System for evaluating Parkinson's disease based on inertial MEMS (Micro Electro Mechanical System) equipment

Abstract

A system for evaluating Parkinson's disease based on inertial MEMS equipment belongs to the technical field of inertial measurement and comprises an information acquisition module for acquiring acceleration and angular velocity information of a measured part according to a preset time sequence and transmitting a data frame composed of the time sequence, the acceleration and the angular velocity information to a data analysis module; the data analysis module is used for analyzing the motion data of the detected part from the received data frame and transmitting the motion data to the data processing module; and the data processing module is used for carrying out attitude calculation on the received motion data to obtain a Parkinson disease assessment result of the tested person. According to the invention, the Parkinson's disease is automatically assessed by using the information acquisition module, and the problem that video analysis is limited by the use environment and the requirement is harsh is solved.

Description

Technical Field

[0001] The invention relates to a system for evaluating Parkinson's disease based on an inertial MEMS device, belonging to the technical field of inertial measurement. Background Art

[0002] The common method for evaluating movement disorders in Parkinson's disease is to comprehensively assess the severity of the disease through medical record information, patient-reported symptoms, clinical pharmacist observations, and the Unified Parkinson's Disease Assessment Scale. This can roughly estimate the general condition of the disease. However, the evaluation criteria of these methods are relatively subjective and lack objective quantitative data support. Therefore, quantitative data with strong consistency is very meaningful in clinical diagnosis.

[0003] In traditional analysis, video analysis is the mainstream method of evaluating technology. However, this evaluation method is challenging, time-consuming and labor-intensive, and is limited by the stringent requirements of the usage environment. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the deficiencies of the prior art, to provide a system for evaluating Parkinson's disease based on an inertial MEMS device, to realize automatic evaluation of Parkinson's disease using an information acquisition module, and to solve the problem that video analysis is limited by the stringent requirements of the use environment.

[0005] The technical solution of the present invention is: a system for evaluating Parkinson's disease based on inertial MEMS devices, comprising:

[0006] The information acquisition module acquires the acceleration and angular velocity information of the measured part according to a preset time sequence, and transmits a data frame consisting of the time sequence, acceleration and angular velocity information to the data analysis module;

[0007] A data analysis module analyzes the motion data of the measured part from the received data frame and transmits the motion data to the data processing module; the motion data includes the motion state and motion parameters of the measured part; the motion state includes motion, stillness, and tremor;

[0008] The data processing module obtains the Parkinson's disease assessment result after posture analysis of the received motion data.

[0009] Furthermore, the information acquisition module includes a plurality of IMU sensors, and the IMU sensors are used to sense time series, acceleration and angular velocity information.

[0010] Furthermore, the IMU sensor has a built-in data storage module, a Bluetooth communication module and a polymer physical rechargeable battery.

[0011] Furthermore, the motion parameters include motion intensity, motion amplitude, and motion frequency.

[0012] Furthermore, the Parkinson's disease assessment result includes movement parameters of the measured part; the movement parameters of the measured part include the movement frequency of the measured part, the maximum value, the minimum value and the average value of the movement amplitude.

[0013] Furthermore, a static initial calibration is performed before the attitude solution, and the attitude angles of each IMU sensor are calculated from the acceleration information and the magnetic field strength information, and then the initial attitude matrix from the carrier coordinate system to the navigation coordinate system is obtained, and then the new attitude angles are calculated by the continuously updated quaternion.

[0014] Furthermore, the updating method of the quaternion is:

[0015]

[0016] Calculated from Q:

[0017]

[0018] According to the updated The three attitude angles are calculated, and the data of the three attitude angles in the whole motion process are obtained. Among them, Q = [q0, q1, q2, q3] is the attitude quaternion, is the projection of the angular velocity of the carrier coordinate system b relative to the navigation coordinate system n in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the X-axis in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the Y axis in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the Z axis in the carrier coordinate system b, is the attitude matrix.

[0019] Furthermore, the information acquisition module provides an external interface, and the external interface is used to obtain the acceleration and angular velocity information of the measured part collected by the external sensor; the measured part includes the hand of the measured person.

[0020] Furthermore, the upper limb data were collected by testing the subjects in four items: sitting rest tremor, standing rest tremor, rapid palm flip, and fist extension.

