An information determination method, apparatus, device, and computer readable storage medium
By acquiring motion and heart rate signals, and using wearable devices to collect target heart rate signals, combined with target parameters and attribute parameters, the objectivity and efficiency of ADHD assessment are achieved, solving the problems of low accuracy and poor user experience in existing technologies.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHINA MOBILE CHENGDU INFORMATION & TELECOMM TECH CO LTD
- Filing Date
- 2023-12-01
- Publication Date
- 2026-07-10
AI Technical Summary
Among existing ADHD assessment methods, those based on patient subjective descriptions have low accuracy, and EEG assessment equipment is inconvenient to use and costly, resulting in poor user experience and low efficiency.
By acquiring motion and heart rate signals, wearable devices are used to collect target heart rate signals of the subject under different states. Combined with target parameters and attribute parameters, objective neurophysiological indicators are used for evaluation to avoid subjective influence.
It improves the accuracy and efficiency of ADHD assessment, reduces usage costs, and enhances the patient experience and convenience of testing.
Smart Images
Figure CN120078413B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of attention deficit hyperactivity disorder (ADHD) assessment technology, and in particular to an information determination method, apparatus, device, and computer-readable storage medium. Background Technology
[0002] Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that begins in childhood and is characterized by disproportionate attention deficit, hyperactivity, and impulsivity. Symptoms persist in 60%–80% of affected children into adolescence, and in 50% into adulthood. Current clinical and basic research indicates that ADHD is a prevalent, long-term public health problem that causes serious and lasting damage to patients' social functioning and mental health. Long-term follow-up and assessment of patients are necessary, along with interventions based on disease progression to achieve comprehensive management throughout the entire course of the disease.
[0003] Currently, there are two main methods for assessing ADHD: Method 1 involves clinicians obtaining assessments based on patients' subjective descriptions of their behavior; Method 2 involves assessing ADHD based on electroencephalogram (EEG) signals. However, Method 1 is task-driven active monitoring, so the assessment results are influenced by the patient's subjectivity, leading to lower accuracy in assessing the patient's ADHD condition. Method 2 requires EEG acquisition devices that need to be fixed in place with helmets, causing discomfort and pressure on the patient's head and neck, resulting in a poor patient experience and lower testing efficiency. Summary of the Invention
[0004] To address the aforementioned technical problems, embodiments of this application aim to provide an information determination method, apparatus, device, and computer-readable storage medium that can solve the problems of low accuracy in assessing patients' ADHD conditions, poor patient experience, and low detection efficiency in related technologies.
[0005] The technical solution of this application is implemented as follows:
[0006] An information determination method, the method comprising:
[0007] Acquire the motion signal and heart rate signal of the object to be detected, and acquire the reference heart rate signal of the object to be detected in the target state;
[0008] Based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal, the target heart rate signal of the object to be detected in different states is determined;
[0009] Determine target parameters for each of the target heart rate signals; wherein the target parameters are used to characterize the mental state of the subject to be detected;
[0010] Obtain the target attribute parameters of the object to be detected, and determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object.
[0011] In the above scheme, determining the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal includes:
[0012] Based on motion signals, a first target heart rate signal of the object to be detected in motion state is determined from the heart rate signal to be processed;
[0013] Based on the reference heart rate signal, a second target heart rate signal for the subject in a resting state and a third target heart rate signal in a focused state are determined from the remaining heart rate signal; wherein, the remaining heart rate signal is the signal in the heart rate signal to be processed other than the first target heart rate signal; the resting state represents the mental relaxation of the subject; the focused state represents the mental tension of the subject.
[0014] In the above scheme, determining the first target heart rate signal of the object under motion from the heart rate signal to be processed based on the motion signal includes:
[0015] Acquire multiple motion parameters of the motion signal;
[0016] For each of the motion parameters, when it is determined that the object to be detected is in motion based on the motion parameters, the motion time of the object to be detected in the motion state is determined;
[0017] From the heart rate signal to be processed, determine the sub-heart rate signal corresponding to each exercise time;
[0018] The first target heart rate signal is determined based on multiple sub-heart rate signals.
[0019] In the above scheme, determining the second target heart rate signal of the subject in the resting state and the third target heart rate signal in the focused state from the remaining heart rate signal based on the reference heart rate signal includes:
[0020] The remaining heart rate signal is classified using a target classification model to obtain a first type of heart rate signal and a second type of heart rate signal; wherein, the target classification model is obtained by training an initial classification model based on the sample parameters of the reference heart rate signal;
[0021] The second target heart rate signal is determined based on the first type of heart rate signal, and the third target heart rate signal is determined based on the second type of heart rate signal.
[0022] In the above scheme, determining the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object includes:
[0023] Based on the target attribute parameters, multiple target parameters, and the sample attribute parameters, the first evaluation parameter of the object to be detected is determined;
[0024] A second evaluation parameter for the object to be detected is determined based on each of the first evaluation parameters and the weight of each of the first evaluation parameters; wherein the evaluation parameter includes the first evaluation parameter and the second evaluation parameter.
[0025] In the above scheme, determining the first evaluation parameter of the object to be detected based on the target attribute parameter, multiple target parameters, and the sample attribute parameter includes:
[0026] From the sample attribute parameters, determine the matching attribute parameters that match the target attribute parameters;
[0027] The first and second values are determined based on the matching attribute parameters;
[0028] The first evaluation parameter is determined based on the first value, the second value, and each of the target parameters.
[0029] In the above scheme, determining the first evaluation parameter of the object to be detected based on the target attribute parameter, multiple target parameters, and the sample attribute parameter includes:
[0030] Based on each of the target parameters, the target attribute parameters, and the calibration model, the calibrated target parameters are determined; wherein the calibration model is determined based on the baseline attribute parameters.
[0031] The third and fourth values are determined based on the aforementioned baseline attribute parameters;
[0032] The first evaluation parameter is determined based on the third value, the fourth value, and each of the calibrated feature parameters.
[0033] An information determining device, the device comprising:
[0034] The acquisition unit is used to acquire the motion signal and heart rate signal of the object to be detected, and to acquire the reference heart rate signal of the object to be detected in the target state;
[0035] The first processing unit is used to determine the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal.
