Fatigue monitoring method and device for dyskinesia patient, electronic equipment, medium and computer program product

Abstract

The invention relates to a fatigue monitoring method and device for a dyskinesia patient, electronic equipment, a medium and a computer program product. The method comprises the following steps: acquiring monitoring data of a target dyskinesia patient in a current time period; the heart rate slope is calculated according to the heart rate data in the current time period, and the heart rate variability is calculated according to the electrocardiogram data in the current time period; adjusting the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, and adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability; and calculating a first fatigue index representing the fatigue degree of the target dyskinesia patient in the current time period based on the target calculation amount and the target weight in the current time period. According to the method, the body fatigue of the patients with dyskinesia such as spinal cord injury in the rehabilitation training process can be comprehensively and accurately evaluated from the objective level of multi-azimuth data.

Description

Technical Field

[0001] The present disclosure relates to the technical field of fatigue monitoring, and in particular to a fatigue monitoring method and device, electronic equipment, medium, and computer program product for patients with motor dysfunction. Background Art

[0002] Currently, mainstream fatigue monitoring technologies are mostly based on physiological assessments of healthy individuals and are widely used in scenarios such as driver fatigue identification and fitness monitoring. These methods typically rely on an individual's complete sensory feedback system, analyzing the correlation between subjective fatigue scores and physiological signals such as heart rate variability, surface electromyography, and skin conductance to construct fatigue status recognition models. However, these methods are difficult to apply to patients with motor dysfunction, particularly those with spinal cord injury or stroke.

[0003] Since patients with motor dysfunction generally have varying degrees of sensory loss or loss below the injury plane, it is difficult for them to produce subjective fatigue perception that matches their physical state during training, which makes the traditional monitoring model that relies on subjective-physiological coupling ineffective. At the same time, in clinical rehabilitation practice, especially in the reconstruction of patient motor function training based on neuromodulation technology, the training intensity and load rhythm are often determined by the experience of clinicians or rehabilitation therapists and fixed. The patients themselves cannot effectively feedback their fatigue state during training, and it is very easy to overtrain without perception. This may not only lead to a decline in training quality, but may also induce secondary injuries such as muscle fatigue injury, knee joint overload or abnormal postural compensation, which will seriously interfere with the rehabilitation rhythm. In addition, traditional fatigue assessment methods fail to perform dynamic comprehensive processing based on autonomic nervous system regulation parameters.

[0004] Therefore, for this special group with sensory loss, there is an urgent need for a fatigue monitoring method that does not rely on subjective perception and can objectively, continuously and dynamically evaluate the body's fatigue state. Summary of the Invention

[0005] In view of this, the present disclosure proposes a fatigue monitoring method and device, electronic equipment, medium and computer program product for patients with movement dysfunction, which can comprehensively and accurately evaluate the body fatigue generated by patients with movement dysfunction such as spinal cord injury during rehabilitation training from an objective and multi-faceted data perspective, provide a scientific basis for adjusting training intensity, and avoid the adverse effects of sports injuries on the rehabilitation training of patients with movement dysfunction.

[0006] According to one aspect of the present disclosure, a fatigue monitoring method for a patient with movement dysfunction is provided, comprising: obtaining monitoring data of a target patient with movement dysfunction in a current time period, the monitoring data including heart rate data and electrocardiogram (ECG) data; calculating a heart rate slope based on the heart rate data in the current time period, and calculating a heart rate variability based on the ECG data in the current time period, wherein the heart rate slope represents the rate at which the heart rate of the target patient with movement dysfunction changes over time in the current time period, and the heart rate variability represents the ability of the autonomic nervous system of the target patient with movement dysfunction to regulate cardiac rhythm in the current time period; adjusting a historical heart rate weight to an updated heart rate weight based on a preset weight adjustment rule and the heart rate slope, and adjusting a historical variability weight to an updated variability weight based on the weight adjustment rule and the heart rate variability; and calculating a first fatigue index representing the degree of fatigue of the target patient with movement dysfunction in the current time period based on a target calculation amount and a target weight in the current time period, wherein the target calculation amount includes the heart rate slope and the heart rate variability, and the target weight includes the updated heart rate weight and the updated variability weight.

[0007] In one possible implementation, the historical heart rate weight is adjusted to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, including: if it is determined according to the heart rate slope that the heart rate change trend is a downward trend, then the historical heart rate weight after the first increment corresponding to the downward trend is increased as the latest heart rate weight; if it is determined according to the heart rate slope that the heart rate change trend is an upward trend, then the historical heart rate weight after the first decrement corresponding to the upward trend is reduced as the latest heart rate weight.

[0008] In one possible implementation, the historical variability weight is adjusted to the latest variability weight based on the weight adjustment rule and the heart rate variability, including: if it is determined according to the heart rate variability that the trend of change in the regulatory ability is a decreasing trend, the historical variability weight after the second increment corresponding to the decreasing trend is increased as the latest variability weight; if it is determined according to the heart rate variability that the trend of change in the regulatory ability is an increasing trend, the historical variability weight after the second decrement corresponding to the increasing trend is reduced as the latest variability weight; wherein the sum of the latest heart rate weight and the latest variability weight is less than or equal to a preset weight threshold.

[0009] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, including: determining a first value based on the heart rate slope and the latest heart rate weight, and determining a second value based on the heart rate variability and the latest variability weight; and determining the first fatigue index based on the first value and the second value.

[0010] In one possible implementation, the monitoring data also includes force data, and the force data represents the force applied by the target motor dysfunction patient on the rehabilitation training equipment during rehabilitation training; wherein, the method also includes: calculating the force slope based on the force data in the current time period, and the force slope represents the force change trend of the target motor dysfunction patient in the current time period; adjusting the historical force weight to the latest force weight based on the weight adjustment rule and the force slope, the target calculation amount also includes the force slope, and the target weight also includes the latest force weight.

[0011] In one possible implementation, the historical force weight is adjusted to the latest force weight based on the weight adjustment rule and the force slope, including: if it is determined according to the force slope that the force change trend is a decreasing trend, then the historical force weight after the third reduction corresponding to the decreasing trend is reduced as the latest force weight; if it is determined according to the force slope that the force change trend is an increasing trend, then the historical force weight after the third increment corresponding to the increasing trend is increased as the latest force weight; wherein the sum of the latest heart rate weight, the latest variability weight, and the latest force weight is less than or equal to a preset weight threshold.

[0012] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, and also includes: determining a third value based on the force slope and the latest force weight; determining the first fatigue index based on the first value, the second value, and the third value, wherein the first value is determined based on the heart rate slope and the latest heart rate weight, and the second value is determined based on the heart rate variability and the latest variability weight.

[0013] In a possible implementation, the method further includes: inputting the monitoring data of the target motor dysfunction patient in the current time period into a first fatigue prediction model for calculation to obtain a second fatigue index of the target motor dysfunction patient in the current time period.

[0014] In a possible implementation, the method further includes: inputting the target calculation amount and the fatigue index corresponding to the current time period into a second fatigue prediction model for calculation to obtain a predicted fatigue index of the target motor dysfunction patient in a future time period.

