CN114287890B - Motion function evaluation method for Parkinson patient based on MEMS sensor - Google Patents
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Abstract
The invention discloses a motion function evaluation method of a Parkinson patient based on a MEMS sensor. Firstly, a subject is adopted to perform a group of straight walking and turning gait tasks for data acquisition; performing gait HS, FF, HO, TO four-period detection by adopting a threshold value based on rules, and calculating stride time, supporting phase time and swing phase time; multitasking is carried out by adopting a threshold detection method based on rules, and turning and straight-going task types are output; step length and pace speed are calculated by combining a step length estimation algorithm of double integration and acceleration compensation. The accuracy meets the requirement of motion analysis, gait evaluation can be effectively and accurately carried out on the Parkinson patient, and a technical basis is provided for clinical motion evaluation of the PD patient.
Description
Technical Field
The invention relates to the field of motion evaluation, in particular to a motion evaluation system of a parkinsonism patient based on a Micro-Electro-Mechanical system (MEMS) sensor under a multitask, and particularly relates to a motion function evaluation method of the parkinsonism patient based on the MEMS sensor.
Background
Parkinson's Disease (PD) is the second most advanced neurodegenerative disease next to alzheimer's disease, caused by damage to the substantia nigra ganglion in the brain. Clinically, various dyskinesias such as muscle stiffness, tremors, instable postures, frozen gait and the like are common, and the quality of life of PD patients is greatly and negatively affected. Because PD can not be cured, early diagnosis and therapeutic intervention can effectively delay the progress of the disease course. However, the clinical diagnosis of PD patients at present mainly uses subjective observation and scale of doctors, and early-medium-term gait disorder is hidden, difficult to confirm and high in misdiagnosis rate. At present, researchers find that the gait disturbance performance of PD under complex exercise tasks (such as turning) is more remarkable, and the development of objective quantification means facing the evaluation of PD movement disturbance can effectively assist disease diagnosis.
Inertial Measurement Units (IMUs) are widely used in wearable motion estimation with their small size, portability, and freedom from experimental environmental constraints. The balance and walking ability of a subject can be assessed from the aspects of step size, gait rhythm, gait variability, asymmetry, body center of gravity stability by mounting one or more wearable inertial sensors on a body segment of the subject through a series of gait actions. Current research is commonly used to calculate gait parameters during running machines or ground walking. While other athletic tasks, such as cornering, are rarely considered in current models. Turning is a challenging motor task that requires the combined action of the motor and cognitive functions of the subject to be accomplished. PD patients exhibited more significant differences in gait variables compared to the control group. In early stages of the disease, and even before gait disturbances appear, parkinsonism patients may develop gait disturbances such as slow gait and difficulty in shifting the center of gravity during turns. It is important to assess gait characteristics of parkinson's disease patients while walking straight and turning.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and fill up the blank, and provides a motion evaluation method of a Parkinson patient based on a Micro-Electro-Mechanical system (Micro Electro-mechanical system) sensor. The invention is composed of three parts of gait phase detection, gait task identification and stride estimation, and can evaluate gait parameters of turning, straight task and step length and pace speed of a parkinsonism patient under multiple tasks by a novel data processing method such as a data filtering method, a threshold value, a peak value identification rule and an acceleration correction technology.
In order to achieve the above object and solve the background technical problems, the scheme of the invention is as follows: a method of assessing motor function of a parkinson's patient based on a MEMS sensor, the method being based on a device comprising an IMU measurement unit;
the IMU measuring unit is arranged on the left and right lower legs, the left and right feet and the waist of the Parkinson patient, a motion evaluation model is built, and gait time parameters and gait space parameters of a human body are evaluated;
further, the device fuses nine-axis inertial sensors to evaluate the motor function of the parkinsonism patient;
further, the motion function evaluation method of the Parkinson patient based on the MEMS sensor comprises the following steps:
s1, a subject is adopted to conduct a group of straight walking and turning gait tasks to conduct data acquisition;
s2, detecting four periods of gait HS, FF, HO, TO by adopting a threshold value based on rules and a peak value detection method, and calculating stride time, supporting phase time and swing phase time;
s3, performing multitasking identification by adopting a rule-based threshold detection method, and outputting turning and straight task types;
s4, calculating the step length and the pace through a step length estimation algorithm combining double integration and acceleration compensation.
Further, in the step S1, a subject is adopted to perform a set of dynamic actions to perform data acquisition, and the specific steps include:
the device is adopted to collect data and output attitude angles and acceleration signals of left and right foot parts and waist sensors which are arranged on left and right lower legs of a subject;
further, in the step S2, a threshold based on a rule is adopted, and the peak detection method is used for detecting the gait cycle, which specifically includes the steps of:
s2.1, recognizing TO and HS gait events by using peak detection according TO the gesture angle signals which are output by the step S1.2 and are installed on the lower leg;
s2.2, performing first-order differentiation according to the attitude angle signal output in the step S1.2 and installed on the foot to obtain the pitch angle angular speed of the foot sensor;
s2.3, filtering the pitch angle angular velocity of the foot sensor through a third-order zero-lag high-pass filter to obtain the pitch angle angular velocity of the filtered foot sensor;
s2.4, identifying HO and FF gait events by using a threshold detection and peak detection method for the pitch angular velocity, and adjusting parameters of the threshold to enable the parameters to meet the precision requirement;
s2.5, calculating according to the HO, TO, HS, FF gait event, and outputting gait time parameters.
