Method and equipment for identifying dyskinesia of Parkinson's disease based on wearable equipment

Abstract

The invention relates to the technical field of dyskinesia recognition, and discloses a dyskinesia recognition method and equipment for a Parkinson's disease based on wearable equipment, and the method comprises the following steps: S1, collecting historical data, and constructing a reference database of dyskinesia; s2, collecting motion data of a user, and obtaining basic motion parameters of the user; s3, based on the basic motion parameters of the user, screening a reference sample for the user from the reference database; s4, performing feature extraction on the motion data of the user to obtain a motion feature index of the user; s5, matching the motion characteristic indexes of the user and the reference sample to obtain a dyskinesia recognition result of the user; according to the invention, the accuracy and comprehensiveness of dyskinesia identification of the Parkinson's patient are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of movement disorder identification, and in particular to a method and device for identifying movement disorders of Parkinson's patients based on wearable devices. Background Art

[0002] In recent years, wearable devices, such as smart watches, wristbands and insoles, can monitor various physiological parameters of the human body in real time and transmit data to the cloud for processing and analysis through wireless communication technology. For Parkinson's patients, wearable devices can not only be used to monitor the patient's daily activities, but also assist doctors in disease assessment and efficacy monitoring, thereby improving the efficiency and accuracy of diagnosis and treatment.

[0003] At present, most Parkinson's disease monitoring systems based on wearable devices on the market still have some shortcomings: existing Parkinson's disease monitoring systems can often only collect a single type of biological signal, which makes it difficult for the system to comprehensively and accurately identify multiple types of movement disorders. At the same time, the lack of effective feature extraction algorithms makes it difficult for the system to capture key features when facing complex movement patterns, affecting the reliability and accuracy of the evaluation results. There are significant individual differences between Parkinson's disease patients, including age, gender, weight, muscle strength and other aspects. However, the current monitoring system often ignores the impact of these individual factors when designing, resulting in a one-size-fits-all evaluation standard that cannot be accurately positioned according to the specific situation of each patient, reducing the practical application value of the evaluation results.

[0004] For example, the Chinese patent application with the authorization announcement number CN111544006B discloses a wearable device for quantifying and identifying movement disorders in Parkinson's patients, which aims to solve the problem of lack of reliable and non-invasive wearable devices to accurately quantify and identify movement disorders in Parkinson's patients. The invention includes: collecting wrist motion signals and ankle motion signals through wrist inertial measurement units and ankle inertial measurement units worn on different sides of the body of the measured object and the healthy person, and extracting the characteristics of the signals; sending the extracted characteristics to the output processing terminal, and obtaining and outputting the degree of the measured object's condition through a preset Parkinson's patient movement disorder quantification and identification method. The invention has a simple structure, is easy to operate, is of suitable size and convenient to wear, has low power consumption, and combines the wrist and ankle motion signals to make the judgment results accurate and reliable.

[0005] For example, a patent application with publication number CN115346670A discloses a Parkinson's disease rating method, electronic device and medium based on posture recognition. The method first obtains a motion video of a Parkinson's patient to be detected, processes each frame of the motion video, and obtains the area where the human skeleton key points of each frame are located; and intercepts a photo corresponding to the area where the human skeleton key points are located, obtains the coordinates of the human skeleton key points after a confidence threshold test, and the time series composed of the coordinates of the human skeleton key points of each frame of the image; according to the International Movement Disorder Society Unified Parkinson's Disease Rating Scale MDS-UPDRS, the time series composed of the coordinates of the human skeleton key points of each frame of the image is converted into a motion quality index corresponding to the motion video; collects patient case information and converts it into a symptom index; splices the motion quality index and the symptom index into an original feature vector, and uses a support vector machine model to regress the original feature vector to obtain a Parkinson's disease severity index.

[0006] The above technical solutions all have the problem raised by this background technology: they ignore the influence of these individual factors and cannot accurately identify each patient according to their specific circumstances.

[0007] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgement or any form of suggestion that the information constitutes the prior art already known to ordinary technicians in this field. Summary of the invention

[0008] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide a method and device for identifying movement disorders in Parkinson's patients based on wearable devices, and improve the accuracy and comprehensiveness of movement disorder identification in Parkinson's patients.

[0009] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0010] In one aspect, the present invention provides a method for identifying movement disorders in Parkinson's disease patients based on a wearable device, comprising the following steps:

[0011] S1: Collect historical data and build a reference database of movement disorders;

[0012] S2: Collect the user's motion data and obtain the user's basic motion parameters;

[0013] S3: based on the matching index and the basic motion parameters of the user, filtering reference samples for the user from the reference database;

[0014] S4: extracting features from the user's motion data to obtain a motion feature index of the user, and determining the user's tremor synchronization features and motion coordination ability based on the motion feature index; the motion feature index includes a tremor feature index, an activity feature index, and a combined feature index;

[0015] S5: Matching the motion feature indicators of the user and the reference sample, and combining the tremor synchronization characteristics with the motion coordination ability to obtain the user's movement disorder identification result.