[0036] A determining unit is configured to determine target parameters for each of the target heart rate signals; wherein the target parameters are used to characterize the mental state of the subject to be detected;
[0037] The second processing unit is used to obtain the target attribute parameters of the object to be detected, and to determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters and sample attribute parameters of the sample object.
[0038] An information determining device, the device comprising: a processor, a memory, and a communication bus;
[0039] The communication bus is used to realize the communication connection between the processor and the memory;
[0040] The processor is used to execute the information determination program stored in the memory to implement the steps of the above-described information determination method.
[0041] A computer-readable storage medium storing one or more programs that can be executed by one or more processors to perform the steps of the information determination method described above.
[0042] The information determination method, apparatus, device, and computer-readable storage medium provided in this application first acquire the motion signal and heart rate signal to be processed of the object to be detected, and acquire the reference heart rate signal of the object to be detected in the target state. Then, based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal, the target heart rate signal of the object to be detected in different states is determined. Next, the target parameters of each target heart rate signal are determined, and the target parameters are used to characterize the mental state of the object to be detected. Then, the target attribute parameters of the object to be detected are acquired, and based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object, the evaluation parameters of the object to be detected are determined. In this way, by actively extracting the target heart rate signal, the object to be detected can achieve the desired mental state. The system detects the heart rate signal to be processed from the target heart rate signal under different states, and then extracts the target parameters of the target heart rate signal. Based on the target parameters, disease assessment is performed. This is a disease assessment based on objective neurophysiological indicators, rather than task-driven active monitoring. Therefore, it is not affected by subjective factors, unlike related technologies that assess disease based on subjective descriptions of patient behavior. This improves the objectivity and accuracy of the assessment results. Furthermore, motion signals, the heart rate signal to be processed, and the reference heart rate signal can all be acquired using wearable devices, which are not only low-cost but also comfortable to wear, thus improving the experience of the subject being tested and the efficiency of the test. Attached Figure Description
[0043] Figure 1 A flowchart illustrating an information determination method provided in an embodiment of this application;
[0044] Figure 2 A flowchart illustrating another information determination method provided in an embodiment of this application;
[0045] Figure 3 This is a schematic diagram of the structure of an information determination device provided in an embodiment of this application;
[0046] Figure 4 This is a schematic diagram of the structure of an information determination device provided in an embodiment of this application. Detailed Implementation
[0047] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.
[0048] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.
[0049] Currently, the main method for assessing ADHD is for clinicians to obtain an assessment of ADHD based on the subjective descriptions of the patient's (i.e., the subject of the test) behavior (i.e., clinical ADHD assessment method). Assessment methods include medical clinical observation, examination-style interviews, and clinical scales. However, this method has limitations, such as potential misunderstandings of the questions by the subjects and state recall bias. Furthermore, the test relies on the subjects' subjective reports and self-evaluations, and is therefore affected by the subjects' subjectivity. As a result, this method is not only highly subjective and lacks objectivity, but is also time-consuming, complex to score, and requires a certain level of cognitive ability from the subjects and a certain level of professionalism from the assessing physicians.
[0050] In recent years, ADHD assessment based on EEG signals has become a hot research topic in scientific research. EEG is a technique that measures brain activity by collecting electrical activity on the scalp using electrodes. EEG can provide temporal and spatial information about brain activity, thus more accurately reflecting the neurophysiological characteristics and severity of ADHD patients. Compared to clinical ADHD assessment methods, it can provide objective and quantitative assessment indicators, without relying on the subject's subjective reports and self-evaluations, thereby avoiding subjective misjudgments and biases. Furthermore, EEG can provide more in-depth information on neurophysiological mechanisms and pathophysiological basis.
[0051] Specifically, the protocol for ADHD assessment using EEG technology is as follows: Brain stimulation tasks are programmed for the subjects, played to them, and their responses are stimulated and recorded. The signals are then processed and analyzed to extract waveform features and calculate scores for each task. Finally, based on the subjects' basic information, a weighted average of the task scores is calculated to obtain a total score, which is used to assess the severity of ADHD symptoms.
[0052] However, the method of using EEG technology for clinical ADHD assessment has the following problems: 1. Low efficiency of active monitoring assessment: This assessment is based on task-driven active monitoring. Completing the test task requires the time and energy of the subject and the patient's full participation and cooperation. However, the subject's attention and concentration are usually difficult to maintain for a long time, which will affect the patient's compliance and the completion of the test process; 2. Wearable devices are complex in structure and expensive, with low technical universality and usability: EEG acquisition devices require the use of multiple sets of electrodes and amplifiers to collect and amplify brain signals. Usually, helmets and other fixation devices are also required, which will cause some discomfort and pressure on the subject's head and neck. Moreover, the cost of purchasing such devices is high, and EEG signal acquisition devices are usually not available in homes. In addition, EEG devices are usually bulky and complex in appearance, which also has certain limitations in terms of movement and portability.
[0053] Based on this, embodiments of this application provide an information determination method, which can be applied to an information determination device, as described above. Figure 1 As shown, the method includes the following steps:
[0054] Step 101: Obtain the motion signal and heart rate signal of the object to be detected, and obtain the reference heart rate signal of the object to be detected in the target state.
[0055] Specifically, the system can continuously acquire motion signals and heart rate signals of the subject within a detection cycle. Motion signals reflect the subject's physical activity state. Heart rate signals and reference heart rate signals refer to the degree of change in heartbeat intervals and can reflect the activity state of the subject's autonomic nervous system. Heart rate signals are signals continuously acquired from the subject throughout the entire detection cycle, while reference heart rate signals are signals acquired when the subject is in a target state. Target states can include focused states and resting states, so the reference heart rate signals for the target state can include reference heart rate signals for both resting and focused states.
