Music bioelectric physiotherapy instrument based on nursing data

Abstract

The application discloses a music bioelectric physiotherapy instrument based on nursing data and belongs to the field of biomedical technology. The music bioelectric physiotherapy instrument based on nursing data comprises a data acquisition module, a data processing module and a music physiotherapy module. The application solves the problem that the existing music bioelectric physiotherapy instrument usually adopts unified physiotherapy parameters and does not fully consider individual differences of patients. The application can accurately determine nursing requirements and physiotherapy targets of patients by collecting and analyzing detailed physiological and nursing data of the patients, realizes individualized physiotherapy scheme formulation, generates corresponding music signals and bioelectric stimulation signals, combines the music signals with the bioelectric stimulation signals, realizes coupling of the music signals and the bioelectric stimulation signals in rhythm and phase, can enhance physiotherapy effect, and dynamically adjusts music and bioelectric stimulation parameters according to real-time physiological states of the patients, so that the accuracy and adaptability of a physiotherapy process can be ensured.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a music bioelectric therapy device based on nursing data. Background Technology

[0002] During music-based bioelectric therapy, patients listen to music selected and tuned by specialized equipment while corresponding parts of their bodies come into contact with electrodes that transmit bioelectric stimulation. This combined effect influences physiological functions and psychological states, achieving therapeutic results. However, existing music-based bioelectric therapy devices typically use standardized parameters, failing to adequately consider individual patient differences, thus hindering the provision of targeted treatment. Therefore, they do not meet current needs. To address this, we propose a music-based bioelectric therapy device based on nursing data. Summary of the Invention

[0003] The purpose of this invention is to provide a music-based bioelectric physiotherapy device based on nursing data. By collecting and analyzing detailed physiological and nursing data of patients, it can accurately determine the patients' nursing needs and physiotherapy goals, realize the formulation of personalized physiotherapy plans, generate corresponding music signals and bioelectric stimulation signals, and combine the music signals and bioelectric stimulation signals, and couple the two in terms of rhythm and phase, which can enhance the physiotherapy effect and solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a music bioelectric therapy device based on nursing data, comprising: The data acquisition module is configured to collect patients' physiological data in real time, perform correlation data collection and analysis, and feed the analysis results back to the data processing module. At the same time, it collects and stores users' nursing data. Physiological data includes, but is not limited to, heart rate, blood pressure, electromyography signals, heart rate variability, skin conductance, and electroencephalogram (EEG) signals. Nursing data includes, but is not limited to, the patient's daily routine, diet, history of physical therapy, user health records, and personalized treatment plans. The data processing module is configured to receive physiological and nursing data from the data acquisition module, process and analyze them, and determine the patient's nursing needs and physical therapy goals. The music therapy module is configured to generate corresponding music signals and bioelectric stimulation signals based on the physiotherapy goals determined by the data processing module. The frequency, rhythm, and volume of the music signals are dynamically adjusted according to the patient's nursing needs. The bioelectric stimulation signals are applied to the patient's body parts through electrode pads, and the rhythm and phase of the bioelectric stimulation signals and music signals are coupled. Among them, the music signal has specific music feature parameters, which are dynamically generated based on physiological and nursing data to match the patient's current physiological state and nursing needs.

[0005] Furthermore, the data acquisition module includes: The data acquisition module is configured to collect patients' physiological data in real time through wearable patches and build a nursing database for storing the collected user nursing data. In the nursing database, data mining algorithms and knowledge graph technology are used to deeply integrate nursing data, including linking patients’ daily routines with their diet to analyze their eating patterns, and combining historical physiotherapy records and health records to uncover potential nursing needs. The quality assessment module is configured to perform quality assessment and correction on the collected physiological and nursing data, including data integrity detection, data accuracy verification, and intelligent correction and compensation. The associated data acquisition module is configured to install temperature and humidity sensors, light sensors, and noise sensors in the physiotherapy room to collect environmental temperature and humidity, light intensity, and noise decibel data in real time. Then, data fusion technology is used to integrate the environmental data with the aforementioned physiological and nursing data to explore the potential correlation between environmental factors and patient care and physiotherapy effects.

[0006] Furthermore, the quality assessment module specifically includes: Data integrity detection: Real-time monitoring of the integrity of various physiological and nursing data collection. By setting reasonable data continuity thresholds and missing value judgment rules, once data interruption or missing data is detected, an alarm will be issued immediately and the time period and type of missing data will be automatically recorded. Data accuracy verification: A data accuracy assessment model is established using machine learning algorithms to verify the collected current patient data by comparing it with the normal physiological data range of similar patients. Intelligent correction and compensation: When data integrity or accuracy is compromised, a Kalman filter signal processing algorithm is used to intelligently compensate for and correct missing or erroneous data by combining normal data from previous and subsequent time periods. At the same time, errors in nursing data are corrected by logical reasoning through correlation with work and rest schedules and other data to ensure the overall data quality is reliable.

[0007] Furthermore, the calculation formula for the data integrity check is as follows: In the formula, the data continuity threshold T c A measure of time interval used to determine whether physiological or nursing data are continuous within a reasonable timeframe; T mThe baseline time interval is set based on the frequency of normal physiological data collection or nursing record keeping; ΔT is the allowable time fluctuation range, usually taken as 0.1 to 0.3.

[0008] Furthermore, the associated acquisition module specifically comprises: Statistical analysis methods were used to analyze the correlation between environmental factors and patients' physiological and nursing data to identify potential relationships. By conducting trend analysis on the fused data, we can observe the changing trends of environmental factors, physiological data, and nursing data over time, determine whether there are regular changes or abnormal fluctuations, and provide a basis for adjusting the physiotherapy plan. The above analysis results are fed back to the data processing module to adjust the physiotherapy goals and generate corresponding music signals and bioelectric stimulation signals.