[0016] As a preferred solution of the method for identifying movement disorders of Parkinson's disease patients based on wearable devices of the present invention, wherein: any historical data in the reference database includes a movement disorder type, a first feature vector, and a second feature vector; wherein the movement disorder type is a movement disorder or a combination of m movement disorders, and m is a positive integer greater than 1; the basic movement parameters are encoded to form a first feature vector; the movement feature index is encoded to form the second feature vector;

[0017] The motion data includes acceleration data at the wrist, acceleration data at the ankle, acceleration data at the chest, and angular velocity data at the head;

[0018] The user's basic sports parameters include age, gender, height, weight, range of joint motion, muscle strength, resting heart rate, and heart rate variability.

[0019] As a preferred solution of the method for identifying movement disorders in Parkinson's disease patients based on wearable devices according to the present invention, the method for screening reference samples for users is as follows:

[0020] Encode the user's basic motion parameters and form a basic parameter vector;

[0021] Calculating a matching index between the basic parameter vector and a first feature vector of each piece of historical data in a reference database;

[0022] The M pieces of historical data corresponding to the largest M matching indexes are extracted as M reference samples for user screening.

[0023] As a preferred solution of the method for identifying movement disorders in Parkinson's disease patients based on wearable devices according to the present invention, the calculation formula of the matching index is as follows:

[0024] ;

[0025] Where S represents the matching index between the basic parameter vector and the first feature vector of any historical data in the reference database; Represents the i-th element in the basic parameter vector, where the value range of i is 1, 2, ..., n, and n is the dimension of the first eigenvector of the basic parameter vector and any historical data; represents the i-th element in the first eigenvector; represents the adjustment coefficient; express The penalty factor is as follows:

[0026] ;

[0027] in, express and The minimum threshold for the difference between .

[0028] As a preferred solution of the method for identifying movement disorders in Parkinson's patients based on wearable devices according to the present invention, the tremor characteristic indicators include wrist tremor frequency, wrist tremor amplitude, head rotation frequency, head rotation amplitude, and posture deviation index; the extraction method of wrist tremor frequency and wrist tremor amplitude is as follows:

[0029] Extracting a first tremor data segment from the acceleration data at the wrist of the user when the user is stationary;

[0030] The first tremor data segment is subjected to a DC component removal and filtering process, specifically as follows:

[0031] Calculating a mean value of the acceleration data in the first tremor data segment, and subtracting the mean value from each acceleration data in the first tremor data segment;

[0032] Using a bandpass filter to perform filtering and noise reduction processing on the first tremor data segment;

[0033] calculating an average peak value of the first tremor data segment as the wrist tremor amplitude;

[0034] Performing frequency domain transformation on the first tremor data segment and extracting the wrist tremor frequency;

[0035] The method for extracting the head rotation frequency and the head rotation amplitude is as follows: extracting a second tremor data segment from the angular velocity data of the user's head when the user is stationary; removing the DC component and filtering the second tremor data segment; calculating the average peak value of the second tremor data segment as the head rotation amplitude; performing frequency domain transformation on the second tremor data segment, and extracting the head rotation frequency.

[0036] The posture deviation index is extracted as follows: extract chest acceleration data of the user in the vertical direction when the user is stationary as the vertical acceleration; the posture deviation index is the absolute value of the difference between the vertical acceleration and the gravity acceleration.

[0037] As a preferred solution of the method for identifying movement disorders of Parkinson's patients based on wearable devices of the present invention, wherein: the activity characteristic indicators include gait cycle, step length, step width, and trunk stability index; wherein the gait cycle, step length, and step width are calculated based on the acceleration data at the ankle when the user is exercising; the trunk stability index is the inverse of the variance of the chest acceleration data;

[0038] The combined characteristic indicators include starting delay and motion duration; the starting delay is the time required for the user to go from a stationary state to start motion when performing a specified action; the motion duration is the time required for the user to complete the specified action.