[0015] In one possible implementation, the method also includes at least one of the following: if it is determined that the fatigue index corresponding to the current time period is less than or equal to a preset first index threshold, then it is determined that the rehabilitation training program for the target movement dysfunction patient remains unchanged; if it is determined that the fatigue index corresponding to the current time period is greater than the first index threshold and less than or equal to the second index threshold, then it is determined to reduce the training intensity in the rehabilitation training program for the target movement dysfunction patient; if it is determined that the fatigue index corresponding to the current time period is greater than the second index threshold, then it is determined to stop the rehabilitation training program for the target movement dysfunction patient; wherein, the first index threshold is less than the second index threshold.

[0016] According to another aspect of the present disclosure, a fatigue monitoring device for patients with motor dysfunction is provided, comprising an acquisition module for acquiring monitoring data of a target motor dysfunction patient within a current time period, wherein the monitoring data includes heart rate data and electrocardiogram data; a first calculation module for calculating a heart rate slope based on the heart rate data within the current time period, and calculating a heart rate variability based on the electrocardiogram data within the current time period, wherein the heart rate slope represents the rate at which the heart rate of the target motor dysfunction patient changes over time within the current time period, and the heart rate variability represents the autonomous movement of the target motor dysfunction patient within the current time period. The nervous system's ability to regulate heart rhythm; an adjustment module for adjusting the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, and adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability; a second calculation module for calculating a first fatigue index representing the fatigue level of the target motor dysfunction patient in the current time period based on the target calculation amount and target weight in the current time period, wherein the target calculation amount includes the heart rate slope and the heart rate variability, and the target weight includes the latest heart rate weight and the latest variability weight.

[0017] In one possible implementation, the historical heart rate weight is adjusted to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, including: if it is determined according to the heart rate slope that the heart rate change trend is a downward trend, then the historical heart rate weight after the first increment corresponding to the downward trend is increased as the latest heart rate weight; if it is determined according to the heart rate slope that the heart rate change trend is an upward trend, then the historical heart rate weight after the first decrement corresponding to the upward trend is reduced as the latest heart rate weight.

[0018] In one possible implementation, the historical variability weight is adjusted to the latest variability weight based on the weight adjustment rule and the heart rate variability, including: if it is determined according to the heart rate variability that the trend of change in the regulatory ability is a decreasing trend, the historical variability weight after the second increment corresponding to the decreasing trend is increased as the latest variability weight; if it is determined according to the heart rate variability that the trend of change in the regulatory ability is an increasing trend, the historical variability weight after the second decrement corresponding to the increasing trend is reduced as the latest variability weight; wherein the sum of the latest heart rate weight and the latest variability weight is less than or equal to a preset weight threshold.

[0019] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, including: determining a first value based on the heart rate slope and the latest heart rate weight, and determining a second value based on the heart rate variability and the latest variability weight; and determining the first fatigue index based on the first value and the second value.

[0020] In one possible implementation, the monitoring data also includes force data, and the force data represents the force applied by the target motor dysfunction patient on the rehabilitation training equipment during rehabilitation training; wherein, the device also includes a calculation adjustment module, which is used to: calculate the force slope based on the force data in the current time period, and the force slope represents the force change trend of the target motor dysfunction patient in the current time period; adjust the historical force weight to the latest force weight based on the weight adjustment rule and the force slope, the target calculation amount also includes the force slope, and the target weight also includes the latest force weight.

[0021] In one possible implementation, the historical force weight is adjusted to the latest force weight based on the weight adjustment rule and the force slope, including: if it is determined according to the force slope that the force change trend is a decreasing trend, then the historical force weight after the third reduction corresponding to the decreasing trend is reduced as the latest force weight; if it is determined according to the force slope that the force change trend is an increasing trend, then the historical force weight after the third increment corresponding to the increasing trend is increased as the latest force weight; wherein the sum of the latest heart rate weight, the latest variability weight, and the latest force weight is less than or equal to a preset weight threshold.

[0022] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, and also includes: determining a third value based on the force slope and the latest force weight; determining the first fatigue index based on the first value, the second value, and the third value, wherein the first value is determined based on the heart rate slope and the latest heart rate weight, and the second value is determined based on the heart rate variability and the latest variability weight.

[0023] In one possible implementation, the device also includes a third calculation module, which is used to: input the monitoring data of the target motor dysfunction patient in the current time period into the first fatigue prediction model for calculation to obtain the second fatigue index of the target motor dysfunction patient in the current time period.

[0024] In one possible implementation, the device also includes a fourth calculation module, which is used to: input the target calculation amount and the fatigue index corresponding to the current time period into a second fatigue prediction model for calculation to obtain the predicted fatigue index of the target motor dysfunction patient in the future time period.

[0025] In one possible implementation, the device also includes a program determination module, which is used to perform at least one of the following: if it is determined that the fatigue index corresponding to the current time period is less than or equal to a preset first indicator threshold, it is determined that the rehabilitation training program for the target movement dysfunction patient remains unchanged; if it is determined that the fatigue index corresponding to the current time period is greater than the first indicator threshold and less than or equal to the second indicator threshold, it is determined to reduce the training intensity in the rehabilitation training program for the target movement dysfunction patient; if it is determined that the fatigue index corresponding to the current time period is greater than the second indicator threshold, it is determined to stop the rehabilitation training program for the target movement dysfunction patient; wherein, the first indicator threshold is less than the second indicator threshold.

[0026] According to another aspect of the present disclosure, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0027] According to another aspect of the present disclosure, a non-volatile computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0028] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0029] Further features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate exemplary embodiments, features, and aspects of the disclosure and, together with the description, serve to explain the principles of the disclosure.

[0031] Figure 1 A flowchart of a fatigue monitoring method for patients with motor dysfunction provided by an embodiment of the present disclosure is shown.

[0032] Figures 2 to 3 A schematic diagram illustrating a fatigue monitoring method for patients with motor dysfunction provided by an embodiment of the present disclosure.

[0033] Figure 4 A block diagram of a fatigue monitoring device for patients with motor dysfunction provided by an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0034] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise indicated.

[0035] As used herein, the terms "comprises," "comprising," "having," or variations thereof are open ended and include one or more stated features, integers, elements, steps, parts, or functions, but do not preclude the presence or addition of one or more other features, integers, elements, steps, parts, functions, or groups thereof.

[0036] When an element is referred to as being "connected," "coupled," "responsive" or variations thereof to another element, it can be directly connected, coupled or responsive to the other element or intervening elements may be present.

[0037] Although the terms first, second, third, etc. may be used herein to describe various elements / operations, these elements / operations should not be limited by these terms. These terms are only used to distinguish one element / operation from another element / operation. Therefore, without departing from the teachings of the present invention, the first element / operation in some embodiments may be referred to as the second element / operation in other embodiments.

[0038] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0039] In addition, numerous specific details are provided in the following detailed description to better illustrate the present disclosure. Those skilled in the art will appreciate that the present disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art are not described in detail in order to highlight the main points of the present disclosure.

[0040] In order to facilitate those skilled in the art to understand the technical solution provided by the embodiments of the present disclosure, the technical environment in which the technical solution is implemented is described below.