[0009] Furthermore, the data processing module includes: The data cleaning module is configured to clean the collected physiological and nursing data, removing obvious noise and outliers. The data synchronization module is configured to synchronize physiological and nursing data at different timestamps to ensure that subsequent analysis is conducted within a unified time frame. The feature extraction module is configured to extract key features from physiological data, including the mean and coefficient of variation of heart rate, the fluctuation range of systolic and diastolic blood pressure, and the amplitude and frequency characteristics of electromyographic signals. Extract features related to patients' health status and nursing needs from nursing data, including the regularity of their daily routine, the balance of their diet, and the effective treatment duration and frequency in their historical physiotherapy records. The status assessment module is configured to assess the patient's current physiological status based on extracted physiological characteristics, combined with medical reference standards and thresholds. Based on nursing characteristics and established nursing quality standards, assess the effectiveness and appropriateness of the patient's current nursing interventions; The data analysis module is configured to comprehensively analyze the patient's specific nursing needs based on the patient's status assessment results, nursing standards, and individualized needs. It also formulates individualized physiotherapy goals based on the patient's physiological state and nursing needs, combined with physiotherapy principles and previous treatment cases.

[0010] Furthermore, the data analysis module includes: Based on the patient status assessment results, risk scores are calculated for patients, and indicators with high risk scores are extracted as the patient's primary physiological needs. Determine the trend of patient care optimization based on the results of nursing effectiveness assessment; Nursing indicators that are above the average optimization trend in the nursing optimization trend are taken as the patient's nursing needs; Based on the patient's personalized needs for physiotherapy, the patient's secondary physiological needs are obtained through needs processing. Compare the first physiological need and the second physiological need, and determine whether there is a conflict between the first physiological need and the second physiological need; When there is no conflict of needs, the physiological priority of the second physiological need is determined based on the analytic hierarchy process. Based on the priority of physiological needs corresponding to the second physiological need, the first physiological need is optimized to obtain the optimized physiological needs; When there is a conflict of needs, the sub-needs corresponding to the conflict of needs in the second physiological needs are removed, and the remaining physiological needs are taken as the third physiological needs. Based on the priority of the third physiological need, the first physiological need is optimized to obtain the optimized physiological needs; Patients with the highest similarity to the patient's physiological data were selected from the historical physiotherapy database as baseline patients; The baseline physiological and nursing needs of the baseline patient are prioritized as the initial priority of the patient's optimized physiological and nursing needs. The patient's initial physiotherapy plan is determined based on the initial demand priority and the corresponding optimized physiological and nursing needs. Acquire patient's physiotherapy data and plans throughout the entire physiotherapy cycle, and optimize the initial physiotherapy plan based on the physiotherapy data and plans; The patient's individualized physical therapy goals are determined based on the results of the optimized treatment plan.

[0011] Furthermore, the music therapy module includes: The music generation module is configured to set the basic characteristics of music based on the physiotherapy goals and patient care needs determined by the data processing module, including the frequency range, rhythm pattern and basic volume of the music. Based on the patient's preferences and treatment needs, it selects the corresponding music type from the music library and generates a specific music signal using music synthesis technology based on the set music characteristics and the selected music type. The biophysical therapy module is configured to generate corresponding bioelectric stimulation signals based on the physiotherapy goals and the rhythmic phase coupling requirements with the music signal. The frequency, amplitude, and waveform of the bioelectric signals are matched with the key features of the music signals.

[0012] Furthermore, the music therapy module also includes: The dynamic adjustment module is configured to monitor new physiological data of patients in real time, assess and provide feedback on patients' physiological responses to current music signals and bioelectric stimulation signals, and dynamically adjust the frequency, rhythm and volume parameters of music signals and the intensity of bioelectric stimulation signals based on feedback information and nursing needs updated by the data processing module.

[0013] Furthermore, the dynamic adjustment module includes: Real-time monitoring of new physiological data of patients, and determination of signal evaluation index based on patients' physiological responses to current music signals and bioelectric stimulation signals; Feedback on the patient's physiological response is based on the signal assessment index.

[0014] Furthermore, the biophysical therapy module includes: Rhythm analysis was performed on music signals and bioelectric stimulation signals respectively to extract key beat points and phase information; Based on the rhythm analysis results, the triggering timing of the bioelectric stimulation signal is adjusted to achieve synchronous coupling between the rhythm phase of the bioelectric stimulation signal and the music signal.

[0015] Furthermore, the music bioelectric therapy device also includes: The safety monitoring module is configured to continuously monitor the bioelectric stimulation parameters to ensure that the bioelectric stimulation parameters are within a safe range. The early warning and intervention module is configured to immediately issue an alarm to remind medical staff once the bioelectric stimulation parameters are found to be outside the safe range, and automatically trigger safety intervention measures, including but not limited to suspending bioelectric stimulation and reducing the stimulation intensity.