[0021] The advantages of the present invention compared with the prior art are:

[0022] The present invention automatically extracts Parkinson's disease assessment results through an information acquisition module. First, the motion parameters such as amplitude and frequency of the measured part are obtained according to the time series, angular velocity information and acceleration information of the sensor, which solves the problems of subjective assessment and inability to conduct quantitative analysis in traditional motor function assessment scales, and the Parkinson's disease assessment results are directly obtained by the host computer software. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0024] Figure 1 It is a schematic diagram of the system principle of the present invention. DETAILED DESCRIPTION

[0025] In order to better understand the above technical scheme, the technical scheme 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 scheme of the present invention, rather than limitations on the technical scheme of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0026] The following is a further detailed description of a system for evaluating Parkinson's disease based on an inertial MEMS device provided by an embodiment of the present invention in conjunction with the drawings of the specification. The specific implementation method may include: an information acquisition module, a data transfer server and a host computer; wherein the host computer includes a data receiving module, a data parsing module, a data processing module and a data display module; the information acquisition module is used to obtain the acceleration and angular velocity information of the measured part, and transmit the initial data composed of the time series, acceleration and angular velocity information to the data receiving module; the information acquisition module provides an external interface, and the external interface is used to obtain the acceleration and angular velocity information of the measured part collected by an external sensor; the measured part includes the hand of the measured person. The data transfer server forwards the wireless signal sent by the information acquisition module to the host computer; the data receiving module transmits the initial data to the data analysis module; the data analysis module obtains motion data from the initial data, and transmits the motion data to the data processing module; the data processing module obtains the Parkinson's disease assessment result through the data processing algorithm of the motion data, and transmits the Parkinson's disease assessment result to the data display module; the data display module receives and displays the Parkinson's disease assessment result.

[0027] The information acquisition module includes a plurality of IMU sensors, which can be sensitive to time series, acceleration and angular velocity information.

[0028] The IMU sensor data processing module, Bluetooth communication module and repeatedly rechargeable polymer physical battery.

[0029] After the IMU sensor is turned on, it is in the waiting state. Open the host computer software on the computer. After the software is opened, it will automatically check the device connection status and automatically complete the sensor configuration (specifically which sensor is used to test which hand, which can be adjusted manually). After confirmation, you can close the window; wear the IMU gloves on the left and right hands respectively, and place them on the pockets of the left and right hands according to the IMU device number configured above, and pay attention to the direction of the sensor. After completing all the tests, the software will automatically generate a PDF report. After the test is completed, take out the IMU device and turn it off, take off the gloves, and finally close the host computer software.

[0030] The motion data includes motion state and motion parameters.

[0031] The Parkinson's disease assessment results include the maximum value, minimum value and average value of movement frequency and movement amplitude.

[0032] The data analysis module analyzes the initial data received to determine whether the frame header checksum of each frame of data is correct and the frame sequence number is continuous without frame loss. If it is found that the frame header checksum is incorrect and the frame sequence number is discontinuous, a "data incomplete" prompt will be given; if the frame header checksum of each frame of data is correct and the frame sequence number is continuous without frame loss, posture calculation will be performed to obtain motion data.

[0033] In attitude solution, the IMU sensor attitude angle is obtained based on the acceleration and magnetic field strength information, and then the initial attitude matrix from the carrier coordinate system to the navigation coordinate system is obtained, and then the new attitude angle is obtained by the continuously updated quaternion calculation.

[0034] The effective peak points and valley points of the motion data are identified through a filtering algorithm, and then the maximum, minimum and average values ​​of the motion frequency and motion amplitude can be automatically calculated through a processing program.

[0035] In the solution provided in the embodiment of the present invention, Figure 1As shown, the system for automatically extracting Parkinson's disease assessment results based on inertial sensors includes: an information acquisition module, a data transfer server and a host computer. The host computer includes a data receiving module, a data analysis module, a data processing module and a data display module; the information acquisition module is used to obtain the acceleration and angular velocity information of the measured part, and transmit the initial data composed of the time series, acceleration and angular velocity information to the data receiving module; the data transfer server forwards the wireless signal sent by the information acquisition module to the host computer; the data receiving module transmits the initial data to the data analysis module; the data analysis module obtains motion data from the initial data, and transmits the motion data to the data processing module; the data processing module obtains the Parkinson's disease assessment result through the data processing algorithm of the motion data, and transmits the Parkinson's disease assessment result to the data display module; the data display module receives and displays the Parkinson's disease assessment result.

[0036] The information acquisition module is used to obtain the angular velocity and acceleration information of the measured part and ensure that the measured data is stored in the cache. The information acquisition module includes several IMU sensors, which can be sensitive to time series, acceleration and angular velocity information. Specifically, the information acquisition module is composed of 1 or 2 IMU sensors. The IMU sensor adopts a low-power design to collect, save and upload the time series, acceleration and angular velocity information it is sensitive to. The information acquisition module provides an external interface, which is used to obtain the acceleration and angular velocity information of the measured part collected by the external sensor; the measured part includes the hands of the person being measured.

[0037] IMU sensor data processing module, Bluetooth communication module and rechargeable polymer battery.