[0056] In this embodiment, a wearable motion signal acquisition device can be used to continuously acquire the motion signal of the subject under test, and wearable heart rate acquisition technology can be used to continuously acquire the heart rate signal to be processed and the reference heart rate signal of the subject under test. It should be noted that when acquiring the reference heart rate signal of the target state, the subject under test can first be placed in the target state, and then the heart rate acquisition technology can be used to continuously acquire the signal for a period of time. During the acquisition of the reference heart rate signal, it is necessary to ensure the stability and quietness of the environment to avoid the influence of external interference on the data. In this way, by monitoring the heart rate signal of the subject under test in real time and continuously, the physical and mental state of the subject under test can be reflected in real time, and the severity of the subject's condition can be assessed accordingly. This helps doctors to more accurately judge the condition of the subject under test, formulate more scientific treatment plans, and improve the treatment effect. Moreover, wearable devices are not only highly comfortable, portable and lightweight, low in cost, and easy to obtain (i.e., the technology is also highly universal and usable), but also highly compatible with common devices such as fitness trackers, and can be well integrated into daily life.
[0057] In one feasible implementation, the heart rate signal to be processed can refer to the heart rate variability (HRV) signal, the reference heart rate signal at rest can refer to the HRV signal at rest, and the reference heart rate signal at a focused state can refer to the HRV signal at a focused state; the detection period can be one day, so as to track the daily changes in the condition of the subject under test; the time period can refer to 30 seconds to 5 minutes, and usually 2 minutes.
[0058] In one feasible implementation, the motion signal acquisition device can be a motion sensor, gyroscope, or Global Positioning System (GPS); the heart rate acquisition technology can be electrocardiogram (ECG) or photoplethysmography (PPG). ECG is a technology that converts electrocardiographic signals into digital signals using electrodes. Its basic principle is to use the electrical signals generated by the heart to measure heart rhythm and rate and calculate HRV. PPG is a technology that converts blood flow into digital signals using sensors. Its basic principle is to use photoelectric sensors to detect changes in the intensity of reflected light on the skin surface to monitor heart rate and calculate HRV. Compared with ECG, PPG has advantages such as small size and low power consumption. However, because the signals acquired by PPG are affected by environmental and individual differences, its accuracy is slightly lower than that of ECG.
[0059] Step 102: Based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal, determine the target heart rate signal of the object to be detected in different states.
[0060] In this embodiment, different states may include an exercise state, a focused state, and a resting state; the target heart rate signal for different states may refer to the target heart rate signal for the exercise state, the target heart rate signal for the focused state, and the target heart rate signal for the resting state. The target heart rate signal for the exercise state may refer to the heart rate signal containing only the heart rate signal of the subject being exercised, the target heart rate signal for the focused state may refer to the heart rate signal containing only the heart rate signal of the subject being focused, and the target heart rate signal for the resting state may refer to the heart rate signal containing only the heart rate signal of the subject being rested. The heart rate signal to be processed can be divided based on the exercise signal, the reference heart rate signal for the focused state, and the reference heart rate signal for the resting state to obtain the target heart rate signal for different states.
[0061] Step 103: Determine the target parameters for each target heart rate signal.
[0062] The target parameter is used to characterize the mental state of the object to be detected.
[0063] In the embodiments of this application, for each target heart rate signal, each target heart rate signal can be analyzed from different angles to obtain multiple parameters under each analysis angle, and multiple target parameters can be determined from the multiple parameters under multiple analysis angles.
[0064] In one feasible implementation, the root mean square of successive differences (RMSSD) of the squared difference between two adjacent R waves (RR interval) in the time domain parameters, and the low frequency (LF) and high frequency (HF) parameters in the frequency domain parameters can be extracted from the target heart rate signal of each state of the object to be detected. This yields target parameters for nine target heart rate signals, which can be denoted as feature group A. It should be noted that the frequency domain parameters refer to a series of frequency domain features obtained by analyzing the power spectral density distribution of the target heart rate signal, while the time domain parameters refer to a series of time domain features obtained by statistically analyzing the heart rate interval sequence of the target heart rate signal. Furthermore, in the frequency domain parameters, LF represents the power of the low-frequency component (0.04–0.15 Hz), HF represents the power of the high-frequency component (0.15–0.4 Hz), and RMSSD represents the root mean square of the difference between two adjacent N waves (NN interval).
[0065] Step 104: Obtain the target attribute parameters of the object to be detected, and determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object.
[0066] Among them, the target attribute parameter can refer to the basic information of the object to be tested, and the sample attribute parameter can refer to the basic information of the sample object. The target attribute parameter and the sample attribute information can specifically include age, gender and body mass index (BMI), etc.; the assessment parameter can characterize the severity of the disease of the object to be tested and is a specific numerical value.
[0067] In this embodiment, the target attribute parameters and sample attribute parameters can be processed first, and then processed with multiple target parameters to obtain a specific numerical value of the disease status of the subject to be tested. Since the final determined assessment parameter for the subject to be tested is a specific numerical value, the output is a quantitative patient condition assessment indicator rather than a grading result, allowing for longitudinal comparison to reflect the patient's disease progression trend. It should be noted that HRV can vary among different subjects; factors such as age, gender, and BMI all affect HRV. For example, HRV generally decreases with age, women often have a higher HRV than men, and people with high BMI often have a lower HRV than those with low BMI. Therefore, when assessing the HRV of the subject to be tested, factors such as age, gender, and BMI need to be considered to achieve accurate assessment of the subject's ADHD symptoms.
[0068] It should be noted that, compared with related technologies, this solution does not require active task-driven actions from the subject of the test. The entire testing cycle is automatic passive monitoring, not task-driven active monitoring. The subject of the test only performs simple information entry and reference heart rate signal collection operations, which does not require too much time and energy from the subject of the test, the guardian, and the medical staff, and can also better ensure the subject of the test's compliance.
[0069] The information determination method provided in this application actively extracts the heart rate signal to be processed from the subject to be tested, and then extracts the target parameters of the target heart rate signal under different states. Based on the target parameters, disease assessment is performed. That is, disease assessment is based on objective neurophysiological indicators, rather than task-driven active monitoring. Therefore, it is not affected by subjective factors, unlike related technologies that assess disease based on subjective descriptions of patient behavior. This improves the objectivity and accuracy of the assessment results. Moreover, the motion signal, the heart rate signal to be processed, and the reference heart rate signal can all be acquired using wearable devices, which are not only low in cost but also highly comfortable to wear, thereby improving the experience of the subject to be tested and the efficiency of the test.