[0016] Compared with the prior art, the beneficial effects of the present invention are: This invention, through the collection and analysis of detailed physiological and nursing data of patients, can accurately determine patients' nursing needs and physiotherapy goals, thereby enabling the formulation of personalized physiotherapy plans and improving physiotherapy effects. Based on the physiotherapy goals, corresponding music signals and bioelectric stimulation signals are generated and combined, with the two coupled in rhythm and phase, which can enhance the physiotherapy effect and improve the patient's psychological and physiological state. The music and bioelectric stimulation parameters are dynamically adjusted according to the patient's real-time physiological state to ensure the accuracy and adaptability of the physiotherapy process. Furthermore, by monitoring the bioelectric stimulation parameters in real time, early warnings can be issued and intervention measures can be taken in a timely manner once abnormalities are detected, ensuring patient safety. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the modules of the music bioelectric therapy device based on nursing data of the present invention; Figure 2This is a schematic diagram of the appearance of the music bioelectric therapy device based on nursing data according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] To address the technical issue that existing music bioelectric therapy devices typically use standardized treatment parameters, failing to adequately consider individual patient differences and thus unable to effectively differentiate and provide targeted treatment, please refer to [link to relevant documentation]. Figures 1-2 This embodiment provides the following technical solution: Music-based bioelectric therapy devices based on nursing data include: The data acquisition module is configured to collect patients' physiological data in real time, perform correlation data collection and analysis, and feed the analysis results back to the data processing module. At the same time, it collects and stores users' nursing data. Physiological data includes, but is not limited to, heart rate, blood pressure, electromyography signals, heart rate variability, skin conductance and electroencephalogram (EEG) signals; nursing data includes, but is not limited to, the patient’s daily routine, diet, history of physical therapy, user health records and personalized treatment plans. The data processing module is configured to receive physiological and nursing data from the data acquisition module, process and analyze them, and determine the patient's nursing needs and physical therapy goals. The music therapy module is configured to generate corresponding music signals and bioelectric stimulation signals based on the physiotherapy goals determined by the data processing module. The frequency, rhythm, and volume of the music signals are dynamically adjusted according to the patient's nursing needs. The bioelectric stimulation signals are applied to the patient's body parts through electrode pads, and the rhythm and phase of the bioelectric stimulation signals and music signals are coupled. Among them, the music signal has specific music feature parameters, which are dynamically generated based on physiological data and nursing data to match the patient's current physiological state and nursing needs; The safety monitoring module is configured to continuously monitor bioelectric stimulation parameters to ensure that the bioelectric stimulation parameters are within a safe range, such as whether the bioelectric stimulation intensity exceeds the patient's tolerance threshold. The early warning and intervention module is configured to immediately issue an alarm to remind medical staff once the bioelectric stimulation parameters are found to be outside the safe range, and automatically trigger safety intervention measures, including but not limited to suspending bioelectric stimulation and reducing the stimulation intensity.

[0020] The technical effects of the above-mentioned solution are as follows: By integrating physiological and nursing data, the patient's nursing needs and physiotherapy goals can be accurately determined, thereby realizing personalized music and bioelectric stimulation physiotherapy plans to improve the physiotherapy effect. Based on the physiotherapy goals determined by the data, corresponding music signals and bioelectric stimulation signals can be generated. Moreover, the music signals and bioelectric stimulation signals can be dynamically adjusted according to the patient's real-time physiological state, ensuring the accuracy and adaptability of the physiotherapy process and improving the effectiveness of the physiotherapy. Through the rhythmic phase coupling of music signals and bioelectric stimulation signals, a more comprehensive stimulation effect can be achieved, thereby enhancing the physiotherapy effect while improving the patient's psychological and physiological state. Furthermore, real-time monitoring of bioelectric stimulation parameters can detect abnormalities and provide timely warnings and interventions, ensuring the patient's safety during the physiotherapy process.

[0021] The data acquisition module includes: The data acquisition module is configured to collect patients' physiological data in real time through wearable patches and build a nursing database for storing the collected user nursing data. In the nursing database, data mining algorithms and knowledge graph technology are used to deeply integrate nursing data, including linking patients’ daily routines with their diet to analyze their eating patterns, and combining historical physiotherapy records and health records to uncover potential nursing needs. The quality assessment module is configured to perform quality assessment and correction on the collected physiological and nursing data, including data integrity detection, data accuracy verification, and intelligent correction and compensation. The associated data acquisition module is configured to install temperature and humidity sensors, light sensors, and noise sensors in the physiotherapy room to collect real-time data on ambient temperature and humidity, light intensity, and noise levels. Then, data fusion technology is used to integrate the environmental data with the aforementioned physiological and nursing data to uncover potential correlations between environmental factors and patient care and physiotherapy outcomes. Specifically: Using statistical analysis methods (such as Pearson correlation coefficient, Spearman rank correlation coefficient, etc.), we analyzed the correlation between environmental factors (temperature, humidity, light, noise) and patients' physiological data (heart rate, blood pressure, etc.) and nursing data (rest time, diet, etc.) to identify potential associations. By conducting trend analysis on the fused data, we can observe the changing trends of environmental factors, physiological data, and nursing data over time, determine whether there are regular changes or abnormal fluctuations, and provide a basis for adjusting the physiotherapy plan. The above analysis results are fed back to the data processing module to adjust the physiotherapy goals and generate corresponding music signals and bioelectric stimulation signals.

[0022] The technical effects of the above solution are as follows: the data acquisition module collects physiological data in real time through wearable patches and builds a nursing database to store nursing data, which can provide comprehensive and accurate data support for the formulation of physiotherapy plans. The quality assessment module performs integrity detection, accuracy verification, and intelligent correction and compensation on the collected physiological and nursing data to ensure the reliability of the data, thereby improving the accuracy of subsequent data analysis and physiotherapy plan formulation. The correlation acquisition module provides a basis for optimizing the physiotherapy environment and physiotherapy plan by exploring the potential correlation between environmental factors and patient care and physiotherapy effects.