[0041] In rehabilitation medicine, heart rate and heart rate variability are critical indicators for assessing a patient's fatigue level during rehabilitation training. There are deep physiological mechanisms behind this. The human body's autonomic nervous system consists of sympathetic and parasympathetic nerves, which work together to regulate cardiac activity. During exercise, the sympathetic nerves become excited, stimulating the secretion of hormones such as adrenaline, which in turn increases the heart rate to meet the body's higher demand for energy and oxygen during exercise. When the body is at rest, the parasympathetic nerves play a dominant role, slowing the heart rate and helping the body recover.

[0042] Once fatigue sets in, this balance of the autonomic nervous system is disrupted. In the early stages of training, sympathetic nervous system excitation predominates, leading to a significant increase in heart rate. However, as training continues and fatigue accumulates, sympathetic nervous system excitability gradually decreases, while parasympathetic nervous system inhibition becomes relatively stronger. This change leads to a decrease in heart rate reactivity, manifested as a smaller increase in heart rate at the same exercise load or a slower decrease in heart rate after exercise. Heart rate variability is also significantly affected. Heart rate variability reflects the time difference between successive cardiac cycles and reflects the heart's ability to regulate the autonomic nervous system. Under normal physiological conditions, high heart rate variability indicates that the heart has good regulatory function and can flexibly respond to various physiological demands. However, when the body is fatigued, the regulatory capacity of the sympathetic and parasympathetic nervous systems decreases, and heart rate variability decreases accordingly, indicating that the heart's adaptability and regulatory capacity have weakened and the body is in a state of fatigue.

[0043] From a practical perspective, measuring heart rate and heart rate variability offers many significant advantages. First, the measurement method is simple and easy. With the rapid advancement of technology, a wide range of portable heart rate monitoring devices, such as smart bracelets and smart watches, are emerging. These devices are simple and convenient to use, enabling everyday users to easily use them during daily exercise without the need for complex medical equipment or professional guidance. They provide real-time, continuous monitoring of heart rate and heart rate variability, allowing users to obtain physiological information anytime, anywhere. Second, this data has high analytical value. Heart rate intuitively reflects the heart's beating frequency. By comparing heart rate changes at different training stages and exercise intensities, it is possible to initially assess the degree of fatigue. For example, if the heart rate is significantly higher than usual at the same training intensity, or if it takes longer to return to normal after exercise, these may be signs of fatigue. Heart rate variability, however, contains even richer physiological information. Through specialized methods such as frequency and time domain analysis, parameters such as standard deviation, root mean square value, low-frequency power, and high-frequency power can be derived. These parameters can reflect the functional state of the autonomic nervous system and the degree of physical fatigue from a more comprehensive and detailed perspective. For example, low-frequency power is closely related to sympathetic nerve activity, while high-frequency power is related to parasympathetic nerve activity. When the body is fatigued, low-frequency power may increase relatively, while high-frequency power may decrease. In addition, combining heart rate and heart rate variability monitoring data with other fatigue monitoring indicators, such as subjective fatigue feeling scores after exercise and muscle strength test results, can more comprehensively and accurately assess the body's fatigue state, providing a more reliable basis for scientific and reasonable adjustments to training plans.

[0044] However, most existing fatigue monitoring technologies are designed for healthy individuals and are primarily used in areas such as driver fatigue monitoring. Patients with motor dysfunction face a unique dilemma. Due to physical injuries, they experience sensory loss below the level of injury. During rehabilitation training with electrical stimulation therapy, they struggle to perceive the fatigue produced by training as well as normal individuals. This loss of perception makes them highly susceptible to overtraining, which can lead to secondary injuries such as knee injuries, severely hindering their recovery.

[0045] In order to solve the above technical problems, the embodiments of the present disclosure provide a fatigue monitoring method for patients with movement dysfunction. By real-time collection of physiological feedback indicators of patients with movement dysfunction during rehabilitation training, and using weight matching to calculate a real-time fatigue index (Fatigue Scale), the fatigue monitoring method of the embodiments of the present disclosure can make comprehensive judgments based on multi-dimensional cardiopulmonary and mechanical data, without relying on subjective perception, and comprehensively evaluate the body fatigue generated during the training of patients with movement dysfunction from multi-faceted data at an objective level, providing a reliable scientific reference basis, namely the fatigue index, to achieve objective, continuous and dynamic evaluation of the body fatigue state of patients with movement dysfunction, so as to dynamically adjust the training intensity of patients with movement dysfunction, avoid sports injuries from causing adverse effects on the rehabilitation training of patients with movement dysfunction, and ensure that rehabilitation training is carried out safely and effectively.

[0046] Now combined Figures 1 to 3 The fatigue monitoring method provided by the embodiment of the present disclosure is schematically described. The fatigue monitoring method of the embodiment of the present disclosure can be applied to a terminal device, so that patients with motor dysfunction or trainers can understand the body fatigue generated during the patient's training and respond in time, ensuring that rehabilitation training is carried out safely and effectively. For example, the terminal device of the embodiment of the present disclosure can be a tablet computer, a laptop computer, etc. Figure 1 As shown, the fatigue monitoring method may include the following steps S101 to S104.

[0047] Step S101: Acquire monitoring data of a target motor dysfunction patient in a current time period.

[0048] Monitoring data may include heart rate data and electrocardiogram data. For example, Figure 1 As shown, monitoring data can be acquired in real time using a chest strap worn by the target motor dysfunction patient. This device communicates with a computer via Bluetooth, allowing the computer to receive the target motor dysfunction patient's heart rate and electrocardiogram (ECG) data for the current time period. Heart rate data refers to the target motor dysfunction patient's heart rate beats per minute. ECG data refers to the changes in the bioelectrical potential of the target motor dysfunction patient's heart during each cardiac cycle.

[0049] The current time period may include the time period during which the target motor dysfunction patient is undergoing rehabilitation training, or may include the time period during which the target motor dysfunction patient is not undergoing rehabilitation training. The division of the current time period depends on the actual situation. For example, the timing may be started after the target motor dysfunction patient wears a heart rate chest belt to obtain monitoring data at time points t0, t1, t2, ..., tn. t0 to t20 may be used as the first current time period, t1 to t21 may be used as the second current time period, and so on. In this way, the fatigue index of the current time period may be calculated based on the monitoring data in the current time period, and the fatigue index of the next current time period may be calculated after the monitoring data at the next time point is obtained, thereby realizing continuous and real-time calculation of the fatigue index of the current time period, so as to provide a reference value indicating the degree of body fatigue that may occur in the patient, so that the training personnel can dynamically adjust the training intensity of the motor dysfunction patient.

[0050] Step S102: Calculate the heart rate slope based on the heart rate data in the current time period, and calculate the heart rate variability based on the electrocardiogram data in the current time period.