[0070] Based on the foregoing embodiments, another information determination method provided in this application embodiment is referred to... Figure 2 As shown, the method may include the following steps:
[0071] Step 201: The information determination device acquires the motion signal and heart rate signal of the object to be detected, and acquires the reference heart rate signal of the object to be detected in the target state.
[0072] Step 202: The information determination device determines the first target heart rate signal of the object under motion from the heart rate signal to be processed based on the motion signal.
[0073] In this embodiment of the application, the first target heart rate signal may refer to the heart rate signal of the object to be detected in motion; the sub-heart rate signal of the object to be detected in motion may be extracted from the heart rate signal to be processed based on the motion signal, and then the first target heart rate signal may be obtained based on the sub-heart rate signal.
[0074] It should be noted that step 202 can be achieved in the following way:
[0075] Step 202A1: The information determination device acquires multiple motion parameters of the motion signal.
[0076] In the embodiments of this application, motion parameters can refer to parameters in the motion signal that characterize the motion state of the object to be detected. Within one detection cycle, the object to be detected can be in motion multiple times, thus acquiring multiple motion parameters. In one feasible implementation, when the motion signal is acquired through an accelerometer worn on the wrist of the object to be detected, the motion parameter can be acceleration. It should be noted that, in order to avoid misjudgment caused by external interference, the acceleration information read by the accelerometer can be filtered (e.g., Kalman filtering algorithm and moving average filtering algorithm can be used) and smoothed to reduce noise and interference, thereby improving the detection accuracy of motion state.
[0077] Step 202A2: For each motion parameter, the information determination device determines the motion time of the object under test in motion state based on the motion parameters.
[0078] In this embodiment, motion time can refer to the time period during which the object to be detected is in motion; each motion parameter can be compared with a preset threshold, and when it is determined that the object to be detected is in motion based on the motion parameters and the preset threshold, the time period during which the object to be detected is in motion is extracted; in one feasible implementation, when the motion parameter is acceleration, when the acceleration exceeds the preset threshold, it can be determined that the object to be detected is in motion.
[0079] Step 202A3: The information determination device determines the sub-heart rate signal corresponding to each exercise time from the heart rate signal to be processed.
[0080] In this embodiment of the application, the sub-heart rate signal can refer to a segment of heart rate signal in motion extracted from the heart rate signal to be processed; specifically, the heart rate signal to be processed and the motion signal can be aligned at each motion time to cut out the segment of heart rate signal in motion.
[0081] Step 202A4: The information determination device determines the first target heart rate signal based on multiple sub-heart rate signals.
[0082] In this embodiment of the application, multiple sub-heart rate signals of the object to be detected can be spliced and recombined to obtain a first target heart rate signal.
[0083] Step 203: The information determination device determines the second target heart rate signal of the subject in the resting state and the third target heart rate signal in the focused state from the remaining heart rate signals based on the reference heart rate signal.
[0084] Among them, the residual heart rate signal is the heart rate signal to be processed other than the first target heart rate signal; the resting state represents the mental relaxation of the subject to be tested; and the focused state represents the mental tension of the subject to be tested.
[0085] In this embodiment, the second target heart rate signal can refer to the heart rate signal acquired when the subject is in a resting state; the third target heart rate signal can refer to the heart rate signal acquired when the subject is in a focused state; the second target heart rate signal in the resting state and the third target heart rate signal in the focused state can be determined from the remaining heart rate signals based on the reference heart rate signal in the resting state and the reference heart rate signal in the focused state. In one feasible implementation, the resting state can refer to the state in which the subject is resting quietly with its eyes open; the focused state can refer to the state in which the subject is performing a certain attention test or task (such as digit memorization and connecting lines).
[0086] It should be noted that step 203 can be achieved in the following way:
[0087] Step 203B1: The information determination device uses a target classification model to classify the remaining heart rate signals to obtain the first type of heart rate signal and the second type of heart rate signal.
[0088] The target classification model is obtained by training the initial classification model based on the sample parameters of the reference heart rate signal.
[0089] In this embodiment, the first type of heart rate signal may refer to multiple sub-heart rate signals in a resting state; the second type of heart rate signal may refer to multiple sub-heart rate signals in a focused state; the remaining heart rate signal can be segmented into segments of a certain time length, and the parameters corresponding to the sample parameters of each segment can be input into the target classification model to obtain the first type of heart rate signal and the second type of heart rate signal; the sample parameters of the reference heart rate signal can be determined first, and then the sample parameters can be input into the initial classification model for model training to obtain the target classification model.
[0090] In one feasible implementation, the sample parameters can refer to multiple time-domain parameters, multiple frequency-domain parameters, and multiple nonlinear parameters of the extracted reference heart rate signals in the resting state and the focused state. A set of parameters that can distinguish the reference heart rate signals in the resting state and the focused state is selected from the multiple time-domain parameters, multiple frequency-domain parameters, and multiple nonlinear parameters as the sample parameters, denoted as feature group B; specifically, feature group B includes RMSSD in the time-domain parameters and LF and HF in the frequency-domain parameters.
[0091] It should be noted that nonlinear parameters refer to a series of nonlinear characteristics obtained through nonlinear analysis of HRV signals. These nonlinear parameters include, but are not limited to: complexity parameters (C0 complexity, C1 complexity, C2 complexity), entropy parameters (sample entropy, approximate entropy, fuzzy entropy), etc. The complexity parameter reflects the rate at which new patterns appear in the heart rate RR interval time series as its length increases. It is defined as the proportion of random components in the time series; the higher the value, the higher the proportion of random components in the sequence, i.e., the higher the complexity of the autonomic nervous system of the subject under test, and vice versa. The entropy parameter represents the regularity of the heart rate RR interval time series. The lower the entropy value, the more predictable and regular the RR interval time series signal is; the higher the entropy value, the more random and unpredictable the RR interval time series is. Time-domain parameters may also include, but are not limited to: mean heart rate (MEAN_HR), maximum heart rate (MAX_HR), minimum heart rate (MIN_HR), and the standard deviation of N-to-N intervals. Intervals (SDNN), RMSSD, the percentage of successive NN intervals that differ by more than 50ms (PNN50), etc.; frequency domain parameters include but are not limited to: LF, HF, very low frequency (VLF), the ratio of low frequency components to high frequency components (i.e., LF / HF), etc., where VLF is the power of the very low frequency component from 0.0033 to 0.04 Hz.