[0023] The quality assessment module, specifically: Data integrity detection: Real-time monitoring of the integrity of various physiological and nursing data collection. By setting reasonable data continuity thresholds and missing value judgment rules, once data interruption or missing data is detected (such as loss of heart rate signal, missing diet record, etc.), an alarm will be issued immediately and the time period and type of missing data will be automatically recorded. The formula for calculating data integrity verification is as follows: In the formula, the data continuity threshold T c A measure of time interval used to determine whether physiological or nursing data are continuous within a reasonable timeframe; T m The baseline time interval is set based on the frequency of normal physiological data collection or nursing record-keeping. For example, if the heart rate data collection frequency is once per second, then Tm is 1 second; ΔT is the allowable time fluctuation range, usually taken as 0.1 to 0.3, for example, 0.2, which means that ±20% time fluctuation is allowed. In this embodiment, it is assumed that for heart rate data acquisition, T is set. m =1 second, ΔT=0.2, then T c =1×(1±0.2)=0.8-1.2 seconds. If the time interval between two consecutive heart rate data acquisitions exceeds 1.2 seconds or is less than 0.8 seconds, it is determined that the data may be discontinuous, triggering the data integrity detection mechanism. Data accuracy verification: A data accuracy assessment model is established using machine learning algorithms. The data is compared with the normal physiological data range of similar patients (normal reference values ​​derived from big data analysis) to verify the current patient data. For example, if the blood pressure value exceeds the normal fluctuation range and differs too much from the patient's previous blood pressure records, it is determined that there may be sensor error or a sudden patient condition, triggering the data correction process. Intelligent correction and compensation: When data integrity or accuracy is compromised, a Kalman filter signal processing algorithm is used to intelligently compensate for and correct missing or erroneous data by combining normal data from previous and subsequent time periods. At the same time, errors in nursing data are corrected by logical reasoning through correlation with work and rest schedules and other data to ensure the overall data quality is reliable.

[0024] The technical effects of the above-mentioned solution are as follows: By monitoring the completeness of physiological and nursing data collection in real time, it helps to ensure data continuity, avoid analytical errors caused by missing data, and provide a complete data foundation for subsequent data analysis and physiotherapy plan development. Through data accuracy verification, abnormal data is effectively identified, thereby improving data accuracy and enabling subsequent analysis to be based on reliable data, thus enhancing the scientificity and effectiveness of physiotherapy plan development. Furthermore, by using the Kalman filter algorithm in conjunction with normal data to intelligently compensate and correct missing or erroneous data, and by correcting errors in nursing data through logical reasoning, it not only reduces the impact of missing and erroneous data on analysis, but also ensures the reliability of overall data quality, providing strong support for accurate nursing needs assessment and physiotherapy goal determination.

[0025] The data processing module includes: The data cleaning module is configured to clean the collected physiological and nursing data, removing obvious noise and outliers, such as removing data points that exceed the normal range of human physiological indicators (e.g., blood pressure values ​​that are lower or higher than the physiologically possible value). The data synchronization module is configured to synchronize physiological and nursing data at different timestamps to ensure that subsequent analysis is conducted within a unified time frame. For example, heart rate data can be synchronized with the daily routine in nursing records to analyze changes in the patient's heart rate under different activity states. The feature extraction module is configured to extract key features from physiological data, including the mean and coefficient of variation of heart rate, the fluctuation range of systolic and diastolic blood pressure, and the amplitude and frequency characteristics of electromyographic signals. Extract features related to patients' health status and nursing needs from nursing data, including the regularity of their daily routine, the balance of their diet, and the effective treatment duration and frequency in their historical physiotherapy records. The status assessment module is configured to assess the patient's current physiological status based on extracted physiological characteristics, combined with medical reference standards and thresholds. For example, it can assess the patient's autonomic nervous system function status through heart rate variability analysis to determine whether there is excessive sympathetic nerve excitation or weakened parasympathetic nerve function. Based on nursing characteristics and established nursing quality standards, assess the effectiveness and appropriateness of the patient's current nursing measures. For example, analyze whether the patient's diet meets their nutritional needs and whether their rest schedule is conducive to physical recovery. The data analysis module is configured to comprehensively analyze the patient's specific nursing needs based on the patient's status assessment results, nursing standards, and individualized needs. It also formulates individualized physiotherapy goals based on the patient's physiological state and nursing needs, combined with physiotherapy principles and previous treatment cases.

[0026] The technical effects of the above solution are as follows: the data cleaning module effectively removes noisy data and outliers, ensuring that subsequent analysis is based on high-quality data; the data synchronization module synchronizes data with different timestamps, ensuring that the analysis is conducted within a unified time frame and avoiding analytical errors caused by time differences; the feature extraction module extracts key features from physiological and nursing data, providing a strong basis for accurately assessing patient status and nursing needs; the status assessment module combines medical standards to assess the patient's physiological status and nursing measures, accurately determining the patient's current condition and providing scientific support for the development of physiotherapy plans; and the data analysis module integrates multiple factors to formulate physiotherapy goals that meet the individual needs of patients, thereby improving the physiotherapy effect and the quality of patient recovery. Through the cooperation of various modules, the data analysis module can formulate physiotherapy goals that meet the individual needs of patients, thereby improving the physiotherapy effect.

[0027] The data analysis module includes: Based on the patient status assessment results, risk scores are calculated for patients, and indicators with high risk scores are extracted as the patient's primary physiological needs. Determine the trend of patient care optimization based on the results of nursing effectiveness assessment; Nursing indicators that are above the average optimization trend in the nursing optimization trend are taken as the patient's nursing needs; Based on the patient's personalized needs for physiotherapy, the patient's secondary physiological needs are obtained through needs processing. Compare the first physiological need and the second physiological need, and determine whether there is a conflict between the first physiological need and the second physiological need; When there is no conflict of needs, the physiological priority of the second physiological need is determined based on the analytic hierarchy process. Based on the priority of physiological needs corresponding to the second physiological need, the first physiological need is optimized to obtain the optimized physiological needs; When there is a conflict of needs, the sub-needs corresponding to the conflict of needs in the second physiological needs are removed, and the remaining physiological needs are taken as the third physiological needs. Based on the priority of the third physiological need, the first physiological need is optimized to obtain the optimized physiological needs; Patients with the highest similarity to the patient's physiological data were selected from the historical physiotherapy database as baseline patients; The baseline physiological and nursing needs of the baseline patient are prioritized as the initial priority of the patient's optimized physiological and nursing needs. The patient's initial physiotherapy plan is determined based on the initial demand priority and the corresponding optimized physiological and nursing needs. Acquire patient's physiotherapy data and plans throughout the entire physiotherapy cycle, and optimize the initial physiotherapy plan based on the physiotherapy data and plans; The patient's individualized physical therapy goals are determined based on the results of the optimized treatment plan.