[0039] As a preferred solution of the method for identifying movement disorders in Parkinson's disease patients based on wearable devices according to the present invention, the tremor synchronization feature is represented by a tremor synchronization index; and the method for determining the tremor synchronization feature of the user is as follows:

[0040] integrating the acceleration data in the first tremor data segment to obtain a third tremor data segment;

[0041] calculating the correlation coefficients between the second tremor data segment and the third tremor data segment at different lag times;

[0042] Extracting the maximum mutual correlation coefficient and the corresponding lag time between the second tremor data segment and the third tremor data segment, and assigning a value to the tremor synchronization index; specifically as follows:

[0043] Let the maximum correlation coefficient between the second tremor data segment and the third tremor data segment be , the corresponding lag time is ;like is greater than a preset first cross-correlation threshold, and If the tremor synchronization index is smaller than a preset first hysteresis threshold, the tremor synchronization index is assigned a value of 1; otherwise, the tremor synchronization index is assigned a value of 0.

[0044] As a preferred solution of the method for identifying movement disorders of Parkinson's patients based on wearable devices of the present invention, wherein: the movement coordination ability is represented by a movement coordination index; the method for determining the movement coordination ability of a user is as follows:

[0045] Extracting a first motion data segment from the acceleration data at the wrist when the user is walking; extracting a second motion data segment from the acceleration data at the ankle when the user is walking;

[0046] Calculating the correlation coefficient between the first motion data segment and the second motion data segment at different lag times;

[0047] Extract the maximum correlation coefficient and the corresponding lag time between the first motion data segment and the second motion data segment, and assign a value to the motion coordination index; specifically as follows:

[0048] Let the maximum correlation coefficient between the first motion data segment and the second motion data segment be , the corresponding lag time is ;like is greater than a preset second cross-correlation threshold, and If the value of the motion coordination index is less than a preset second hysteresis threshold, the motion coordination index is assigned a value of 1; otherwise, the motion coordination index is assigned a value of 0.

[0049] As a preferred solution of the method for identifying movement disorders of Parkinson's patients based on wearable devices according to the present invention, the method of matching the movement feature indicators of the user with those of the reference sample and combining the tremor synchronization characteristics with the movement coordination ability to obtain the movement disorder identification result of the user is as follows:

[0050] S501: Encode the user's motion characteristic index, tremor synchronization index, and motion coordination index to form a motion characteristic vector;

[0051] S502: Calculate the tremor synchronization index and the movement coordination index of each reference sample; encode the tremor synchronization index and the movement coordination index of each reference sample and add them to the corresponding second feature vector;

[0052] S503: Calculate the similarity between the motion feature vector and the second feature vector of each reference sample;

[0053] S504: extracting N reference samples corresponding to the N second feature vectors having the greatest similarity to the motion feature vectors as voting samples;

[0054] S505: Count the movement disorder types of the voting samples, and the movement disorder type that appears most frequently in the N voting samples is the movement disorder recognition result of the user.

[0055] In a second aspect, the present invention provides a device for identifying movement disorders of Parkinson's disease patients based on a wearable device, comprising a database module, a data acquisition module, a data processing module, a characteristic index module, and an identification module; wherein:

[0056] The database module is used to collect historical data and build a reference database;

[0057] The data collection module is used to collect the user's motion data and obtain the user's basic motion parameters;

[0058] The data processing module is equipped with a matching index calculation formula and a penalty factor value selection strategy to screen reference samples for users;

[0059] The characteristic index module extracts the user's motion characteristic index based on the user's motion data;

[0060] The recognition module is used to match the motion feature indicators of the user with those of the reference sample to obtain the user's movement disorder recognition result.

[0061] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0062] By integrating multi-source data from the wrists, ankles, chest and head, it can effectively identify common movement disorders such as tremor, muscle rigidity and bradykinesia; by accurately calculating characteristic indicators such as tremor frequency, amplitude, gait cycle, etc., the accuracy and reliability of recognition are improved.

[0063] A personalized evaluation system is established by collecting basic information such as age, gender, height, and weight of the user, and combining functional test data such as range of motion of joints and muscle strength. On this basis, the present invention also proposes a method based on matching basic parameter vectors with historical data feature vectors to screen out reference samples with similar athletic ability to the target user, thereby improving the pertinence and effectiveness of the evaluation results. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0065] Figure 1 A flow chart of a method for identifying movement disorders in Parkinson's disease patients based on a wearable device provided by the present invention;

[0066] Figure 2 A flow chart of a method for extracting wrist tremor frequency and wrist tremor amplitude provided by the present invention;

[0067] Figure 3 A flow chart of a method for extracting a tremor synchronization index provided by the present invention;

[0068] Figure 4 A flow chart of the method for obtaining a user's movement disorder identification result provided by the present invention. DETAILED DESCRIPTION

[0069] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0070] Example 1

[0071] This embodiment introduces a method for identifying movement disorders in Parkinson's disease patients based on a wearable device. Figure 1 , the method comprises the following steps:

[0072] S1: Collect historical data and build a reference database of movement disorders;

[0073] Any historical data in the reference database includes a movement disorder type, a first feature vector, and a second feature vector; wherein the movement disorder type is a movement disorder or a combination of m movement disorders, and m is a positive integer greater than 1; the first feature vector is a feature vector composed of basic motion parameter encodings; and the second feature vector is a feature vector composed of motion feature index encodings.