[0051] The heart rate slope can represent the rate at which the heart rate of the target movement dysfunction patient changes over time during the current time period. The heart rate change trend of the target movement dysfunction patient can be determined based on the heart rate slope, and the heart rate change trend is a decreasing trend or an increasing trend. For example, the highest heart rate in the current time period can be determined based on the heart rate data in the current time period, and then the historical heart rate before the preset time interval is checked from this highest heart rate. If the overall change trend of the heart rate in this time interval is an upward trend, the heart rate rising slope (Heart Rate Gradient, HRG) is calculated; if the overall change trend of the heart rate in this time interval is a downward trend, the heart rate falling rate is calculated, also known as the heart rate recovery rate (Heart Rate Recovery Rate, HRR). In this fatigue monitoring method, the heart rate rising slope is used as the preferred heart rate slope. Taking HRG as an example, the time interval can be preset to 5 seconds. Specifically, HRG can be calculated by △heart rate / △time, where △heart rate = highest heart rate - historical heart rate, △time = 5 seconds. In fact, the heart rate slope can be completely other ways in the relevant technology, and the embodiments of the present disclosure are not limited to this.

[0052] Heart rate variability can indicate the ability of the autonomic nervous system of the target movement dysfunction patient to regulate the heart rhythm in the current time period. According to the heart rate variability, the changing trend of the target movement dysfunction patient's ability to regulate the heart rhythm can be determined, and the changing trend of the regulating ability is a decreasing trend or an increasing trend. For example, the root mean square successive deviation (RMSSD) algorithm can be used to calculate the specific value of the heart rate variability from the electrocardiogram data in the current time period, and the changing trend of the target movement dysfunction patient's ability to regulate the heart rhythm can be determined based on the specific value. In fact, the calculation method of heart rate variability can completely adopt other methods in the relevant technology, and the embodiments of the present disclosure are not limited to this.

[0053] In some examples, the acquired heart rate data and ECG data may be normalized before calculating the heart rate slope and heart rate variability. The order in which the heart rate slope and heart rate variability are calculated may be a specific sequence or performed simultaneously, depending on the actual situation and is not limited in this disclosure.

[0054] Step S103: adjusting the historical heart rate weight to the latest heart rate weight based on the preset weight adjustment rule and the heart rate slope, and adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability.

[0055] After obtaining the heart rate slope of the target motor dysfunction patient in the current time period, the latest heart rate weight can be determined in combination with the weight adjustment rules. After obtaining the heart rate variability of the target motor dysfunction patient in the current time period, the latest variability weight can be determined in combination with the weight adjustment rules. The weight adjustment rule may refer to a specific way of adjusting the historical heart rate weight according to the heart rate slope in the current time period and a specific way of adjusting the historical variability weight according to the heart rate variability in the current time period, and when the two calculation quantities of heart rate slope and heart rate variability are used for weight matching calculation, the sum of the adjusted latest heart rate weight and the latest variability weight is less than or equal to a preset weight threshold. For example, the weight threshold can be taken as 1, specifically, the historical heart rate weight is adjusted from 0.5 to 0.55 and the historical variability weight is adjusted from 0.5 to 0.45, so that 0.55+0.45=1.

[0056] The historical heart rate weight adjusted for the first current time period includes the initial heart rate weight, and the historical heart rate weight adjusted for each current time period after the first current time period includes the latest heart rate weight adjusted for the previous current time period. For example, in the first current time period, the historical heart rate weight at this time, i.e., the initial heart rate weight (denoted as a0), is adjusted to the latest heart rate weight (denoted as a1); in the second current time period, the historical heart rate weight at this time, i.e., a1, is adjusted to a2; in the third current time period, the historical heart rate weight at this time, i.e., a2, is adjusted to a3, and the adjustment process for subsequent current time periods is similar.

[0057] The adjusted historical variability weight corresponding to the first current time period includes the initial variability weight, and the adjusted historical variability weight corresponding to each current time period after the first current time period includes the latest variability weight adjusted for the previous current time period. For example, in the first current time period, the historical variability weight at that time, i.e., the initial variability weight (denoted as b0), is adjusted to the latest variability weight (denoted as b1); in the second current time period, the historical variability weight at that time, i.e., b1, is adjusted to b2; in the third current time period, the historical variability weight at that time, i.e., b2, is adjusted to b3, and the adjustment process for subsequent current time periods is similar.

[0058] Adjusting the historical heart rate weight to the latest heart rate weight based on the preset weight adjustment rule and the heart rate slope in step S103 may include: if it is determined according to the heart rate slope that the heart rate change trend is a decreasing trend, then the historical heart rate weight after the first increment corresponding to the decreasing trend is increased as the latest heart rate weight; if it is determined according to the heart rate slope that the heart rate change trend is an increasing trend, then the historical heart rate weight after the first decrement corresponding to the increasing trend is reduced as the latest heart rate weight.

[0059] For example, in the current time period, if it is determined that the trend of heart rate change is a downward trend, a corresponding first increment can be determined based on this downward trend. This first increment can be a fixed increment. For example, when it is determined that the trend of heart rate change is a downward trend, a fixed increment is added to the historical heart rate weight to obtain the latest heart rate weight. This first increment can also be a corresponding increment determined based on the significance of the downward trend. This increment can change with the significance. For example, because the downward trend is more significant, a larger increment is added to the historical heart rate weight to obtain the latest heart rate weight. The adjustment process when the heart rate change trend is an upward trend is similar to the adjustment process when the heart rate change trend is a downward trend, and will not be repeated here.

[0060] Adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability in step S103 may include: if it is determined according to the heart rate variability that the trend of change in the regulatory ability is a decreasing trend, then the historical variability weight after the second increment corresponding to the decreasing trend is increased as the latest variability weight; if it is determined according to the heart rate variability that the trend of change in the regulatory ability is an increasing trend, then the historical variability weight after the second decrement corresponding to the increasing trend is decreased as the latest variability weight.

[0061] For example, in the current time period, if it is determined that the trend of the change in the regulatory ability is a decreasing trend, a corresponding second increment can be determined based on this decreasing trend. This second increment can be a fixed increment. For example, when it is determined that the trend of the change in the regulatory ability is a decreasing trend, a fixed increment is added to the historical variability weight to obtain the latest variability weight. This second increment can also be a corresponding increment determined based on the significance of the decreasing trend. This increment can change with the significance. For example, because the decreasing trend is more significant, a larger increment is added to the historical variability weight to obtain the latest variability weight. The adjustment process when the trend of the change in the regulatory ability is an upward trend is similar to the adjustment process when the trend of the change in the regulatory ability is a decreasing trend, and will not be repeated here.

[0062] Step S104: Calculate a first fatigue index representing the fatigue level of the target motor dysfunction patient in the current time period based on the target calculation amount and the target weight in the current time period.

[0063] The target calculation amount may include heart rate slope and heart rate variability, and the target weight may include the latest heart rate weight and the latest variability weight, such as Figure 2 As shown, a weighted ratio calculation is performed based on the two calculated quantities, heart rate slope and heart rate variability, to obtain a first fatigue index. Thus, step S104 may include: determining a first value based on the heart rate slope and the latest heart rate weight; and determining a second value based on the heart rate variability and the latest variability weight; and determining the first fatigue index based on the first value and the second value.

[0064] For example, after determining the heart rate slope (denoted as x1), the latest heart rate weight (denoted as a1), the heart rate variability (denoted as x2), and the latest variability weight (denoted as a2) within the current time period, the result of x1*a1 can be used as the first value (denoted as y1), the result of x2*a2 can be used as the second value (denoted as y2), and the result of y1+y2 can be used as the first fatigue indicator. The first value, second value, and calculation method for determining the first fatigue indicator based on the first and second values ​​in this example are merely illustrative. In fact, other methods can be used in actual calculations, and the present disclosure does not limit this.