[0028] In this embodiment, the patient status assessment result is a set of indicators used to assess the patient's current health status by analyzing the patient's physiological data.

[0029] In this embodiment, the risk score is based on a preset medical standard or machine learning model, which converts the patient's status assessment results into a quantitative risk value. For example, the risk score can range from 0 to 100.

[0030] In this embodiment, a high-risk indicator refers to a physiological parameter whose risk score exceeds a preset threshold. For example, when the risk score ranges from (0 to 100), the preset threshold is generally around 80 points. For example, a high-risk indicator could be blood pressure consistently >160 / 100 mmHg or electromyography showing excessive muscle fatigue (i.e., amplitude fluctuation >30%).

[0031] In this embodiment, the nursing effectiveness assessment result is determined by comparing nursing standards with the patient's actual nursing data to determine the degree of improvement of the current physical therapy measures on the patient's condition.

[0032] In this embodiment, the nursing optimization trend refers to the changing trend of the patient's nursing behavior before and at the current moment. For example, "the frequency of bioelectric stimulation is increased from 50Hz to 60Hz" and "the daily physiotherapy duration is extended from 20 minutes to 30 minutes" are both nursing optimization trends.

[0033] In this embodiment, nursing needs are nursing indicators extracted from the assessment results that are higher than the average improvement rate; that is, nursing indicators that need to be strengthened are taken as nursing needs.

[0034] In this embodiment, personalized needs are generated by combining the patient's subjective feedback with objective physiological data to create an executable second physiological need. For example, the patient's subjective feedback includes preferences for music types and tolerance to stimulation intensity, while the patient's objective physiological data includes the heart rate response curve to music. For example, "the rhythm of the physiotherapy music needs to be controlled at 60-80 BPM to maintain HRV>50ms" and "the intensity of bioelectric stimulation needs to be ≤15mA to avoid muscle spasms".

[0035] In this embodiment, demand conflict refers to the situation where the first physiological need and the second physiological need conflict in terms of intervention methods, and the demand conflict is resolved by prioritizing or eliminating needs.

[0036] In this embodiment, the third physiological need refers to the process of eliminating conflicting sub-needs in the second physiological need when there is a conflict between the first and second physiological needs. The remaining sub-needs in the second physiological need are then considered as the third physiological need. Finally, the first and third physiological needs are weighted according to the analytic hierarchy process (AHP) and the needs are processed accordingly.

[0037] In this embodiment, when there is no conflict of demand, the hierarchical analysis method is used to assign weights to the first physiological demand and the second physiological demand for demand processing.

[0038] In this embodiment, the Analytic Hierarchy Process (AHP) is a multi-criteria decision-making method that generates the optimal solution by constructing a hierarchical model to quantify the priority of different needs.

[0039] In this embodiment, optimizing physiological needs involves determining the final set of physiological needs by combining the priority weights of the first, second (or third) physiological needs. When there is no conflict in needs, the first and second physiological needs are determined by combining their respective priority weights. When a conflict exists, the first and third physiological needs are determined by combining their respective priority weights. For example, if the first physiological need is "to lower blood pressure to 130 / 85 mmHg" and the third physiological need is "stimulation intensity ≤ 15mA, daily therapy ≤ 30 minutes," then the optimized physiological need is "to lower blood pressure to 130 / 85 mmHg within 6 weeks through low-frequency stimulation of 15mA for 30 minutes / session, with a patient comfort score ≥ 8 points." In this embodiment, the baseline patient is a patient who is matched from a historical database with cases whose physiological data are similar to those of the current patient.

[0040] In this embodiment, the initial physiotherapy plan is determined by prioritizing the baseline physiological and nursing needs of the baseline patient as the patient's priority for physiological and nursing needs. The initial physiotherapy plan includes bioelectric parameters, music intervention parameters, execution cycle, monitoring indicators, etc.

[0041] In this embodiment, the optimization of the treatment plan is achieved by dynamically adjusting the parameters of the initial treatment plan using full-cycle physiotherapy data and the patient's real-time physiological data.

[0042] In this embodiment, the personalized physiotherapy goal is the physiotherapy objective determined based on the optimized physiotherapy plan after optimizing the initial physiotherapy plan.

[0043] The working principle of the above technical solution is as follows: First, based on the patient's health risk, high-risk indicators are identified as the primary physiological needs. At the same time, the effectiveness of nursing care for the patient is analyzed, and optimization trends are extracted as nursing needs. Simultaneously, the patient's personalized feedback on music type and stimulation intensity is combined to generate secondary physiological needs. The analytic hierarchy process (AHP) is used to resolve the conflict of physiological needs and form optimized physiological needs. Next, patients with similar cases are matched from the historical database, and the patient's physiotherapy plan is transferred to determine the initial physiotherapy plan. Subsequently, throughout the patient's entire physiotherapy cycle, the patient's physiological data and feedback are collected in real time, and bioelectrical parameters and music intervention strategies are dynamically adjusted to ultimately determine personalized goals.