[0074] The types of movement disorders include tremor, muscle rigidity, bradykinesia, gait disorder, and balance disorder; tremor that occurs when the limbs are in a static state is a static tremor; the muscle rigidity is manifested as continuous muscle tension, resulting in a limited range of motion; the bradykinesia includes delayed start and prolonged movement duration; the delayed start is manifested as difficulty in starting the movement, and the time required from static state to starting movement is too long; the prolonged movement duration is manifested as a long time required to complete the movement. The gait disorder includes gait freezing, gait instability, and gait panic; gait freezing is a sudden inability to move; gait instability is manifested as irregular or unstable gait cycle; gait panic is manifested as shorter step length, wider step width, etc. These characteristics may be to maintain the stability of the steps. The balance disorder includes posture control disorder, posture deviation, and abnormal posture reflex. Among them, posture control disorder is identified by analyzing the stability of the trunk. A large amplitude of trunk shaking indicates that there is an obstacle to posture control; posture deviation is manifested as a large offset of the center of gravity of the body when standing, which means a decrease in posture control ability. Abnormal posture reflex refers to the inability to adjust posture in time to maintain balance. Large time lags between movements of different limbs or body parts indicate poor coordination, i.e. abnormal postural reflexes.

[0075] S2: Collect the user's motion data and obtain the user's basic motion parameters;

[0076] The motion data includes acceleration data at the wrist, acceleration data at the ankle, acceleration data at the chest, and angular velocity data at the head.

[0077] Accelerometers can be used to collect acceleration data at the wrist, ankle, and chest. For Parkinson's patients, accelerometers can help record information such as tremor amplitude and frequency, gait changes, and balance ability. Gyroscopes can be used to collect angular velocity data of the head, which is important for detecting changes in rotational movements and reduced posture stability.

[0078] The user's basic sports parameters include age, gender, height, weight, range of joint motion, muscle strength, resting heart rate, and heart rate variability.

[0079] There are significant differences in muscle strength, reaction time and flexibility between people of different ages and genders; weight and height can affect gait, strength and flexibility. By collecting age, gender, height and weight, it can be used to exclude samples with significant differences in athletic ability between users due to age differences, gender differences, height and weight differences. In addition, some functional test data should be collected, including joint range of motion, muscle strength, resting heart rate, and heart rate variability. Among them, the range of joint motion affects athletic ability and posture control; muscle strength directly affects the ability to do daily activities; resting heart rate is the heart rate in a quiet state, which can reflect the basic metabolic capacity and exercise level to a certain extent; heart rate variability is expressed by the low-frequency / high-frequency ratio of heart rate, which is used to exclude differences in athletic ability caused by differences in the function of the autonomic nervous system. Through the comprehensive evaluation of the above basic motion parameters, samples with similar athletic ability can be screened out more comprehensively, thereby improving the accuracy of identifying movement disorders. These parameters cover multiple aspects such as physiological characteristics, functional tests, cardiopulmonary function, and neurological function, which help to eliminate interference factors caused by individual differences.

[0080] S3: Based on the matching index and the basic motion parameters of the user, a reference sample is selected for the user from the reference database; the method is as follows:

[0081] Encode the user's basic motion parameters and form a basic parameter vector;

[0082] The matching index between the basic parameter vector and the first feature vector of each historical data in the reference database is calculated using the following formula:

[0083] ;

[0084] Where S represents the matching index between the basic parameter vector and the first feature vector of any historical data in the reference database; Represents the i-th element in the basic parameter vector, where the value range of i is 1, 2, ..., n, and n is the dimension of the first eigenvector of the basic parameter vector and any historical data; represents the i-th element in the first eigenvector; represents the adjustment coefficient, which is set by technicians in this field based on actual needs; express The penalty factor is as follows:

[0085] ;

[0086] in, express and The minimum threshold of the difference between the two is set by those skilled in the art based on actual needs. Through the calculation of the above matching index, on the one hand, the cumulative difference between the corresponding elements of the basic parameter vector and the first feature vector of any historical data is encouraged to be minimized; on the other hand, by adjusting the coefficient and the penalty factor The adjustment can avoid too large a difference between any group of corresponding elements between the basic parameter vector and the first eigenvector of any historical data, thereby ensuring that the similarity between the first eigenvector with a larger matching index and the basic parameter vector is high, and any group of corresponding elements are relatively close.