[0038] The system for automatically extracting Parkinson's disease assessment results based on inertial sensors also includes: a data transfer router, which establishes a wireless link between the information acquisition module and the data receiving module in the host computer via a Bluetooth signal, and uploads the time series, acceleration, and angular velocity information stored in the information acquisition module to the data receiving module in the host computer through the wireless link.

[0039] The data receiving module passes the data to the data analysis module, which obtains motion data from the random, noisy original sensor signal, and then obtains the motion parameters through the data processing algorithm and displays them by the host computer.

[0040] The data of the sensor is transmitted wirelessly, each sensor is independent of each other, and the data can be stored at the same time to ensure data integrity. The specific implementation method is as follows:

[0041] Send online reporting information to the data transfer router through wireless Bluetooth communication, and then establish a stable and reliable wireless data transmission link with it. After the IMU sensor is turned on, it is in the waiting state. Open the host computer software on the computer. After the software is opened, it will automatically check the device connection status and automatically complete the sensor configuration (specifically which sensor is used to test which hand, which can be adjusted manually). After confirmation, you can close the window; wear the IMU gloves on the left and right hands respectively, and place it in the pockets of the left and right hands according to the IMU device number configured above, and pay attention to the direction of the sensor.

[0042] All instructions and measurement data are transmitted via wireless Bluetooth, which greatly reduces the complexity of motion measurement systems using traditional video monitoring methods in terms of device connection, wearability, and information transmission, and greatly improves the ease of use of the system.

[0043] The data analysis module obtains the motion state and motion parameters through the time series, angular velocity information and acceleration information through pattern recognition method. The specific implementation method is as follows:

[0044] The received data is analyzed to determine whether the frame header checksum of each frame of data is correct, the frame sequence number is continuous and there is no frame loss. If the frame header checksum is incorrect or the frame sequence number is discontinuous, an "incomplete data" prompt will be given. After parsing the complete data, the attitude solution will be performed.

[0045] Furthermore, static initial calibration is required before attitude solution. The attitude angles of each IMU sensor are calculated from the acceleration information and magnetic field strength information, and then the initial attitude matrix from the carrier coordinate system to the navigation coordinate system is obtained to solve the problem of human body wearing and installation. After that, the new attitude angle is calculated by the continuously updated quaternion. The specific implementation method is as follows:

[0046] The strapdown inertial navigation system (SINS) selects the "east-north-sky" geographic coordinate system as the navigation coordinate system to implement the recursive update algorithm of the strapdown inertial navigation. The strapdown inertial navigation update algorithm is divided into three parts: attitude, velocity and position update, and the attitude update algorithm is the core.

[0047] The letters in the formula below refer to the following:

[0048] 1. Carrier coordinate system (b)

[0049] 2. Navigation coordinate system (n)

[0050] 3. Inertial coordinate system (i)

[0051] 4. Earth coordinate system (e)

[0052] The formula below explains:

[0053] Represents the projection of the angular velocity of the b system relative to the n system in the b system, similarly;

[0054] Represents the component of the angular velocity of the b system relative to the n system projected on the X axis in the b system, similarly;

[0055] From the angular velocity equation we get

[0056]

[0057] Since the MEMS sensor has low accuracy and cannot be sensitive to the angular velocity of the earth's rotation, it is ignored. At the same time, the speed of people in indoor sports scenes is less than 2m / s, the radius of the earth R is 6371393m, and so For 10 -7 ~10 -6 Therefore, for MEMS sensors, the above formula can be equivalent to:

[0058]

[0059] and Q are the attitude matrix and attitude quaternion, and the initial values ​​are the initial attitude angles θ, γ, Calculated, and then calculated by the continuously updated quaternion. After that, the quaternion update equation is as follows:

[0060]

[0061] Calculated by Q The formula is as follows:

[0062]

[0063] According to the updated The three attitude angles can be calculated, thereby obtaining the data of the three attitude angles during the entire movement process.

[0064] Furthermore, Parkinson's patients were tested through four items: sitting rest tremor, standing rest tremor, rapid palm flip, and fist extension. Their upper limb data were collected, which included parameters such as angle, amplitude, angular velocity, acceleration, and frequency. The data were compared and analyzed with the data of healthy people.

[0065] Furthermore, in the sitting static tremor project, the affected limb showed periodic changes in angle with a frequency between 3-4 Hz, and the angle change was significantly greater than that of the healthy limb.

[0066] Furthermore, in the standing static tremor test, the affected limb showed periodic changes in angle with a frequency between 3-4 Hz, and the angle change was significantly greater than that of the healthy limb.

[0067] Furthermore, in the palm rapid flipping event, the healthy limb was able to flip rapidly (greater than 1Hz) with a large amplitude (160°-180°), while the amplitude and frequency of the affected limb were significantly lower than those of the healthy limb.