[0044] The technical effects of the above-mentioned technical solution are as follows: by integrating the patient's physiological data and nursing data, the high-risk health indicators of the patient can be accurately identified, and combined with the patient's personalized needs, the physiotherapy needs can be comprehensively determined, thereby improving the pertinence of physiotherapy. At the same time, the analytic hierarchy process and historical data can be combined to optimize the priority of the patient's physiotherapy needs, improve the scientificity and effectiveness of the physiotherapy plan, improve the patient's comfort, and meet the patient's personalized physiotherapy goals to the greatest extent.

[0045] The music therapy module includes: The music generation module is configured to set the basic characteristics of music based on the physiotherapy goals and patient care needs determined by the data processing module, including the frequency range of the music (related to heart rate and brain wave frequency), rhythm pattern (matching breathing rhythm and heart rate variability), and basic volume. According to the patient's preferences and treatment needs, it selects the corresponding music type from the music library, and generates a specific music signal based on the set music characteristics and the selected music type using music synthesis technology (such as MIDI synthesis, digital audio sampling, etc.). The biophysical therapy module is configured to generate corresponding bioelectric stimulation signals based on the physiotherapy goals and the rhythmic phase coupling requirements with the music signal. The frequency, amplitude, and waveform (such as sine wave, square wave, etc.) of the bioelectric signal are matched with the key features of the music signal. For example, the frequency of the bioelectric stimulation signal can be synchronized with the low-frequency components of the music, the rising and falling edges of the waveform are consistent with the key beats of the music rhythm, and the amplitude can be adjusted according to the patient's tolerance and treatment needs. The music therapy module also includes: The dynamic adjustment module is configured to monitor new physiological data of patients in real time, assess and provide feedback on patients' physiological responses to current music signals and bioelectric stimulation signals, and dynamically adjust the frequency, rhythm, and volume parameters of the music signal, as well as the intensity of the bioelectric stimulation signal, based on the feedback information and the nursing needs updated by the data processing module. For example, if the patient's heart rate is detected to be too fast, the music rhythm and frequency can be appropriately reduced, low-frequency components can be increased to promote relaxation, and the intensity of the bioelectric stimulation signal can be reduced; if the patient's blood pressure is low and they need to improve their vitality, the music rhythm and volume can be appropriately increased, high-frequency components can be increased to stimulate sympathetic nerve excitability, and the intensity of the bioelectric stimulation signal can be appropriately increased.

[0046] The technical effects of the above solution are as follows: music signals are generated according to the patient's nursing needs and physiotherapy goals to meet the treatment needs of different patients and improve the treatment effect. At the same time, bioelectric stimulation signals coupled with music signals are generated to enhance the physiotherapy effect, thereby improving the patient's physiological and psychological regulation. The patient's physiological response is monitored in real time to assess the patient's physiological response to the current physiotherapy signal, so as to dynamically adjust the parameters of music and bioelectric stimulation signals, ensure the accuracy and adaptability of physiotherapy, and realize an intelligent and optimized physiotherapy process.

[0047] The dynamic adjustment module includes: Real-time monitoring of new physiological data of patients, and determination of signal evaluation index based on patients' physiological responses to current music signals and bioelectric stimulation signals; Feedback on the patient's physiological response is based on the signal assessment index.

[0048] In this embodiment, the music signal evaluation index is M; Where M is the music signal evaluation index. This refers to the difference in the patient's heart rate at the current treatment time compared to before treatment. The difference between the maximum heart rate and the maximum heart rate. This represents the root mean square of the adjacent RR intervals within the current treatment cycle for the patient. This represents the maximum root mean square change between adjacent RR intervals. The root mean square of the patient's electromyography (EMG) signals during the current treatment cycle. This represents the maximum root-mean-square variation of the electromyographic signal. This represents the difference in skin electrical activity between the patient at the current treatment time and before treatment. This represents the maximum change in skin electrical activity. Weighting the impact of heart rate changes. Weighting for the impact of heart rate variability. The weights are affected by changes in electromyographic signals. The weights for the influence of skin conductance response are defined as follows: the weights for the influence of heart rate changes, heart rate variability, electromyographic signal changes, and skin conductance response are summed to 1.

[0049] In this embodiment, the maximum heart rate difference, the maximum root mean square change, the maximum root mean square change of the electromyographic signal, and the maximum activity change of the skin electrical activity all refer to the maximum change within the standard range.

[0050] In this embodiment, heart rate variability is determined based on the root mean square difference of adjacent RR intervals, where adjacent RR intervals refer to the time interval between two adjacent R waves on an electrocardiogram.

[0051] In this embodiment, the root mean square (RMS) value of the electromyography (EMG) signal refers to the change in the RMS value of the EMG signal of the target muscle (such as the trapezius muscle), which is used to reflect the degree of muscle tension.

[0052] In this embodiment, the weights of heart rate change, heart rate variability, electromyography signal change, and skin conductance response are dynamically adjusted according to the patient's clinical physiotherapy goals.

[0053] The technical effects of the above-mentioned technical solution are as follows: by monitoring the patient's physiological data in real time, the signal evaluation index of the patient's response to music signals and bioelectric stimulation signals can be determined based on the monitoring results, thereby providing feedback on physiological responses. This makes the feedback results more accurate and allows for timely and accurate adjustment of music and bioelectric stimulation signal parameters, ensuring the precision and adaptability of physiotherapy.

[0054] The biophysical therapy module includes: Rhythm analysis was performed on music signals and bioelectric stimulation signals respectively to extract key beat points and phase information; Based on the rhythm analysis results, the triggering timing of the bioelectric stimulation signal is adjusted to achieve synchronous coupling between the rhythm phase of the bioelectric stimulation signal and the music signal.

[0055] The technical effects of the above solution are as follows: by extracting key information through rhythm analysis, the rhythmic phase of the bioelectric stimulation signal and the music signal is precisely synchronized and coupled, thereby enhancing the physiotherapy effect. The synchronized bioelectric stimulation signal can change with the rhythm of the music signal, better regulating the patient's physiological state, thereby realizing an intelligent and optimized physiotherapy process and improving the accuracy and effectiveness of the treatment.