[0092] It's important to note that the characteristic values (i.e., parameters) of HRV (Human Threat Value) will exhibit different changes under different states of motion, focus, and rest, reflecting the activity level and balance of the autonomic nervous system in different states. During motion, due to increased physical activity and metabolism, sympathetic activity in the autonomic nervous system increases while parasympathetic activity decreases, leading to a decrease in HRV. Specifically, HRV characteristic values reflecting parasympathetic activity, such as RMSSD and HF, will decrease, while HRV characteristic values reflecting sympathetic activity, such as LF and LF / HF, will increase. Under focus, activity in the cerebral cortex increases, leading to increased sympathetic activity in the autonomic nervous system and decreased parasympathetic activity, thus resulting in a decrease in HRV. Under rest, the body is relaxed, leading to increased parasympathetic activity and decreased sympathetic activity in the autonomic nervous system, thus increasing HRV.
[0093] In one feasible implementation, if the parameters corresponding to feature group B for each segment of the remaining heart rate signal are input into the target classification model, the target classification model can determine whether the segment is a resting segment (i.e., a sub-heart rate signal in the resting state), a focused segment (i.e., a sub-heart rate signal in the focused state), or a segment in other states by comparing it with feature group B. Specifically, when feature group B is selected as LF, HF, and RMSSD, the LF, HF, and RMSSD of the reference heart rate signal can be calculated first, and the maximum allowable difference for each parameter can be set, such as LF = 7 for the resting state. If the maximum allowable difference is 10ms², then the range for determining the residual heart rate (LF) is 60-80ms². Next, the characteristic group B of the residual heart rate signal is calculated. Each parameter in characteristic group B is compared with the determination range of characteristic groups B for resting and focused states. If a parameter in characteristic group B falls within the determination range for resting state, the segment is considered to be in a resting state; if a parameter in characteristic group B falls within the determination range for focused state, the segment is considered to be in a focused state; if a parameter in characteristic group B falls within neither the determination range for resting nor focused state, the segment is considered to be in another state.
[0094] Step 203B2: The information determining device determines the second target heart rate signal based on the first type of heart rate signal, and determines the third target heart rate signal based on the second type of heart rate signal.
[0095] In this embodiment of the application, multiple sub-heart rate signals in the first type of heart rate signal can be spliced and recombined to obtain a second target heart rate signal, and multiple sub-heart rate signals in the second type of heart rate signal can be spliced and recombined to obtain a third target heart rate signal.
[0096] Step 204: The information determination device determines the target parameters for each target heart rate signal.
[0097] The target parameter is used to characterize the mental state of the object to be detected.
[0098] Step 205: The information determination device acquires the target attribute parameters of the object to be detected.
[0099] Step 206: The information determination device determines the first evaluation parameter of the object to be tested based on the target attribute parameter, multiple target parameters and sample attribute parameters.
[0100] In the embodiments of this application, the first evaluation parameter can characterize the relative position of the symptom severity of the subject to be tested in the population; the target attribute parameters and sample attribute parameters can be processed first, and then the first evaluation parameter of the subject to be tested can be determined by multiple target parameters.
[0101] It should be noted that step 206 can be achieved through steps 206C1 to 206C3;
[0102] Step 206C1: The information determination device determines the matching attribute parameter that matches the target attribute parameter from the sample attribute parameters.
[0103] It should be noted that sufficient HRV data of normal individuals can be collected from different groups (age, gender, and BMI) and states (resting, focused, and active). The collected HRV data can then be grouped according to age, gender, and BMI, and the mean and standard deviation (i.e., norm) of the sample parameters in each normal group can be calculated.
[0104] In this embodiment, the matching attribute parameter can refer to the parameter in the sample attribute parameters that matches the target attribute parameter. In one feasible implementation, when the target attribute parameter is male, 30 years old, BMI=22, it is necessary to find the parameter "male, 30 years old, BMI=22" from the sample attribute parameters as the matching attribute parameter.
[0105] Step 206C2: The information determination device determines the first and second values based on the matching attribute parameters.
[0106] In this embodiment of the application, the first value may refer to the average value of each sample parameter corresponding to the matching attribute parameter, and the second value may refer to the standard deviation of each sample parameter corresponding to the matching attribute parameter. In step 206C1, the first value and the second value of each sample parameter in each group of normal people have been calculated. Then, from the multiple calculated first values and second values, the first value and the second value of each sample parameter of the matching attribute parameter are determined.
[0107] Step 206C3: The information determination device determines the first evaluation parameter based on the first value, the second value, and each target parameter.
[0108] In this embodiment, the first evaluation parameter can characterize the difference score between each target parameter of the object to be detected and the sample parameter of the matching attribute parameter; for each target parameter, the first evaluation parameter can be obtained by performing calculations on the first value, the second value and the target parameter; specifically, the first evaluation parameter can be calculated by the expression 50+10*(target parameter-first value) / second value.
[0109] It should be noted that when the first value refers to the mean and the second value refers to the standard deviation, the above expression makes the mean of the first evaluation parameter 50 and the standard deviation 10. Then, people whose target parameter is higher than the mean of the feature parameters in the norm will get a first evaluation parameter higher than 50, and vice versa. In this way, the relative position of the symptom severity of the person being tested in the population can be intuitively understood through the first evaluation parameter.
[0110] It should be noted that step 206 can be achieved through steps 206D1 to 206D3;
[0111] Step 206D1: The information determination device determines the calibrated target parameters based on each target parameter, target attribute parameter, and calibration model.
[0112] The calibration model is determined based on the baseline attribute parameters.
[0113] In the embodiments of this application, the purpose of calibrating the model is to remove the influence of individual attribute parameters (such as age, gender, and BMI) on the target parameters and unify the target parameters to a standard norm. In one feasible implementation, the standard norm can be male, 30 years old, and BMI = 22.