[0065] In this way, by collecting the physiological feedback indicators such as heart rate and electrocardiogram of the target patients with motor dysfunction in real time, especially during rehabilitation training, and calculating the real-time fatigue index based on the weighted ratio of the two calculation quantities, heart rate slope and heart rate variability, a reliable reference basis can be provided for training personnel so that they can dynamically adjust the training intensity and ensure that rehabilitation training is carried out safely and effectively.

[0066] The fatigue monitoring method provided by the embodiment of the present disclosure can also calculate a real-time fatigue index based on the weighted ratio of the three calculation quantities: heart rate slope, heart rate variability, and force slope.

[0067] The monitoring data of the target motor dysfunction patient obtained by this fatigue monitoring method during the current time period may also include force data. The force data represents the force applied by the target motor dysfunction patient on the rehabilitation training equipment during rehabilitation training. This force may specifically include the pressure or tension applied by the hand of the target motor dysfunction patient on the rehabilitation training equipment, such as a walker, and may also include the pressure applied by the leg of the target motor dysfunction patient on the sole of the foot on the rehabilitation training equipment, such as a pressure plate. For example, a force sensor may be used to obtain the force applied by the hand of the target motor dysfunction patient on the rehabilitation training equipment.

[0068] This fatigue monitoring method may also include: calculating the force slope based on the force data in the current time period, the force slope represents the force change trend of the target movement dysfunction patient in the current time period; adjusting the historical force weight corresponding to the force slope to the force weight according to the weight adjustment rule and the force slope, the target calculation amount also includes the force slope, and the target weight also includes the force weight.

[0069] For example, the maximum force within the current time period can be determined based on the force data within the current time period. Next, starting from this maximum force, the historical forces before a preset time interval are retrieved. This time interval is denoted as Δt. The force slope can be calculated as Δforce / Δt, where Δforce = maximum force - historical forces. In fact, the force slope can be calculated using other methods, and this disclosure is not limited thereto.

[0070] After obtaining the force slope of the target motor dysfunction patient in the current time period, the latest force weight can be determined in combination with the weight adjustment rule. The weight adjustment rule can also refer to a specific way of adjusting the historical force weight according to the force slope in the current time period, and when the three calculation quantities of heart rate slope, heart rate variability, and force slope are used for weight matching calculation, the sum of the adjusted latest heart rate weight, latest variability weight, and latest force weight is less than or equal to the preset weight threshold. The weight threshold can select a suitable value from (0,1] according to actual needs. There is no special restriction on the size between the weight threshold and the weight threshold.

[0071] The historical force weight adjusted corresponding to the first current time period includes the initial force weight, and the historical force weight adjusted corresponding to each current time period after the first current time period includes the latest force weight adjusted in the previous current time period. For example, in the first current time period, the historical force weight at this time, i.e., the initial force weight (denoted as c0), is adjusted to the latest force weight (denoted as c1); in the second current time period, the historical force weight at this time, i.e., c1, is adjusted to c2; in the third current time period, the historical force weight at this time, i.e., c2, is adjusted to c3, and the adjustment process for subsequent current time periods is analogous. The initial heart rate weight, initial variability weight, and initial force weight can all be flexibly set according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0072] The above-mentioned adjustment of the historical force weight to the latest force weight based on the weight adjustment rule and the force slope may include: if it is determined according to the force slope that the force change trend is a downward trend, then the historical force weight after the third reduction corresponding to the downward trend will be reduced as the latest force weight; if it is determined according to the force slope that the force change trend is an upward trend, then the historical force weight after the third increment corresponding to the upward trend will be increased as the latest force weight.

[0073] For example, in the current time period, if it is determined that the trend of the force change is a decreasing trend, a corresponding third decrement can be determined based on this decreasing trend. This third decrement can be a fixed decrement. For example, when it is determined that the trend of the force change is a decreasing trend, the fixed decrement is reduced on the basis of the historical force weight to obtain the latest force weight. This third decrement can also be determined based on the significance of the decreasing trend. This decrement can change with the significance. For example, because the decreasing trend is more significant, a larger decrement is reduced on the basis of the historical force weight to obtain the latest force weight. The adjustment process when the force change trend is a decreasing trend is similar to the adjustment process when the force change trend is an upward trend, and will not be repeated here.

[0074] When using the three calculation quantities of heart rate slope, heart rate variability, and force slope for weighted ratio calculation, in addition to heart rate slope and heart rate variability, the target calculation quantity also includes force slope; in addition to the latest heart rate weight and the latest variability weight, the target weight also includes the latest force weight. Figure 3 As shown, a weighted ratio calculation is performed based on the heart rate slope, heart rate variability, and force slope to obtain a first fatigue index. Thus, step S104 may further include: determining a third value based on the force slope and the latest force weight; and determining the first fatigue index based on the first value, the second value, and the third value, wherein the first value is determined based on the heart rate slope and the latest heart rate weight, and the second value is determined based on the heart rate variability and the latest variability weight.

[0075] For example, after determining the heart rate slope (denoted as x11), the latest heart rate weight (denoted as a11), the heart rate variability (denoted as x22), the latest variability weight (denoted as a22), the force slope (denoted as x33), and the latest force slope (denoted as a33) in the current time period, the result of x11*a11 can be used as the first value (denoted as y11), the result of x22*a22 can be used as the second value (denoted as y22), and the result of x33*a33 can be used as the third value (denoted as y33). Then, the result of y11+y22+y33 can be used as the first fatigue index. The first value, second value, third value, and the calculation method for determining the first fatigue index based on the first value, second value, and third value in this example are merely illustrative. In fact, other methods can be used in actual calculations, and the present disclosed embodiments do not limit this.

[0076] It should be noted that the specific value of the latest heart rate weight adjusted when the two calculation quantities of heart rate slope and heart rate variability are used for weight matching calculation may be different from the latest heart rate weight adjusted when the three calculation quantities of heart rate slope, heart rate variability and force slope are used for weight matching calculation. Similarly, the specific value of the latest variability weight adjusted when the two calculation quantities of heart rate slope and heart rate variability are used for weight matching calculation may also be different from the heart rate variability adjusted when the three calculation quantities of heart rate slope, heart rate variability and force slope are used for weight matching calculation.

[0077] In this way, by collecting the physiological feedback indicators such as heart rate, electrocardiogram, and force of the target patients with motor dysfunction in real time, especially during rehabilitation training, and calculating the real-time fatigue index based on the weighted ratio of the three calculation quantities of heart rate slope, heart rate variability, and force slope, it can provide a reliable reference basis for training personnel so that they can dynamically adjust the training intensity and ensure that rehabilitation training is carried out safely and effectively.

[0078] In addition to the above-mentioned real-time fatigue index calculated based on the weighted ratio of each calculation amount, such as Figure 3 As shown, the fatigue monitoring method provided by the embodiment of the present disclosure can also obtain predicted fatigue indicators with the help of machine learning algorithms.

