1 and Δp2 are weighted and summed. The normalization process is to divide Δp1 and Δp2 by their respective thresholds. When Δp1 and Δp2 are greater than or equal to the threshold, the normalization value is 1. The corresponding weights are obtained by integrating test data to obtain the state value of the user's basic physiological parameter. The state values of all user basic physiological parameters are weighted and summed with their corresponding influence coefficients. The corresponding weights are set according to the influence of different user basic physiological parameters in the test big data to obtain the user's state value. The user's state value is compared with a preset value, which is a value close to the value calculated from the test data. Therefore, when the user's state value exceeds the preset value, the physiotherapy process is stopped. Through the above process, compared with existing technologies, it is possible to more sensitively determine whether to stop the physiotherapy process based on the changes in the user's physiological parameters, thereby ensuring the user's physical condition during the physiotherapy process.
[0022] Subsequently, the control parameters of the phototherapy process are adjusted based on the temperature and blood oxygen saturation of the user's treatment area. These control parameters include light wavelength, power density, and irradiation time. The process of adjusting these parameters involves: obtaining the temperature difference ΔT and blood oxygen difference ΔBo between the user's treatment area and corresponding reference values. The reference values are set based on empirical data, for example, a temperature reference value of 36.5 degrees Celsius; and obtaining the corresponding values based on the range of the temperature difference ΔT and blood oxygen difference ΔBo. Regarding the adjustment strategy for the control parameters of the phototherapy process, it should be noted that this embodiment achieves multiple combinations based on different temperature difference ranges ΔT and different blood oxygen difference ranges ΔBo. Each combination obtains the optimal control parameter adjustment value based on test data. Therefore, based on the range of temperature difference ΔT and blood oxygen difference ΔBo, the corresponding control parameter adjustment values for the phototherapy process can be obtained, which serve as the adjustment strategy for the control parameters of the phototherapy process. It should be noted that the specific control parameter adjustment values are obtained through multiple tests, thus ensuring the effectiveness of the phototherapy process.
[0023] Subsequently, the control parameters of the electrotherapy process are adjusted based on the muscle fatigue coefficient. These control parameters include pulse intensity, pulse frequency, pulse width, and pulse waveform type. The process of adjusting the control parameters based on the muscle fatigue coefficient includes: comparing the muscle fatigue coefficient with a preset fatigue coefficient benchmark value, which is set according to user selection. If the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value is less than the allowable error value, it indicates that the currently set control parameters do not need adjustment, and therefore the existing control parameters of the electrotherapy process are maintained. Otherwise, an adjustment strategy for the electrotherapy process control parameters is obtained based on the range of the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value. It should be noted that in this embodiment, corresponding control parameters are set based on the range of differences between different preset fatigue coefficient benchmark values. These control parameters can provide the optimal physiotherapy state for the fatigued state, and thus, based on the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value, an adjustment strategy for the electrotherapy process control parameters can be obtained.
[0024] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. An intelligent control system for a phototherapy device based on multimodal feedback, characterized in that, include: The information input terminal is used to obtain basic user information and physiotherapy needs information; The user model matching module is used to match user models based on basic user information and physiotherapy needs information. The sensor group includes a temperature sensor, a blood oxygen monitoring module, a surface electromyography signal sensor, a tension sensor, and a cluster of user physiological signal sensors, used to acquire user status information; The microprocessor is used to obtain the corresponding physiotherapy control strategy based on the user model and to adjust the physiotherapy control strategy in real time based on the user's status information. The execution module is used to sequentially perform phototherapy and electrotherapy according to the microprocessor's physiotherapy strategy.
2. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 1, characterized in that, The user model matching process includes: Several preset models are established based on big data. Each preset model corresponds to a range of physiotherapy needs information and user basic information. The user basic information includes user age, user gender and user BMI. Based on the physiotherapy needs information, the preset models are initially screened to obtain the preset models to be selected; The user's basic information is compared with the range of user basic information corresponding to the preset models to be selected, and the preset model with the highest degree of overlap is selected as the user model.
3. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 1, characterized in that, The user status information includes the temperature of the user's physiotherapy area, blood oxygen saturation, muscle fatigue coefficient, and the user's basic physiological parameters. The process of making real-time adjustments to physical therapy control strategies includes: Determine whether to stop physical therapy based on the range and trend of the user's basic physiological parameters; The control parameters of the phototherapy process are adjusted according to the temperature and blood oxygen saturation of the user's treatment area; the control parameters of the phototherapy process include light wavelength, power density and irradiation time; The control parameters of the electrotherapy process are adjusted according to the muscle fatigue coefficient; the control parameters of the electrotherapy process include pulse intensity, pulse frequency, pulse width and pulse waveform type.
4. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 3, characterized in that, The process of determining whether to stop physical therapy includes: Determine if the user's basic physiological parameters exceed the corresponding numerical range: If a user's baseline physiological parameters exceed the corresponding numerical range, the physiotherapy process will be stopped. If there are no user basic physiological parameters exceeding the corresponding numerical range, first obtain the absolute value of the difference between each user basic physiological parameter at the current time point and the median value of the corresponding numerical range, △p1. Then obtain the maximum change △p2 of each user basic physiological parameter during the △t period before the current time point. After normalizing △p1 and △p2, perform a weighted sum to obtain the state value of the user basic physiological parameter. Perform a weighted sum of the state values of all user basic physiological parameters and the corresponding influence coefficients to obtain the user state value. Compare the user state value with the preset value. When the user state value exceeds the preset value, stop the physiotherapy process.
5. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 3, characterized in that, The process of adjusting the control parameters of phototherapy based on the temperature and blood oxygen saturation of the user's treatment area includes: The temperature difference ΔT and blood oxygen saturation difference ΔBo of the user's treatment area and the corresponding reference values are obtained respectively. The adjustment strategy of the phototherapy process control parameters is obtained according to the range of the temperature difference ΔT and the range of the blood oxygen difference ΔBo.
6. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 3, characterized in that, The process of obtaining the muscle fatigue coefficient includes: The total action potentials generated by muscle fibers under nerve electrical signal stimulation are detected in real time by a surface electromyography signal sensor. The first muscle fatigue coefficient is obtained according to the value of the total action potentials. The value range of the first muscle fatigue coefficient is 0~1. A constant pressure is applied to the muscle using a tension sensor, and its deformation resistance is measured. The second muscle fatigue coefficient is obtained based on the range of the static muscle tension value, which is 0 to 1. The reliability ratio of the first muscle fatigue coefficient to the second muscle fatigue coefficient is obtained based on the user model. : , Through computational model: Muscle fatigue coefficient = First muscle fatigue coefficient Second muscle fatigue coefficient The muscle fatigue coefficient is calculated.
7. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 6, characterized in that, The process of adjusting the control parameters of the electrotherapy procedure based on the muscle fatigue coefficient includes: Compare the muscle fatigue coefficient with the preset fatigue coefficient benchmark value: If the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value is less than the allowable error value, then the existing control parameters of the electrotherapy process are maintained. Otherwise, an adjustment strategy for the electrotherapy process control parameters is obtained based on the range of the difference between the muscle fatigue coefficient and the preset fatigue coefficient benchmark value.
8. The intelligent control system for a phototherapy device based on multimodal feedback according to claim 3, characterized in that, The user's basic physiological parameters include blood pressure, heart rate, and blood oxygen saturation.