[0087] The M pieces of historical data corresponding to the largest M matching indexes are extracted as the M reference samples screened for the user. The basic athletic ability of each reference sample obtained based on the screening of the matching index is close to that of the user being tested, and each reference sample has a great reference value for the user being tested.

[0088] S4: extracting features from the user's motion data to obtain a motion feature index of the user, and determining the user's tremor synchronization features and motion coordination ability based on the motion feature index;

[0089] The motion characteristic index includes a tremor characteristic index, an activity characteristic index, and a combined characteristic index;

[0090] The tremor characteristic indexes include wrist tremor frequency, wrist tremor amplitude, head rotation frequency, head rotation amplitude, and posture deviation index; Figure 2 ,The extraction method of wrist tremor frequency and wrist tremor amplitude is as follows:

[0091] Extracting a first tremor data segment from the acceleration data at the wrist of the user when the user is stationary;

[0092] The first tremor data segment is subjected to a DC component removal and filtering process, specifically as follows:

[0093] Calculating a mean value of the acceleration data in the first tremor data segment, and subtracting the mean value from each acceleration data in the first tremor data segment;

[0094] A bandpass filter is used to perform filtering and noise reduction processing on the first tremor data segment; for example, the upper cutoff frequency of the bandpass filter is 8 Hz, and the lower cutoff frequency is 4 Hz;

[0095] The average peak value of the first tremor data segment is calculated as the wrist tremor amplitude; when calculating the average peak value, the absolute value of the peak value of the acceleration data in the first tremor data segment is used.

[0096] The first tremor data segment is transformed into a frequency domain, and the wrist tremor frequency is extracted. For example, the first tremor data segment is transformed into a frequency domain by FFT, a spectrum is plotted, and the spectrum energy is calculated. The frequency corresponding to the maximum energy is the wrist tremor frequency.

[0097] One of the typical symptoms of Parkinson's disease patients is tremor, especially resting tremor. By measuring the peak value in the accelerometer data, the amplitude of the tremor can be estimated; and by frequency domain analysis, the main frequency components of the tremor can be determined. This information helps monitor the severity of the tremor.

[0098] The method for extracting the head rotation frequency and the head rotation amplitude is as follows:

[0099] Extracting a second tremor data segment from the angular velocity data of the user's head when the user is stationary, wherein the second tremor data segment corresponds to the first tremor data segment in time; that is, the start time and the end time of the first tremor data segment and the second tremor data segment are the same;

[0100] performing direct current component removal and filtering processing on the second tremor data segment;

[0101] The average peak value of the second tremor data segment is calculated as the head rotation amplitude; when calculating the average peak value, the absolute value of the peak value of the angular velocity data in the second tremor data segment is used.

[0102] The second tremor data segment is transformed into a frequency domain, and the head rotation frequency is extracted. For example, the second tremor data segment is transformed into a frequency domain by FFT, a spectrum is plotted, and the spectrum energy is calculated. The frequency corresponding to the maximum energy is the head rotation frequency.

[0103] Some movement disorders in people with Parkinson's disease involve rotation of the body or limbs, such as involuntary rotation of the head. Knowing the intensity of these rotational movements can help identify if abnormal rotational behavior is present.

[0104] The method for extracting the posture deviation index is as follows: extract the chest acceleration data in the vertical direction when the user is stationary as the vertical acceleration; the posture deviation index is the absolute value of the difference between the vertical acceleration and the gravity acceleration. The gravity acceleration is 9.8 m / s². When the user stands, ideally, the center of gravity of the body should be located on a vertical line. However, due to muscle weakness, loss of balance, etc., the center of gravity may shift when standing. When the user is stationary, the vertical acceleration measured by the accelerometer should be close to the gravity acceleration, that is, 9.8 m / s². If the vertical acceleration measured by the accelerometer deviates from the gravity acceleration, it means that the center of gravity of the body has shifted. For example, if the vertical acceleration is less than 9.8 m / s², it may be because the body is leaning forward; if it is greater than 9.8 m / s², it may be because the body is leaning backward. Parkinson's patients often show balance problems, such as unstable standing. Assess the patient's posture stability and detect whether there is a risk of falling.

[0105] The activity characteristic indicators include gait cycle, step length, step width, and trunk stability index; wherein, the gait cycle, step length, and step width are calculated based on the acceleration data at the ankle when the user is exercising; the gait cycle can be calculated by detecting the periodic pattern in the acceleration data at the ankle. The acceleration data at the ankle is three-axis acceleration data, and the displacement in different directions can be calculated through the three-axis acceleration data to obtain the step length and step width. Gait disorder is one of the common symptoms of Parkinson's patients, including gait freezing, panic gait, etc. By analyzing the periodic patterns in the accelerometer data, the step length and step width can be calculated, and these parameters can reflect whether the patient's gait pattern is normal. In addition, the periodic changes in gait can also help identify whether there is gait instability.