[0079] The fatigue monitoring method may also include a first prediction step: inputting the monitoring data of the target motor dysfunction patient during the current time period into a first fatigue prediction model for calculation to obtain a second fatigue index for the target motor dysfunction patient during the current time period. Specifically, the first prediction step may be performed before step S102 above, so that the predicted second fatigue index for the current time period can be promptly viewed, providing a reliable reference for training personnel. The second fatigue index may be the same as or different from the first fatigue index.

[0080] The fatigue monitoring method may further include a second prediction step: inputting the target calculation amount and the fatigue index corresponding to the current time period into a second fatigue prediction model for calculation to obtain a predicted fatigue index for the target motor dysfunction patient in a future time period. The target calculation amount in the input of the second fatigue prediction model may include the heart rate slope and heart rate variability in the current time period, or may include the heart rate slope, heart rate variability, and force slope in the current time period. The fatigue index input into the second fatigue prediction model may include the first fatigue index or the second fatigue index.

[0081] The fatigue prediction model (first fatigue prediction model, second fatigue prediction model) can be obtained by training the machine learning model. The machine learning algorithm involved in the fatigue prediction model can be flexibly selected according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0082] During the training process of the fatigue prediction model, the fatigue prediction model can be trained based on past training data and collected data to provide a predicted fatigue reference index. Furthermore, after each new training data addition, the fatigue prediction model can be self-trained by adding data to the training set, thereby enhancing the fatigue prediction model's accuracy in predicting the patient's fatigue reference index. Past training data and collected data can include target computational loads and fatigue indices over multiple historical time periods. Each new training data addition can include both the actual data input to the fatigue prediction model and the predicted fatigue index calculated from that data.

[0083] In this way, using machine learning algorithms to predict the fatigue indicators of patients with target motor dysfunction can provide training personnel with a reliable reference basis so that they can dynamically adjust the training intensity and ensure that rehabilitation training is carried out safely and effectively.

[0084] After determining the fatigue index (first fatigue index, second fatigue index) of the target patient with motor dysfunction, the target execution mode of the rehabilitation training program for the target patient with motor dysfunction can also be determined based on the fatigue index. The fatigue monitoring method can also include at least one of the following: if it is determined that the fatigue index corresponding to the current time period is less than or equal to a preset first index threshold, then it is determined that the rehabilitation training program for the target patient with motor dysfunction remains unchanged; if it is determined that the fatigue index corresponding to the current time period is greater than the first index threshold and less than or equal to the second index threshold, then it is determined to reduce the training intensity in the rehabilitation training program for the target patient with motor dysfunction; if it is determined that the fatigue index corresponding to the current time period is greater than the second index threshold, then it is determined to stop the rehabilitation training program for the target patient with motor dysfunction; wherein, the fatigue index corresponding to the current time period can be the first fatigue index or the second fatigue index; and the first index threshold is less than the second index threshold.

[0085] For example, the first indicator threshold value can be 3, and the second indicator threshold value can be 4. If it is determined that the fatigue index corresponding to the current time period is less than or equal to 3, this indicates that the target motor dysfunction patient has not yet experienced physical fatigue, then the rehabilitation training program for the target motor dysfunction patient is maintained unchanged and training can continue, but if the patient complains of fatigue, a rest period can be taken. Considering that pure cardiopulmonary fatigue does not mean physical fatigue, the fatigue complained of here may be cardiopulmonary fatigue, such as fatigue caused by the patient's breathing regulation problems when walking. If it is determined that the fatigue index corresponding to the current time period is greater than 3 and less than 4, this indicates that the target motor dysfunction patient may experience physical fatigue, then it is determined to reduce the training intensity in the rehabilitation training program for the target motor dysfunction patient, and the rest time between groups can be appropriately extended, pay attention to the patient's condition, and do not overtrain. If it is determined that the fatigue index corresponding to the current time period is greater than 4, this indicates that the target motor dysfunction patient may experience obvious physical fatigue, then it is determined to stop the rehabilitation training program for the target motor dysfunction patient, i.e., suspend the training, let the patient rest, and adjust the training according to the situation. The specific values ​​of the first indicator threshold and the second indicator threshold are flexibly set according to actual conditions, and the embodiments of the present disclosure do not limit this.

[0086] This fatigue monitoring method may also include: calculating an average fatigue index based on the fatigue index of the target motor dysfunction patient in multiple different time periods; if it is determined that the average fatigue index is less than or equal to a preset first index threshold, determining that the rehabilitation training program for the target motor dysfunction patient in the future time period remains unchanged, and normal training can continue in the future; if it is determined that the average fatigue index is greater than the first index threshold and less than or equal to the second index threshold, determining to reduce the training intensity of the rehabilitation training program for the target motor dysfunction patient in the future time period, and appropriately reducing training; if it is determined that the average fatigue index is greater than the second index threshold, determining to stop the rehabilitation training program for the target motor dysfunction patient in the future time period, and recommending rest to avoid injuries. In this way, the training intensity of the next day can be adjusted according to the existing training data and classification judgment to avoid injuries to the target motor dysfunction patient.

[0087] The fatigue monitoring method may also include: displaying at least one of the monitoring data, target calculation amount, and fatigue index of the target motor dysfunction patient in the current time period, wherein the monitoring data may include at least one of heart rate data, electrocardiogram data, and force data; the target calculation amount may include at least one of heart rate slope, heart rate variability, and force slope; and the fatigue index may include at least one of a first fatigue index, a second fatigue index, and an average fatigue index. The display method may be to display the corresponding data on the display of the terminal device. Taking the force data as an example, the words "the force data in the current time period is: 50 (kg)" may be displayed but not limited to. As for the division method of the display area and the display method, they can be flexibly adjusted according to the actual situation, and the embodiments of the present disclosure do not limit this.

[0088] The fatigue index can be displayed differently. For example, if the fatigue index corresponding to the current time period is determined to be less than or equal to a first index threshold, indicating that the target motor dysfunction patient is not experiencing obvious fatigue, the fatigue index is displayed in a first color. If the fatigue index corresponding to the current time period is determined to be greater than the first index threshold and less than or equal to a second index threshold, indicating that the target motor dysfunction patient is experiencing fatigue, the fatigue index is displayed in a second color. If the fatigue index corresponding to the current time period is determined to be greater than the second index threshold, indicating that the target motor dysfunction patient is experiencing obvious fatigue, the fatigue index is displayed in a third color. The first, second, and third colors are each different. For example, the first color can be green, the second color can be yellow, and the third color can be red. In this way, using different colors to display fatigue indexes in different ranges facilitates trainers to intuitively obtain the target motor dysfunction patient's fatigue level in the current time period, which can serve as a basis for trainers to dynamically adjust training volume.