[0114] In this embodiment, algorithms such as linear regression, multiple regression, support vector machine, random forest, and neural network can be used to establish a calibration model based on collected norm data (i.e., sufficient HRV data of normal people). Each target parameter and target attribute parameter of the object to be detected is input into the calibration model, and the corrected feature values (i.e., target parameters) under the standard norm can be output.
[0115] Step 206D2: The information determination device determines the third and fourth values based on the baseline attribute parameters.
[0116] In this embodiment of the application, if the first and second values of each sample parameter in each group of normal people have been calculated, then the third and fourth values of each sample parameter of the baseline attribute parameter are determined from the calculated first and second values.
[0117] Step 206D3: The information determination device determines the first evaluation parameter based on the third value, the fourth value, and each calibrated characteristic parameter.
[0118] In the embodiments of this application, for each calibrated feature parameter, the third value, the fourth value, and the calibrated feature parameter can be calculated to obtain the first evaluation parameter; specifically, the first evaluation parameter can be calculated using the expression 50+10*(calibrated feature parameter-third value) / fourth value.
[0119] Step 207: The information determination device determines the second evaluation parameters of the object to be detected based on each first evaluation parameter and the weight of each first evaluation parameter.
[0120] The evaluation parameters include a first evaluation parameter and a second evaluation parameter.
[0121] In this embodiment of the application, in order to integrate the difference scores of various feature parameters (i.e., the first evaluation parameter), an appropriate weight can be assigned to each first evaluation parameter feature. The weight can be used to reflect the importance of different feature parameters to ADHD performance, and the weight can be determined based on historical experience. The first evaluation parameter determined by more significant or key feature parameters may be assigned a higher weight. The second evaluation parameter can refer to the comprehensive score of the ADHD performance of the subject to be tested. The second evaluation parameter takes into account the differences of various feature parameters and can reflect the severity of the ADHD symptoms of the subject to be tested.
[0122] In this embodiment, each first evaluation parameter can be multiplied by its weight to obtain multiple results. These results are then added together to obtain the second evaluation parameter. By integrating the difference scores of multiple HRV feature parameters into a single comprehensive score, the contribution of each feature parameter to ADHD is considered comprehensively. This avoids relying solely on a single, subjective feature for evaluation, allowing for a more objective and comprehensive understanding of the ADHD performance of the subject. Furthermore, the second evaluation parameter can intuitively reflect the degree of difference between the subject and the sample (i.e., normal individuals), indicating the severity of ADHD and providing a quantitative indicator for assessing and monitoring disease progression.
[0123] It should be noted that the descriptions of the same steps and contents as in other embodiments in this embodiment can be found in the descriptions in other embodiments, and will not be repeated here.
[0124] The information determination method provided in this application actively extracts the heart rate signal to be processed from the subject to be tested, and then extracts the target parameters of the target heart rate signal under different states. Based on the target parameters, disease assessment is performed. That is, disease assessment is based on objective neurophysiological indicators, rather than task-driven active monitoring. Therefore, it is not affected by subjective factors, unlike related technologies that assess disease based on subjective descriptions of patient behavior. This improves the objectivity and accuracy of the assessment results. Moreover, the motion signal, the heart rate signal to be processed, and the reference heart rate signal can all be acquired using wearable devices, which are not only low in cost but also highly comfortable to wear, thereby improving the experience of the subject to be tested and the efficiency of the test.
[0125] Based on the foregoing embodiments, this application provides an information determining device, which can be applied to... Figure 1 and Figure 2 In the information determination method provided in the corresponding embodiment, refer to Figure 3As shown, the information determining device 3 may include: an acquisition unit 31, a first processing unit 32, a determining unit 33, and a second processing unit 34, wherein:
[0126] The acquisition unit 31 is used to acquire the motion signal and heart rate signal of the object to be detected, and to acquire the reference heart rate signal of the object to be detected in the target state;
[0127] The first processing unit 32 is used to determine the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed and the reference heart rate signal.
[0128] The determining unit 33 is used to determine the target parameters for each target heart rate signal; wherein, the target parameters are used to characterize the mental state of the subject to be detected.
[0129] The second processing unit 34 is used to obtain the target attribute parameters of the object to be detected, and to determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters and sample attribute parameters of the sample object.
[0130] In other embodiments of this application, the first processing unit 32 is further configured to perform the following steps:
[0131] Based on motion signals, the first target heart rate signal of the object under motion is determined from the heart rate signal to be processed;
[0132] Based on the reference heart rate signal, the second target heart rate signal of the subject in the resting state and the third target heart rate signal in the focused state are determined from the remaining heart rate signal; wherein, the remaining heart rate signal is the signal in the heart rate signal to be processed other than the first target heart rate signal; the resting state represents the mental relaxation of the subject; the focused state represents the mental tension of the subject.
[0133] In other embodiments of this application, the first processing unit 32 is further configured to perform the following steps:
[0134] Acquire multiple motion parameters of the motion signal;
[0135] For each motion parameter, when it is determined that the object to be detected is in motion based on the motion parameter, the motion time of the object in motion is determined.
[0136] From the heart rate signal to be processed, determine the sub-heart rate signal corresponding to each exercise time;
[0137] The first target heart rate signal is determined based on multiple sub-heart rate signals.
[0138] In other embodiments of this application, the first processing unit 32 is further configured to perform the following steps:
[0139] The remaining heart rate signals are classified using a target classification model to obtain a first type of heart rate signal and a second type of heart rate signal; the target classification model is obtained by training an initial classification model based on the sample parameters of the reference heart rate signal.
[0140] A second target heart rate signal is determined based on the first type of heart rate signal, and a third target heart rate signal is determined based on the second type of heart rate signal.
[0141] In other embodiments of this application, the second processing unit 34 is further configured to perform the following steps:
[0142] Based on the target attribute parameters, multiple target parameters, and sample attribute parameters, the first evaluation parameter of the object to be detected is determined.
[0143] Based on each first evaluation parameter and its weight, a second evaluation parameter for the object to be detected is determined; wherein the evaluation parameter includes the first evaluation parameter and the second evaluation parameter.