[0106] The trunk stability index is the inverse of the variance of the chest acceleration data. A segment of the chest acceleration data of the user during movement is intercepted and the variance is calculated. If the trunk is stable, the variance of the chest acceleration is small and the trunk stability index is high.

[0107] The combined characteristic indicators include starting delay and movement duration; the starting delay is the time required for the user to start moving from a stationary state when performing a specified action; the movement duration is the time required for the user to complete the specified action. For example, when a user raises his arm, the starting delay and movement duration can be extracted based on the acceleration data at the wrist. Bradykinesia refers to the difficulty that the patient has in starting the action and the slow movement process. The degree of bradykinesia can be quantified by measuring the time required to start moving from a stationary state (starting delay) and the time required to complete a specific task (movement duration).

[0108] The tremor synchronization feature is represented by a tremor synchronization index; Figure 3, a method for determining the user's tremor synchronization characteristics is as follows:

[0109] The acceleration data in the first tremor data segment is integrated to obtain a third tremor data segment; the third tremor data segment is obtained by integrating the first tremor data segment in the time domain, and contains linear velocity data corresponding to the acceleration data at the wrist.

[0110] calculating the correlation coefficients between the second tremor data segment and the third tremor data segment at different lag times;

[0111] Extracting the maximum mutual correlation coefficient and the corresponding lag time between the second tremor data segment and the third tremor data segment, and assigning a value to the tremor synchronization index; specifically as follows:

[0112] Let the maximum correlation coefficient between the second tremor data segment and the third tremor data segment be , the corresponding lag time is ;like is greater than a preset first cross-correlation threshold, and If the tremor synchronization index is smaller than a preset first hysteresis threshold, the tremor synchronization index is assigned a value of 1; otherwise, the tremor synchronization index is assigned a value of 0.

[0113] The above-mentioned tremor synchronization index assignment method evaluates whether the tremor is synchronized by analyzing the phase relationship between the wrist accelerometer and the head gyroscope data, that is, the cross-correlation function is used to detect the consistency of tremor between different body parts. When the cross-correlation coefficient is high (greater than the preset first cross-correlation threshold) and the corresponding lag time is close to 0 (less than the preset first lag threshold), it means that the tremor synchronization between the wrist and the head is high. By analyzing the phase relationship between the accelerometer data of different parts, it can be found whether the tremor occurs synchronously in different parts. This helps to further distinguish the tremor type or evaluate the effect of tremor control treatment.

[0114] The motor coordination ability is represented by a motor coordination index; the method for determining the motor coordination ability of a user is as follows:

[0115] The method for extracting the movement coordination index is as follows:

[0116] Extracting a first motion data segment from the acceleration data at the wrist when the user is walking; extracting a second motion data segment from the acceleration data at the ankle when the user is walking;

[0117] Calculating the correlation coefficient between the first motion data segment and the second motion data segment at different lag times;

[0118] Extract the maximum correlation coefficient and the corresponding lag time between the first motion data segment and the second motion data segment, and assign a value to the motion coordination index; specifically as follows:

[0119] Let the maximum correlation coefficient between the first motion data segment and the second motion data segment be , the corresponding lag time is ;like is greater than a preset second cross-correlation threshold, and If the value of the motion coordination index is less than a preset second hysteresis threshold, the motion coordination index is assigned a value of 1; otherwise, the motion coordination index is assigned a value of 0.

[0120] The above-mentioned method of assigning the motor coordination index detects the coordination of the user's hands and feet when walking by analyzing the cross-correlation coefficient between the wrist accelerometer and the ankle accelerometer data. When the cross-correlation coefficient is high (greater than the preset second cross-correlation threshold) and the corresponding lag time is close to 0 (less than the preset second lag threshold), it means that the user's hands and feet are well coordinated when walking. Parkinson's patients may experience decreased motor coordination, that is, impaired coordinated movements between different parts of the body. By analyzing the movement lag and synchronization between different limbs in the accelerometer data, the patient's motor coordination ability can be evaluated.