[0089] The fatigue monitoring method provided by the embodiment of the present disclosure collects physiological indicators such as heart rate in real time, and communicates with a computer in real time via Bluetooth communication. The collected data is processed to obtain the heart rate slope and heart rate variability slope, and then the real-time fatigue index is calculated by a weight matching algorithm. The fatigue index can also be predicted by a machine learning algorithm, and these data are displayed for reference during training. Specifically, if the heart rate rising slope / heart rate falling slope slows down, that is, the heart rate change trend shows a downward trend, then it is determined that the target motor dysfunction patient has fatigue. In the weight matching algorithm, the heart rate weight corresponding to the heart rate slope is increased, which will cause the fatigue index to rise. If the heart rate variability begins to decline and the regulatory ability change trend shows a downward trend, then it is determined that the target motor dysfunction patient has fatigue. In the weight matching algorithm, the variability weight corresponding to the heart rate variability is increased, which will cause the fatigue index to rise accordingly, reflecting the increase in the current fatigue of the target motor dysfunction patient. In addition, hand force data can also be added to perform fatigue index calculations with more dimensions.

[0090] The fatigue monitoring method provided by the embodiment of the present disclosure has a simple physiological data collection method. By wearing a chest strap for collecting heart rate during training, data collection can be completed with one click, and the calculation of fatigue index can be realized according to the above steps. It has the characteristics of being easy to use and can be used to provide trainers with an objective reference direction without complicated training. Considering that the perception of fatigue of patients with motor dysfunction is significantly different from that of healthy people, subjective judgment is not sufficient, and objective facts such as physiological indicators need to be added for comprehensive evaluation. The fatigue index with effective reference properties obtained by this fatigue monitoring method can be used as an objective basis for adjusting the intensity of walking rehabilitation training for patients with motor dysfunction, and can ensure the safety of patients with motor dysfunction during rehabilitation training, so as to ensure the safe and effective advancement of training.

[0091] The embodiment of the present disclosure also provides a fatigue monitoring device for patients with motor dysfunction, comprising: an acquisition module for acquiring monitoring data of a target motor dysfunction patient in a current time period, wherein the monitoring data includes heart rate data and electrocardiogram data; a first calculation module for calculating a heart rate slope based on the heart rate data in the current time period, and calculating a heart rate variability based on the electrocardiogram data in the current time period, wherein the heart rate slope represents the rate at which the heart rate of the target motor dysfunction patient changes over time in the current time period, and the heart rate variability represents the autonomous movement of the target motor dysfunction patient in the current time period. The nervous system's ability to regulate heart rhythm; an adjustment module for adjusting the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, and adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability; a second calculation module for calculating a first fatigue index representing the fatigue level of the target motor dysfunction patient in the current time period based on the target calculation amount and target weight in the current time period, wherein the target calculation amount includes the heart rate slope and the heart rate variability, and the target weight includes the latest heart rate weight and the latest variability weight.

[0092] In one possible implementation, the historical heart rate weight is adjusted to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, including: if it is determined according to the heart rate slope that the heart rate change trend is a downward trend, then the historical heart rate weight after the first increment corresponding to the downward trend is increased as the latest heart rate weight; if it is determined according to the heart rate slope that the heart rate change trend is an upward trend, then the historical heart rate weight after the first decrement corresponding to the upward trend is reduced as the latest heart rate weight.

[0093] In one possible implementation, the historical variability weight is adjusted to the latest variability weight based on the weight adjustment rule and the heart rate variability, including: if it is determined according to the heart rate variability that the trend of change in the regulatory ability is a decreasing trend, the historical variability weight after the second increment corresponding to the decreasing trend is increased as the latest variability weight; if it is determined according to the heart rate variability that the trend of change in the regulatory ability is an increasing trend, the historical variability weight after the second decrement corresponding to the increasing trend is reduced as the latest variability weight; wherein the sum of the latest heart rate weight and the latest variability weight is less than or equal to a preset weight threshold.

[0094] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, including: determining a first value based on the heart rate slope and the latest heart rate weight, and determining a second value based on the heart rate variability and the latest variability weight; and determining the first fatigue index based on the first value and the second value.

[0095] In one possible implementation, the monitoring data also includes force data, and the force data represents the force applied by the target motor dysfunction patient on the rehabilitation training equipment during rehabilitation training; wherein, the device also includes a calculation adjustment module, which is used to: calculate the force slope based on the force data in the current time period, and the force slope represents the force change trend of the target motor dysfunction patient in the current time period; adjust the historical force weight to the latest force weight based on the weight adjustment rule and the force slope, the target calculation amount also includes the force slope, and the target weight also includes the latest force weight.

[0096] In one possible implementation, the historical force weight is adjusted to the latest force weight based on the weight adjustment rule and the force slope, including: if it is determined according to the force slope that the force change trend is a decreasing trend, then the historical force weight after the third reduction corresponding to the decreasing trend is reduced as the latest force weight; if it is determined according to the force slope that the force change trend is an increasing trend, then the historical force weight after the third increment corresponding to the increasing trend is increased as the latest force weight; wherein the sum of the latest heart rate weight, the latest variability weight, and the latest force weight is less than or equal to a preset weight threshold.

[0097] In one possible implementation, a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, and also includes: determining a third value based on the force slope and the latest force weight; determining the first fatigue index based on the first value, the second value, and the third value, wherein the first value is determined based on the heart rate slope and the latest heart rate weight, and the second value is determined based on the heart rate variability and the latest variability weight.

[0098] In one possible implementation, the device also includes a third calculation module, which is used to: input the monitoring data of the target motor dysfunction patient in the current time period into the first fatigue prediction model for calculation to obtain the second fatigue index of the target motor dysfunction patient in the current time period.

[0099] In one possible implementation, the device also includes a fourth calculation module, which is used to: input the target calculation amount and the fatigue index corresponding to the current time period into a second fatigue prediction model for calculation to obtain the predicted fatigue index of the target motor dysfunction patient in the future time period.

[0100] In one possible implementation, the device also includes a program determination module, which is used to perform at least one of the following: if it is determined that the fatigue index corresponding to the current time period is less than or equal to a preset first indicator threshold, it is determined that the rehabilitation training program for the target movement dysfunction patient remains unchanged; if it is determined that the fatigue index corresponding to the current time period is greater than the first indicator threshold and less than or equal to the second indicator threshold, it is determined to reduce the training intensity in the rehabilitation training program for the target movement dysfunction patient; if it is determined that the fatigue index corresponding to the current time period is greater than the second indicator threshold, it is determined to stop the rehabilitation training program for the target movement dysfunction patient; wherein, the first indicator threshold is less than the second indicator threshold.

[0101] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments. For the sake of brevity, it will not be repeated here.

[0102] An embodiment of the present disclosure further provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above method.

[0103] An embodiment of the present disclosure further provides a non-volatile computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0104] An embodiment of the present disclosure further provides a computer program product, including a computer program, or a non-volatile computer-readable storage medium carrying the computer program, wherein the computer program implements the steps of the above method when executed by a processor.

[0105] Figure 4 FIG1 shows a block diagram of a fatigue monitoring device for patients with motor dysfunction provided by an embodiment of the present disclosure. For example, the device 1900 can be provided as a server or a terminal device. Figure 4The apparatus 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions, such as an application, that can be executed by the processing component 1922. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute the instructions to perform the above-described method.

[0106] The device 1900 may also include a power supply component 1926 configured to perform power management of the device 1900, a wired or wireless network interface 1950 configured to connect the device 1900 to a network, and an input / output interface 1958 (I / O interface). The device 1900 may operate based on an operating system stored in the memory 1932, such as Windows Server 2003. TM , MacOS X TM , Unix TM ,Linux TM , FreeBSD TM or similar.