[0056] Working principle: By integrating physiological and nursing data, the system can accurately determine the patient's nursing needs and physiotherapy goals, thereby enabling the development of personalized physiotherapy plans. Based on the physiotherapy goals determined by the data, corresponding music signals and bioelectric stimulation signals can be generated. These signals can be dynamically adjusted according to the patient's real-time physiological state, ensuring the accuracy and adaptability of the physiotherapy process. Through the rhythmic phase coupling of music signals and bioelectric stimulation signals, a more comprehensive stimulation effect can be achieved, thereby enhancing the physiotherapy effect. Real-time monitoring of bioelectric stimulation parameters allows for timely warnings and interventions for abnormalities, ensuring the safety of the patient during the physiotherapy process.

[0057] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0058] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention.

Claims

1. A music bioelectric therapy device based on nursing data, characterized in that, include: The data acquisition module is configured to collect patients' physiological data in real time, perform correlation data collection and analysis, and feed the analysis results back to the data processing module. At the same time, it collects and stores users' nursing data. Physiological data includes, but is not limited to, heart rate, blood pressure, electromyography signals, heart rate variability, skin conductance, and electroencephalogram (EEG) signals. Nursing data includes, but is not limited to, the patient's daily routine, diet, history of physical therapy, user health records, and personalized treatment plans. The data processing module is configured to receive physiological and nursing data from the data acquisition module, process and analyze them, and determine the patient's nursing needs and physical therapy goals. The music therapy module is configured to generate corresponding music signals and bioelectric stimulation signals based on the physiotherapy goals determined by the data processing module. The frequency, rhythm, and volume of the music signals are dynamically adjusted according to the patient's nursing needs. The bioelectric stimulation signals are applied to the patient's body parts through electrode pads, and the rhythm and phase of the bioelectric stimulation signals and music signals are coupled. Among them, the music signal has specific music feature parameters, which are dynamically generated based on physiological data and nursing data to match the patient's current physiological state and nursing needs; The music therapy module includes a dynamic adjustment module, configured to monitor new physiological data of the patient in real time, assess the patient's physiological response to the current music signal and bioelectric stimulation signal and provide feedback, and dynamically adjust the frequency, rhythm and volume parameters of the music signal and the intensity of the bioelectric stimulation signal according to the feedback information and the nursing needs updated by the data processing module. The dynamic adjustment module includes real-time monitoring of the patient's new physiological data, determining a signal evaluation index based on the patient's physiological response to the current music signal and bioelectric stimulation signal, and providing feedback on the patient's physiological response based on the signal evaluation index. The music signal evaluation index is M; Where M is the music signal evaluation index. This refers to the difference in the patient's heart rate at the current treatment time compared to before treatment. The difference between the maximum heart rate and the maximum heart rate. This represents the root mean square of the adjacent RR intervals within the current treatment cycle for the patient. This represents the maximum root mean square change between adjacent RR intervals. The root mean square of the patient's electromyography (EMG) signals during the current treatment cycle. This represents the maximum root-mean-square variation of the electromyographic signal. This represents the difference in skin electrical activity between the patient at the current treatment time and before treatment. This represents the maximum change in skin electrical activity. Weighting the impact of heart rate changes. Weighting for the impact of heart rate variability. The weights are affected by changes in electromyographic signals. The weights for the influence of skin conductance response are: the weights for the influence of heart rate change, heart rate variability, electromyography signal change, and skin conductance response are summed to 1. The maximum heart rate difference, the maximum root mean square change, the maximum root mean square change of electromyographic signal, and the maximum activity change of skin electrical activity all refer to the maximum change within the standard range. Heart rate variability is determined based on the root mean square of the difference between adjacent RR intervals, where the adjacent RR interval refers to the time interval between two adjacent R waves on an electrocardiogram. The root mean square (RMS) value of electromyography (EMG) signals refers to the change in the RMS value of the EMG signal of the target muscle, which is used to reflect the degree of muscle tension. The weights for the influence of heart rate variability, heart rate variability, electromyographic signal changes, and skin conductance response are dynamically adjusted according to the patient's clinical physiotherapy goals.

2. The music bioelectric therapy device based on nursing data according to claim 1, characterized in that, The data acquisition module includes: The data acquisition module is configured to collect patients' physiological data in real time through wearable patches and build a nursing database for storing the collected user nursing data. In the nursing database, data mining algorithms and knowledge graph technology are used to deeply integrate nursing data, including linking patients’ daily routines with their diet to analyze their eating patterns, and combining historical physiotherapy records and health records to uncover potential nursing needs. The quality assessment module is configured to perform quality assessment and correction on the collected physiological and nursing data, including data integrity detection, data accuracy verification, and intelligent correction and compensation. The associated data acquisition module is configured to install temperature and humidity sensors, light sensors, and noise sensors in the physiotherapy room to collect environmental temperature and humidity, light intensity, and noise decibel data in real time. Then, data fusion technology is used to integrate the environmental data with the aforementioned physiological and nursing data to explore the potential correlation between environmental factors and patient care and physiotherapy effects.

3. The music bioelectric therapy device based on nursing data according to claim 2, characterized in that, The quality assessment module specifically includes: Data integrity detection: Real-time monitoring of the integrity of various physiological and nursing data collection. By setting reasonable data continuity thresholds and missing value judgment rules, it detects data interruptions or missing data, immediately issues alarms, and automatically records the time period and type of missing data. Data accuracy verification: A data accuracy assessment model is established using machine learning algorithms to verify the collected current patient data by comparing it with the normal physiological data range of similar patients. Intelligent correction and compensation: When data integrity or accuracy is compromised, a Kalman filter signal processing algorithm is used to intelligently compensate for and correct missing or erroneous data by combining normal data from previous and subsequent time periods. At the same time, errors in nursing data are corrected by logical reasoning through correlation with work and rest schedules and other data to ensure the overall data quality is reliable.