[0121] S5: Matching the motion feature index of the user with that of the reference sample, and combining the tremor synchronization feature with the motion coordination ability to obtain the user's movement disorder identification result. Figure 4 , the steps are as follows:

[0122] S501: Encode the user's motion characteristic index, tremor synchronization index, and motion coordination index to form a motion characteristic vector;

[0123] S502: Calculate the tremor synchronization index and the movement coordination index of each reference sample; encode the tremor synchronization index and the movement coordination index of each reference sample and add them to the corresponding second feature vector;

[0124] S503: Calculate the similarity between the motion feature vector and the second feature vector of each reference sample; the similarity may be calculated by using methods such as Euclidean distance and cosine similarity;

[0125] S504: extract N reference samples corresponding to the N second feature vectors with the greatest similarity to the motion feature vectors as voting samples; N is a positive integer, and the best value of N can be selected through cross-validation.

[0126] S505: Count the movement disorder types of the voting samples, and the movement disorder type that appears most frequently in the N voting samples is the movement disorder recognition result of the user.

[0127] Example 2

[0128] This embodiment is the second embodiment of the present invention. Based on the same inventive concept as the first embodiment, this embodiment introduces a movement disorder identification device for Parkinson's disease patients based on a wearable device, including a database module, a data acquisition module, a data processing module, a characteristic index module, and an identification module; wherein:

[0129] The database module is used to collect historical data and construct a reference database; any historical data in the reference database includes a movement disorder type, a first eigenvector, and a second eigenvector.

[0130] The data acquisition module is used to collect the user's motion data and obtain the user's basic motion parameters; the module is equipped with sensors such as accelerometers, gyroscopes and other testing devices, which can measure the motion data and basic motion parameters of different parts of the user;

[0131] The data processing module is equipped with a matching index calculation formula and a penalty factor value selection strategy to screen reference samples for users;

[0132] The characteristic index module extracts the user's motion characteristic index based on the user's motion data, including tremor characteristic index, activity characteristic index, and combined characteristic index;

[0133] The recognition module is used to match the motion feature indicators of the user and the reference sample to obtain the user's movement disorder recognition result. The voting samples are selected from the reference samples by calculating the vector similarity, and the user's movement disorder recognition result is obtained by voting based on the voting samples.

[0134] The specific functional implementation of each of the above modules refers to the relevant content of the method for identifying movement disorders in Parkinson's patients based on wearable devices described in Example 1, and will not be repeated here.

[0135] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the purpose and scope of protection of the present invention, which are all within the protection of the present invention.

Claims

1. A method for identifying movement disorders in Parkinson's disease patients based on wearable devices, characterized in that: The following steps are involved: S1: Collect historical data and build a reference database of movement disorders; S2: Collect the user's motion data and obtain the user's basic motion parameters; S3: based on the matching index and the basic motion parameters of the user, selecting reference samples for the user from a reference database; S4: extracting features from the user's motion data to obtain a motion feature index of the user, and determining the user's tremor synchronization features and motion coordination ability based on the motion feature index; the motion feature index includes a tremor feature index, an activity feature index, and a combined feature index; S5: Matching the motion feature indicators of the user and the reference sample, and combining the tremor synchronization characteristics with the motion coordination ability to obtain the user's movement disorder identification result.

2. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 1, characterized in that: Any piece of historical data in the reference database includes a movement disorder type, a first feature vector, and a second feature vector; wherein the movement disorder type is a movement disorder or a combination of m movement disorders, and m is a positive integer greater than 1; the basic movement parameters are encoded to form a first feature vector; and the movement feature index is encoded to form a second feature vector; The motion data includes acceleration data at the wrist, acceleration data at the ankle, acceleration data at the chest, and angular velocity data at the head; The user's basic sports parameters include age, gender, height, weight, range of joint motion, muscle strength, resting heart rate, and heart rate variability.

3. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 2, characterized in that: The method for screening reference samples for users is as follows: Encode the user's basic motion parameters and form a basic parameter vector; Calculating a matching index between the basic parameter vector and a first feature vector of each piece of historical data in a reference database; The M pieces of historical data corresponding to the largest M matching indexes are extracted as M reference samples for user screening.

4. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 3, characterized in that: The calculation formula of the matching index is as follows: ； Where S represents the matching index between the basic parameter vector and the first feature vector of any historical data in the reference database; Represents the i-th element in the basic parameter vector, where the value range of i is 1, 2, ..., n, and n is the dimension of the first eigenvector of the basic parameter vector and any historical data; represents the i-th element in the first eigenvector; represents the adjustment coefficient; express The penalty factor is as follows: ； in, express and The minimum threshold for the difference between .

5. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 4, characterized in that: The tremor characteristic indexes include wrist tremor frequency, wrist tremor amplitude, head rotation frequency, head rotation amplitude, and posture deviation index; the extraction method of wrist tremor frequency and wrist tremor amplitude is as follows: Extracting a first tremor data segment from the acceleration data at the wrist of the user when the user is stationary; The first tremor data segment is subjected to a DC component removal and filtering process, specifically as follows: Calculating a mean value of the acceleration data in the first tremor data segment, and subtracting the mean value from each acceleration data in the first tremor data segment; Using a bandpass filter to perform filtering and noise reduction processing on the first tremor data segment; calculating an average peak value of the first tremor data segment as the wrist tremor amplitude; Performing frequency domain transformation on the first tremor data segment and extracting the wrist tremor frequency; The method for extracting the head rotation frequency and the head rotation amplitude is as follows: extracting a second tremor data segment from the angular velocity data of the head when the user is stationary; performing DC component removal and filtering processing on the second tremor data segment; calculating the average peak value of the second tremor data segment as the head rotation amplitude; performing frequency domain transformation on the second tremor data segment, and extracting the head rotation frequency; The posture deviation index is extracted as follows: extract chest acceleration data of the user in the vertical direction when the user is stationary as the vertical acceleration; the posture deviation index is the absolute value of the difference between the vertical acceleration and the gravity acceleration.

6. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 5, characterized in that: The activity characteristic index includes gait cycle, step length, step width, and trunk stability index; wherein the gait cycle, step length, and step width are calculated based on the acceleration data at the ankle when the user is exercising; the trunk stability index is the inverse of the variance of the chest acceleration data; The combined characteristic indicators include starting delay and motion duration; the starting delay is the time required for the user to go from a stationary state to start motion when performing a specified action; the motion duration is the time required for the user to complete the specified action.

7. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 6, characterized in that: The tremor synchronization feature is represented by a tremor synchronization index; the method for determining the tremor synchronization feature of a user is as follows: integrating the acceleration data in the first tremor data segment to obtain a third tremor data segment; calculating the correlation coefficients between the second tremor data segment and the third tremor data segment at different lag times; Extracting the maximum mutual correlation coefficient and the corresponding lag time between the second tremor data segment and the third tremor data segment, and assigning a value to the tremor synchronization index; specifically as follows: Let the maximum correlation coefficient between the second tremor data segment and the third tremor data segment be , the corresponding lag time is ;like is greater than a preset first cross-correlation threshold, and is less than a preset first hysteresis threshold, the tremor synchronization index is assigned a value of 1; Otherwise, the tremor synchronization index is assigned a value of 0.

8. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 7, characterized in that: The motor coordination ability is represented by a motor coordination index; the method for determining the motor coordination ability of a user is as follows: Extracting a first motion data segment from the acceleration data at the wrist when the user is walking; extracting a second motion data segment from the acceleration data at the ankle when the user is walking; Calculating the correlation coefficient between the first motion data segment and the second motion data segment at different lag times; Extract the maximum correlation coefficient and the corresponding lag time between the first motion data segment and the second motion data segment, and assign a value to the motion coordination index; specifically as follows: Let the maximum correlation coefficient between the first motion data segment and the second motion data segment be , the corresponding lag time is ;like is greater than a preset second cross-correlation threshold, and If the value of the motion coordination index is less than a preset second hysteresis threshold, the motion coordination index is assigned a value of 1; otherwise, the motion coordination index is assigned a value of 0.

9. The method for identifying movement disorders in Parkinson's disease patients based on a wearable device as claimed in claim 8, characterized in that: The method of matching the motion feature indicators of the user with the reference sample and combining the tremor synchronization feature with the motion coordination ability to obtain the user's movement disorder identification result is as follows: S501: Encode the user's motion characteristic index, tremor synchronization index, and motion coordination index to form a motion characteristic vector; S502: Calculate the tremor synchronization index and the movement coordination index of each reference sample; encode the tremor synchronization index and the movement coordination index of each reference sample and add them to the corresponding second feature vector; S503: Calculate the similarity between the motion feature vector and the second feature vector of each reference sample; S504: extracting N reference samples corresponding to the N second feature vectors having the greatest similarity to the motion feature vectors as voting samples; S505: Count the movement disorder types of the voting samples, and the movement disorder type that appears most frequently in the N voting samples is the movement disorder recognition result of the user.

10. A wearable device-based movement disorder recognition device for Parkinson's disease patients, used to implement the wearable device-based movement disorder recognition method for Parkinson's disease patients according to any one of claims 1 to 9, characterized in that: It includes database module, data acquisition module, data processing module, characteristic index module and identification module; among which: The database module is used to collect historical data and build a reference database; The data collection module is used to collect the user's motion data and obtain the user's basic motion parameters; The data processing module is equipped with a matching index calculation formula and a penalty factor value selection strategy to screen reference samples for users; The characteristic index module extracts the user's motion characteristic index based on the user's motion data; The recognition module is used to match the motion feature indicators of the user with those of the reference sample to obtain the user's movement disorder recognition result.

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