[0068] Furthermore, in the hand-clenching fist stretching event, the angular velocity changes significantly when the healthy limb clenches a fist, while the angular velocity changes less and more slowly when the affected limb clenches a fist.

[0069] Therefore, Parkinson's patients have significant differences from healthy people in sitting rest tremor, standing rest tremor, rapid palm flipping, and fist extension. These four items can be used to determine the level of Parkinson's disease and achieve systematic intelligent evaluation and diagnosis.

[0070] Furthermore, according to the data of the movement stage, the data is Fourier transformed to obtain the frequency value f corresponding to the maximum value in the power spectrum density. As the sampling interval, use the MATLAB built-in function "islocalmax" to obtain the peak and valley values ​​of a section of the data.

[0071] The data processing module identifies effective peak points and valley points through a filtering algorithm, thereby obtaining parameters such as angle, amplitude, angular velocity, acceleration, and frequency, and displays them in the form of a report on the host computer. No human intervention is required throughout the process, and it can be directly provided for reference by medical staff.

[0072] The present invention solves the problem that traditional video monitoring methods have strict environmental requirements. According to the time series, angular velocity and acceleration information of the inertial wearable device, the motion state and motion parameters are obtained through pattern recognition, and then the effective peak and valley value points are identified through a filtering algorithm, thereby obtaining the Parkinson's disease assessment result.

[0073] The present invention automatically extracts Parkinson's disease assessment results through an information acquisition module. First, according to the time series, angular velocity information and acceleration information of the sensor, the motion state and motion parameters are obtained through a pattern recognition method, and then the Parkinson's disease assessment results are output. No human intervention is required throughout the process, and it can be directly provided to medical staff for reference.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

[0075] The contents not described in detail in the specification of the present invention belong to the common knowledge of those skilled in the art.

Claims

1. A system for evaluating Parkinson's disease based on inertial MEMS devices, characterized in that: include: The information acquisition module acquires the acceleration and angular velocity information of the measured part according to a preset time sequence, and transmits a data frame consisting of the time sequence, acceleration and angular velocity information to the data analysis module; A data analysis module analyzes the motion data of the measured part from the received data frame and transmits the motion data to the data processing module; the motion data includes the motion state and motion parameters of the measured part; the motion state includes motion, stillness, and tremor; The data processing module obtains the Parkinson's disease assessment result after posture analysis of the received motion data.

2. A system for evaluating Parkinson's disease based on inertial MEMS devices according to claim 1, characterized in that: The information acquisition module includes a plurality of IMU sensors, and the IMU sensors are used to sense time series, acceleration and angular velocity information.

3. A system for evaluating Parkinson's disease based on inertial MEMS devices according to claim 1, characterized in that: The IMU sensor has a built-in data storage module, a Bluetooth communication module and a polymer physical rechargeable battery.

4. The system for evaluating Parkinson's disease based on inertial MEMS device according to claim 1, characterized in that: The motion parameters include motion intensity, motion amplitude, and motion frequency.

5. The system for evaluating Parkinson's disease based on inertial MEMS device according to claim 1, characterized in that: The Parkinson's disease assessment result includes movement parameters of the measured part; the movement parameters of the measured part include the movement frequency of the measured part, the maximum value, the minimum value and the average value of the movement amplitude.

6. The system for evaluating Parkinson's disease based on inertial MEMS device according to claim 1, characterized in that: Before the attitude solution, a static initial calibration is performed, and the attitude angles of each IMU sensor are calculated from the acceleration information and the magnetic field strength information, and then the initial attitude matrix from the carrier coordinate system to the navigation coordinate system is obtained, and then the new attitude angles are calculated by the continuously updated quaternion.

7. A system for evaluating Parkinson's disease based on inertial MEMS devices according to claim 6, characterized in that: The updating method of the quaternion is Calculated from Q: According to the updated The three attitude angles are calculated, and the data of the three attitude angles in the whole motion process are obtained. Among them, Q = [q0, q1, q2, q3] is the attitude quaternion, is the projection of the angular velocity of the carrier coordinate system b relative to the navigation coordinate system n in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the X-axis in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the Y axis in the carrier coordinate system b, is the component of the angular velocity of the carrier coordinate system relative to the navigation coordinate system n projected on the Z axis in the carrier coordinate system b, is the attitude matrix.

8. The system for evaluating Parkinson's disease based on inertial MEMS device according to claim 1, characterized in that: The information acquisition module provides an external interface, and the external interface is used to obtain the acceleration and angular velocity information of the measured part collected by the external sensor; the measured part includes the hand of the measured person.

9. A system for evaluating Parkinson's disease based on inertial MEMS devices according to claim 8, characterized in that: The upper limb data were collected by testing the subjects in four items: sitting rest tremor, standing rest tremor, rapid palm flipping, and fist extension.

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