[0107] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by the processing component 1922 of the apparatus 1900 to perform the above-described method.

[0108] A computer-readable storage medium can be a tangible device that can hold and store programs / instructions used by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0109] The computer programs (or computer-readable program instructions) described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0110] The computer program (or computer program instructions) for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, by utilizing state information of computer-readable program instructions to personalize and customize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), the electronic circuit can execute the computer-readable program instructions to implement various aspects of the present disclosure.

[0111] Various aspects of the present disclosure are described herein with reference to flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions.

[0112] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device is generated that implements the functions / actions specified in one or more blocks in the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium, where these instructions cause the computer, programmable data processing device, and / or other device to operate in a specific manner. Thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing various aspects of the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0113] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more blocks in the flowchart and / or block diagram.

[0114] The flow charts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the prescribed logical function. In some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the prescribed function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0115] While various embodiments of the present disclosure have been described above, the foregoing description is intended to be illustrative, non-exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or technological improvements in the marketplace, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A fatigue monitoring method for patients with motor dysfunction, characterized in that: include: Acquiring monitoring data of a target motor dysfunction patient in a current time period, wherein the monitoring data includes heart rate data and electrocardiogram data; Calculating a heart rate slope based on the heart rate data within the current time period, and calculating a heart rate variability based on the electrocardiogram data within the current time period, wherein the heart rate slope represents a rate of change of the heart rate of the target movement dysfunction patient over time within the current time period, and the heart rate variability represents an ability of the autonomic nervous system of the target movement dysfunction patient to regulate the heart rhythm within the current time period; Adjusting the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, and adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability; A first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period is calculated based on the target calculation amount and target weight during the current time period, wherein the target calculation amount includes the heart rate slope and the heart rate variability, and the target weight includes the latest heart rate weight and the latest variability weight.

2. The method according to claim 1, characterized in that Adjusting the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope includes: If it is determined according to the heart rate slope that the heart rate change trend is a decreasing trend, the historical heart rate weight after adding a first increment corresponding to the decreasing trend is used as the latest heart rate weight; If it is determined according to the heart rate slope that the heart rate change trend is an upward trend, the historical heart rate weight after the first reduction corresponding to the upward trend is reduced and used as the latest heart rate weight.

3. The method according to claim 1, characterized in that Adjusting the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability includes: If it is determined based on the heart rate variability that the change trend of the regulatory ability is a decreasing trend, the historical variability weight after adding a second increment corresponding to the decreasing trend is used as the latest variability weight; If it is determined based on the heart rate variability that the change trend of the regulatory ability is an upward trend, the historical variability weight after the second reduction corresponding to the upward trend is reduced as the latest variability weight; The sum of the latest heart rate weight and the latest variability weight is less than or equal to a preset weight threshold.

4. The method according to claim 1, wherein Calculating a first fatigue index representing the fatigue level of the target movement dysfunction patient in the current time period based on the target calculation amount and the target weight in the current time period includes: determining a first value based on the heart rate slope and the latest heart rate weight, and determining a second value based on the heart rate variability and the latest variability weight; The first fatigue index is determined according to the first value and the second value.

5. The method according to any one of claims 1 to 3, characterized in that The monitoring data further includes force data, wherein the force data represents the force applied by the target motor dysfunction patient on the rehabilitation training device during rehabilitation training; The method further comprises: Calculating a force slope based on the force data in the current time period, wherein the force slope represents a trend of force changes of the target movement dysfunction patient in the current time period; The historical force weight is adjusted to the latest force weight based on the weight adjustment rule and the force slope, the target calculation amount also includes the force slope, and the target weight also includes the latest force weight.

6. The method according to claim 5, characterized in that Adjusting the historical force weight to the latest force weight based on the weight adjustment rule and the force slope includes: If it is determined according to the force slope that the force change trend is a decreasing trend, the historical force weight after the third decrement corresponding to the decreasing trend is used as the latest force weight; If it is determined based on the force slope that the force change trend is an upward trend, the historical force weight after adding the third increment corresponding to the upward trend is used as the latest force weight; The sum of the latest heart rate weight, the latest variability weight, and the latest force weight is less than or equal to a preset weight threshold.

7. The method according to claim 5, characterized in that Calculating a first fatigue index representing the fatigue level of the target movement dysfunction patient in the current time period based on the target calculation amount and the target weight in the current time period further includes: determining a third value according to the force slope and the latest force weight; The first fatigue index is determined based on the first value, the second value, and the third value, wherein the first value is determined based on the heart rate slope and the latest heart rate weight, and the second value is determined based on the heart rate variability and the latest variability weight.

8. The method according to any one of claims 1 to 4, characterized in that The method further comprises: The monitoring data of the target motor dysfunction patient in the current time period is input into a first fatigue prediction model for calculation to obtain a second fatigue index of the target motor dysfunction patient in the current time period.

9. The method according to any one of claims 1 to 4, characterized in that The method further comprises: The target calculation amount and the fatigue index corresponding to the current time period are input into a second fatigue prediction model for calculation to obtain a predicted fatigue index of the target motor dysfunction patient in a future time period.

10. The method according to any one of claims 1 to 4, characterized in that The method further comprises at least one of: If it is determined that the fatigue index corresponding to the current time period is less than or equal to a preset first index threshold, determining that the rehabilitation training program for the target motor dysfunction patient remains unchanged; If it is determined that the fatigue index corresponding to the current time period is greater than the first index threshold and less than or equal to the second index threshold, determining to reduce the training intensity in the rehabilitation training program for the target motor dysfunction patient; If it is determined that the fatigue index corresponding to the current time period is greater than a second index threshold, determining to stop the rehabilitation training program for the target motor dysfunction patient; The first indicator threshold is smaller than the second indicator threshold.

11. A fatigue monitoring device for patients with motor dysfunction, characterized in that: include: An acquisition module, configured to acquire monitoring data of a target motor dysfunction patient in a current time period, wherein the monitoring data includes heart rate data and electrocardiogram data; a first calculation module, configured to calculate a heart rate slope based on the heart rate data within the current time period, and to calculate a heart rate variability based on the electrocardiogram data within the current time period, wherein the heart rate slope represents a rate of change of the heart rate of the target movement dysfunction patient over time within the current time period, and the heart rate variability represents an ability of the autonomic nervous system of the target movement dysfunction patient to regulate the heart rhythm within the current time period; an adjustment module, configured to adjust the historical heart rate weight to the latest heart rate weight based on a preset weight adjustment rule and the heart rate slope, and to adjust the historical variability weight to the latest variability weight based on the weight adjustment rule and the heart rate variability; The second calculation module is used to calculate a first fatigue index representing the fatigue level of the target movement dysfunction patient during the current time period based on the target calculation amount and target weight during the current time period, wherein the target calculation amount includes the heart rate slope and the heart rate variability, and the target weight includes the latest heart rate weight and the latest variability weight.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory, wherein: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 10.

13. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

14. A computer program product comprising a computer program, or a non-volatile computer-readable storage medium carrying a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 10 are implemented.

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