4. The music bioelectric therapy device based on nursing data according to claim 3, characterized in that, The calculation formula for the data integrity check is as follows: In the formula, the data continuity threshold T c A measure of time interval used to determine whether physiological or nursing data are continuous within a reasonable timeframe; T m The baseline time interval is set based on the frequency of normal physiological data collection or nursing record keeping; ΔT is the allowable time fluctuation range, usually taken as 0.1 to 0.

3.

5. The music bioelectric therapy device based on nursing data according to claim 2, characterized in that, The associated acquisition module specifically includes: Statistical analysis methods were used to analyze the correlation between environmental factors and patients' physiological and nursing data to identify potential relationships. By conducting trend analysis on the fused data, we can observe the changing trends of environmental factors, physiological data, and nursing data over time, determine whether there are regular changes or abnormal fluctuations, and provide a basis for adjusting the physiotherapy plan. The above analysis results are fed back to the data processing module to adjust the physiotherapy goals and generate corresponding music signals and bioelectric stimulation signals.

6. The music bioelectric therapy device based on nursing data according to claim 1, characterized in that, The data processing module includes: The data cleaning module is configured to clean the collected physiological and nursing data, removing obvious noise and outliers. The data synchronization module is configured to synchronize physiological and nursing data at different timestamps to ensure that subsequent analysis is conducted within a unified time frame. The feature extraction module is configured to extract key features from physiological data, including the mean and coefficient of variation of heart rate, the fluctuation range of systolic and diastolic blood pressure, and the amplitude and frequency characteristics of electromyographic signals. Extract features related to patients' health status and nursing needs from nursing data, including the regularity of their daily routine, the balance of their diet, and the effective treatment duration and frequency in their historical physiotherapy records. The status assessment module is configured to assess the patient's current physiological status based on extracted physiological characteristics, combined with medical reference standards and thresholds. Based on nursing characteristics and established nursing quality standards, assess the effectiveness and appropriateness of the patient's current nursing interventions; The data analysis module is configured to comprehensively analyze the patient's specific nursing needs based on the patient's status assessment results, nursing standards, and individualized needs. It also formulates individualized physiotherapy goals based on the patient's physiological state and nursing needs, combined with physiotherapy principles and previous treatment cases.

7. The music electrotherapy device based on nursing data according to claim 6, characterized in that, The data analysis module includes: Based on the patient status assessment results, risk scores are calculated for patients, and indicators with high risk scores are extracted as the patient's primary physiological needs. Determine the trend of patient care optimization based on the results of nursing effectiveness assessment; Nursing indicators that are above the average optimization trend in the nursing optimization trend are taken as the patient's nursing needs; Based on the patient's personalized needs for physiotherapy, the patient's secondary physiological needs are obtained through needs processing. Compare the first physiological need and the second physiological need, and determine whether there is a conflict between the first physiological need and the second physiological need; When there is no conflict of needs, the physiological priority of the second physiological need is determined based on the analytic hierarchy process. Based on the priority of physiological needs corresponding to the second physiological need, the first physiological need is optimized to obtain the optimized physiological needs; When there is a conflict of needs, the sub-needs corresponding to the conflict of needs in the second physiological needs are removed, and the remaining physiological needs are taken as the third physiological needs. Based on the priority of the third physiological need, the first physiological need is optimized to obtain the optimized physiological needs; Patients with the highest similarity to the patient's physiological data were selected from the historical physiotherapy database as baseline patients; The baseline physiological and nursing needs of the baseline patient are prioritized as the initial priority of the patient's optimized physiological and nursing needs. The patient's initial physiotherapy plan is determined based on the initial demand priority and the corresponding optimized physiological and nursing needs. Acquire patient's physiotherapy data and plans throughout the entire physiotherapy cycle, and optimize the initial physiotherapy plan based on the physiotherapy data and plans; The patient's individualized physical therapy goals are determined based on the results of the optimized treatment plan.

8. The music bioelectric therapy device based on nursing data according to claim 1, characterized in that, The music therapy module also includes: The music generation module is configured to set the basic characteristics of music based on the physiotherapy goals and patient care needs determined by the data processing module, including the frequency range, rhythm pattern and basic volume of the music. Based on the patient's preferences and treatment needs, it selects the corresponding music type from the music library and generates a specific music signal using music synthesis technology based on the set music characteristics and the selected music type. The biophysical therapy module is configured to generate corresponding bioelectric stimulation signals based on the physiotherapy goals and the rhythmic phase coupling requirements with the music signal. The frequency, amplitude, and waveform of the bioelectric signals are matched with the key features of the music signals.

9. The music bioelectric therapy device based on nursing data according to claim 8, characterized in that, The biophysical therapy module includes: Rhythm analysis was performed on music signals and bioelectric stimulation signals respectively to extract key beat points and phase information; Based on the rhythm analysis results, the triggering timing of the bioelectric stimulation signal is adjusted to achieve synchronous coupling between the rhythm phase of the bioelectric stimulation signal and the music signal.

10. The music bioelectric therapy device based on nursing data according to claim 1, characterized in that, The music bioelectric therapy device also includes: The safety monitoring module is configured to continuously monitor the bioelectric stimulation parameters to ensure that the bioelectric stimulation parameters are within a safe range. The early warning and intervention module detects that the bioelectric stimulation parameters exceed the safe range, immediately issues an alarm to remind medical staff, and automatically triggers safety intervention measures, including but not limited to suspending bioelectric stimulation and reducing the stimulation intensity.

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