[0144] In other embodiments of this application, the second processing unit 34 is further configured to perform the following steps:
[0145] From the sample attribute parameters, determine the matching attribute parameters that match the target attribute parameters;
[0146] The first and second values are determined based on the matching attribute parameters;
[0147] Based on the first value, the second value, and each target parameter, the first evaluation parameter is determined.
[0148] In other embodiments of this application, the second processing unit 34 is further configured to perform the following steps:
[0149] Based on each target parameter, target attribute parameter, and calibration model, the calibrated target parameters are determined; wherein, the calibration model is determined based on the baseline attribute parameters.
[0150] The third and fourth values are determined based on the baseline attribute parameters;
[0151] The first evaluation parameter is determined based on the third value, the fourth value, and each calibrated characteristic parameter.
[0152] It should be noted that the specific implementation process of the steps performed by each module in the embodiments of this application can be referred to Figure 1 and Figure 2 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.
[0153] The information determination device provided in the embodiments of this application actively extracts the heart rate signal to be processed from the subject to be tested, and then extracts the target parameters of the target heart rate signal under different states. Based on the target parameters, disease assessment is performed. That is, disease assessment is based on objective neurophysiological indicators, not task-driven active monitoring. Therefore, it is not affected by subjective factors, unlike the subjective description of patient behavior in related technologies. This improves the objectivity and accuracy of the assessment results. Moreover, the motion signal, the heart rate signal to be processed, and the reference heart rate signal can all be acquired using wearable devices, which are not only low in cost but also highly comfortable to wear, thereby improving the experience of the subject to be tested and the efficiency of the test.
[0154] Based on the foregoing embodiments, embodiments of this application provide an information determining device, which can be applied to... Figure 1 and Figure 2 In the information determination method provided in the corresponding embodiment, refer to Figure 4 As shown, the information determining device 4 may include: a processor 41, a memory 42, and a communication bus 43, wherein:
[0155] Communication bus 43 is used to realize the communication connection between processor 41 and memory 42;
[0156] The processor 41 is used to execute the information determination program in the memory 42 to perform the following steps:
[0157] Acquire the motion signal and heart rate signal of the object to be detected, and acquire the reference heart rate signal of the object to be detected in the target state;
[0158] Based on motion signals, heart rate signals to be processed, and reference heart rate signals, the target heart rate signals of the subject under different states are determined.
[0159] Determine the target parameters for each target heart rate signal; whereby the target parameters characterize the mental state of the subject being tested.
[0160] Obtain the target attribute parameters of the object to be detected, and determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object.
[0161] In other embodiments of this application, the processor 41 is used to execute the information determination program in the memory 42 to determine the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal, so as to achieve the following steps:
[0162] Based on motion signals, the first target heart rate signal of the object under motion is determined from the heart rate signal to be processed;
[0163] Based on the reference heart rate signal, the second target heart rate signal of the subject in the resting state and the third target heart rate signal in the focused state are determined from the remaining heart rate signal; wherein, the remaining heart rate signal is the signal in the heart rate signal to be processed other than the first target heart rate signal; the resting state represents the mental relaxation of the subject; the focused state represents the mental tension of the subject.
[0164] In other embodiments of this application, the processor 41 is used to execute the motion signal-based information determination program in the memory 42 to determine a first target heart rate signal of the object to be detected in motion from the heart rate signal to be processed, in order to implement the following steps:
[0165] Acquire multiple motion parameters of the motion signal;
[0166] For each motion parameter, when it is determined that the object to be detected is in motion based on the motion parameter, the motion time of the object in motion is determined.
[0167] From the heart rate signal to be processed, determine the sub-heart rate signal corresponding to each exercise time;
[0168] The first target heart rate signal is determined based on multiple sub-heart rate signals.
[0169] In other embodiments of this application, the processor 41 is configured to execute an information determination program in the memory 42 based on a reference heart rate signal to determine a second target heart rate signal of the subject in a resting state and a third target heart rate signal in a focused state from the remaining heart rate signal, in order to implement the following steps:
[0170] The remaining heart rate signals are classified using a target classification model to obtain a first type of heart rate signal and a second type of heart rate signal; the target classification model is obtained by training an initial classification model based on the sample parameters of the reference heart rate signal.
[0171] A second target heart rate signal is determined based on the first type of heart rate signal, and a third target heart rate signal is determined based on the second type of heart rate signal.
[0172] In other embodiments of this application, the processor 41 is used to execute the information determination program in the memory 42 to determine the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object, in order to implement the following steps:
[0173] Based on the target attribute parameters, multiple target parameters, and sample attribute parameters, the first evaluation parameter of the object to be detected is determined.
[0174] Based on each first evaluation parameter and its weight, a second evaluation parameter for the object to be detected is determined; wherein the evaluation parameter includes the first evaluation parameter and the second evaluation parameter.
[0175] In other embodiments of this application, processor 41 is used to execute the information determination program in memory 42 to determine the first evaluation parameters of the object to be detected based on target attribute parameters, multiple target parameters, and sample attribute parameters, in order to implement the following steps:
[0176] From the sample attribute parameters, determine the matching attribute parameters that match the target attribute parameters;
[0177] The first and second values are determined based on the matching attribute parameters;
[0178] Based on the first value, the second value, and each target parameter, the first evaluation parameter is determined.
[0179] In other embodiments of this application, processor 41 is used to execute the information determination program in memory 42 to determine the first evaluation parameters of the object to be detected based on target attribute parameters, multiple target parameters, and sample attribute parameters, in order to implement the following steps:
[0180] Based on each target parameter, target attribute parameter, and calibration model, the calibrated target parameters are determined; wherein, the calibration model is determined based on the baseline attribute parameters.
[0181] The third and fourth values are determined based on the baseline attribute parameters;
[0182] The first evaluation parameter is determined based on the third value, the fourth value, and each calibrated characteristic parameter.
[0183] It should be noted that a detailed description of the steps performed by the processor can be found in [reference needed]. Figure 1 and Figure 2 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.
[0184] The information determination device provided in this application actively extracts the heart rate signal to be processed from the subject to be tested, and then extracts the target parameters of the target heart rate signal under different states. Based on the target parameters, disease assessment is performed. That is, disease assessment is based on objective neurophysiological indicators, rather than task-driven active monitoring. Therefore, it is not affected by subjective factors, unlike related technologies that assess disease based on subjective descriptions of patient behavior. This improves the objectivity and accuracy of the assessment results. Moreover, the motion signal, the heart rate signal to be processed, and the reference heart rate signal can all be acquired using wearable devices, which are not only low in cost but also highly comfortable to wear, thereby improving the experience of the subject to be tested and the efficiency of the test.
[0185] Based on the foregoing embodiments, this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to achieve... Figure 1 and Figure 2 The steps in the information determination method provided in the corresponding embodiment.
[0186] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.
[0187] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0188] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.
[0189] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0190] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0191] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0192] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0193] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.
Claims
1. A method for determining information, characterized in that, The method includes: The motion signal and heart rate signal of the subject to be tested are acquired, and the reference heart rate signal of the subject to be tested in the target state is acquired; wherein, the target state includes a resting state and a focused state; the resting state represents the mental relaxation of the subject to be tested, and the focused state represents the mental tension of the subject to be tested; Based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal, the target heart rate signal of the object to be detected in different states is determined; wherein, the different states include the motion state, the resting state, and the focused state; the target heart rate signal in different states is obtained by dividing the heart rate signal to be processed based on the motion signal, the reference heart rate signal in the focused state, and the reference heart rate signal in the resting state; Determine target parameters for each of the target heart rate signals; wherein the target parameters are used to characterize the mental state of the subject to be detected; The target attribute parameters of the object to be tested are obtained, and the assessment parameters of the object to be tested in terms of attention deficit hyperactivity disorder are determined based on the target attribute parameters, multiple target parameters and sample attribute parameters of sample objects; wherein the target attribute parameters and the sample attribute parameters respectively include at least age, gender and body mass index.
2. The method according to claim 1, characterized in that, The step of determining the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal includes: Based on motion signals, a first target heart rate signal of the object to be detected in motion state is determined from the heart rate signal to be processed; Based on the reference heart rate signal, a second target heart rate signal for the subject in a resting state and a third target heart rate signal in a focused state are determined from the remaining heart rate signal; wherein, the remaining heart rate signal is the signal in the heart rate signal to be processed other than the first target heart rate signal; the resting state represents the mental relaxation of the subject; the focused state represents the mental tension of the subject.
3. The method according to claim 2, characterized in that, The step of determining the first target heart rate signal of the object under motion from the heart rate signal to be processed, based on motion signals, includes: Acquire multiple motion parameters of the motion signal; For each of the motion parameters, when it is determined that the object to be detected is in motion based on the motion parameters, the motion time of the object to be detected in the motion state is determined; From the heart rate signal to be processed, determine the sub-heart rate signal corresponding to each exercise time; The first target heart rate signal is determined based on multiple sub-heart rate signals.
4. The method according to claim 2, characterized in that, The step of determining, based on the reference heart rate signal, the second target heart rate signal of the subject under test in a resting state and the third target heart rate signal in a focused state from the remaining heart rate signal includes: The remaining heart rate signal is classified using a target classification model to obtain a first type of heart rate signal and a second type of heart rate signal; wherein, the target classification model is obtained by training an initial classification model based on the sample parameters of the reference heart rate signal; The second target heart rate signal is determined based on the first type of heart rate signal, and the third target heart rate signal is determined based on the second type of heart rate signal.
5. The method according to claim 1, characterized in that, The step of determining the evaluation parameters of the object to be detected based on the target attribute parameters, multiple target parameters, and sample attribute parameters of the sample object includes: Based on the target attribute parameters, multiple target parameters, and the sample attribute parameters, the first evaluation parameter of the object to be detected is determined; A second evaluation parameter for the object to be detected is determined based on each of the first evaluation parameters and the weight of each of the first evaluation parameters; wherein the evaluation parameter includes the first evaluation parameter and the second evaluation parameter.
6. The method according to claim 5, characterized in that, The step of determining the first evaluation parameter of the object to be detected based on the target attribute parameter, multiple target parameters, and the sample attribute parameter includes: From the sample attribute parameters, determine the matching attribute parameters that match the target attribute parameters; The first and second values are determined based on the matching attribute parameters; The first evaluation parameter is determined based on the first value, the second value, and each of the target parameters.
7. The method according to claim 5, characterized in that, The step of determining the first evaluation parameter of the object to be detected based on the target attribute parameter, multiple target parameters, and the sample attribute parameter includes: Based on each of the target parameters, the target attribute parameters, and the calibration model, the calibrated target parameters are determined; wherein the calibration model is determined based on the baseline attribute parameters. The third and fourth values are determined based on the aforementioned baseline attribute parameters; The first evaluation parameter is determined based on the third value, the fourth value, and each of the calibrated feature parameters.
8. An information determining device, characterized in that, The device includes: The acquisition unit is used to acquire the motion signal and heart rate signal to be processed of the object to be detected, and to acquire the reference heart rate signal of the object to be detected in the target state; wherein, the target state includes a resting state and a focused state; the resting state represents the mental relaxation of the object to be detected, and the focused state represents the mental tension of the object to be detected; The first processing unit is configured to determine the target heart rate signal of the object to be detected in different states based on the motion signal, the heart rate signal to be processed, and the reference heart rate signal; wherein, the different states include the motion state, the resting state, and the focused state; the target heart rate signal in different states is obtained by dividing the heart rate signal to be processed based on the motion signal, the reference heart rate signal in the focused state, and the reference heart rate signal in the resting state; A determining unit is configured to determine target parameters for each of the target heart rate signals; wherein the target parameters are used to characterize the mental state of the subject to be detected. The second processing unit is used to acquire the target attribute parameters of the object to be tested, and to determine the assessment parameters of the object to be tested in terms of attention deficit hyperactivity disorder based on the target attribute parameters, multiple target parameters and sample attribute parameters of sample objects; wherein the target attribute parameters and the sample attribute parameters respectively include at least age, gender and body mass index.
9. An information determining device, characterized in that, The device includes: a processor, a memory, and a communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is used to execute the information determination program stored in the memory to implement the steps of the information determination method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The storage medium stores one or more programs, which can be executed by one or more processors to implement the steps of the information determination method as described in any one of claims 1 to 7.