Further, in the step S3, a rule-based threshold detection method is adopted to identify the type of the output task by the multitasking of turning and straight running, and the specific steps include:
s3.1, according to the attitude angle signals of the sensors arranged on the left foot, the right foot and the waist, which are output by the step S1.2, using threshold detection to identify the straight walking and turning gait tasks:
further, in the step S4, based on the gait cycle, the step length and the speed are estimated by using the acceleration of the foot as input and combining the double-integral step length estimation algorithm of error correction, and the specific steps include:
s4.1, eliminating the influence of gravity acceleration in a global coordinate system according to the acceleration signal which is output in the step S1.2 and is installed on the foot;
s4.2, carrying out zero-speed update on the acceleration signal after the influence of the gravity is eliminated according to the gait time parameter output in the step S2.5, and calculating an integral error by using a zero-speed correction method:
s4.3, calculating displacement, step length and speed of the corrected acceleration signal by double integration.
The beneficial effects provided by the invention are as follows:
the invention provides a wearable IMU-based walking-turning multitasking-oriented PD patient gait space-time parameter model.
Based on inertial sensors placed on the left and right lower legs, the left and right feet and the waist, through novel filtering processing methods, data processing methods such as threshold value, peak value identification and acceleration correction technology can accurately identify straight-going and turning tasks, detect HS, FF, HO, TO gait events and calculate step length and step speed parameters.
The gait phase detection algorithm can detect gait events with 100% of recognition accuracy, the gait task recognition accuracy is higher than 98.83%, the gait time parameter error is lower than 4.81ms, the average deviation of the gait space parameter is lower than 2cm, and the absolute average deviation of the walking speed is lower than 0.01 m/s.
The accuracy of the invention meets the requirement of motion analysis, can effectively and accurately perform gait evaluation on the Parkinson patient, and provides a technical basis for clinical motion evaluation of the PD patient.
Drawings
FIG. 1 is a method of wearing a MEMS sensor device used in the present invention;
FIG. 2 is a schematic representation of the dynamic actions employed by the present invention;
FIG. 3 is a flow chart of a method of wearable sensor-based assessment of the motor function of parkinsonism of the present invention;
FIG. 4 is a diagram of gait task recognition results of the present invention;
FIG. 5 is a diagram of the results of the gait spatiotemporal parameter verification of the present invention;
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
1. As shown in fig. 1, the subject wears five wireless inertial sensors to collect motion sensing data, and the information of the lower limbs and the waist is fused to build a motion estimation model. The nine-axis sensor comprises a three-axis accelerometer, a three-axis gyroscope and a three-axis magnetometer, wherein two sensors are placed on the backs of left and right feet and fixed by 3M double faced adhesive tape, two sensors are respectively fixed at the left and right lower leg positions, and one sensor is placed at the back upper spine position of the ilium and fixed by an elastic belt; sampling was performed at a frequency of 100 Hz. Participants were required to first conduct a static trial while standing still with feet 15 cm apart. In the walking test, he/she performs the "straight walk-turn-straight walk" task as shown in fig. 2, and repeats 20 turns at a self-comfortable speed (fig. 2). The path length is 7 meters, and the tested person needs to make 180-degree turns in the walking process. Acceleration and attitude angle data of the limb are acquired through the IMU. 2. Four gait phase detection is performed, heel Strike (HS), flat Foot (FF), heel Off (HO), and Toe Off (TO). Current researchers generally divide gait four phases by the pitch angular velocity of the foot. At the time of the HS and TO events, i.e., the beginning and end of the swing phase, the pitch angle angular velocity of the foot sensor reaches a local minimum; whereas at the moment of FF and HO events, i.e. the beginning and end of the flat phase, the absolute value of the pitch angle angular velocity measurement of the foot sensor approaches zero. Gait phases can be detected by a simple threshold, peak detection algorithm.
However, scrutiny of typical angular rate measurements of a subject while walking normally shows that the change in pitch angle angular rate of the foot has a large difference due to the difference in gait, and a simple threshold algorithm is not sufficient to accurately detect gait phase.
In order to solve the above problems, the present invention proposes a novel gait event detection algorithm. As shown in fig. 3, data preprocessing is first performed: calculating a first derivative of the pitch angle of the foot in the global coordinate system and then using a 3 rd Zero hysteresis high pass filter (f) HC =1 Hz) to obtain the pitch angular velocity of the foot. The pitch angle of the lower leg is 12 with the cut-off frequency of 5HZ th Zero-lag low-pass Butterworth filter.
Four gait events are then identified using a rule-based machine learning method:
1) The local peak of the shank pitch angle is detected as an HS event.
2) And detecting a zero crossing point of the angular speed of the pitch angle of the foot after the HS event as an FF event.
3) Detecting the local peak value of the angular velocity of the pitch angle of the foot after the FF event as the HO event
4) The local negative peak of the shank pitch angle is detected as a TO event.
Based on the detection of gait events, the time parameters of gait, such as stride time, stance phase time and swing time, are calculated.
3. And performing gait straight-going and turning multitasking recognition. Since the turn is accomplished by sequential rotation of the body segments from top to bottom, the course angle output from the inertial sensors on the torso and the inertial sensors on the feet are used for turn detection, and the angular change of the course angle between HO and the next HS event is calculated as shown in FIG. 1. A threshold detection method based on rules is adopted to conduct walking and turning multitasking recognition based on gait cycles.
If the parameters meet the following conditions, the gait state is considered to be steering:
4. and (5) performing gait space parameter calculation. And obtaining the three-dimensional position of the foot by adopting a double integration method based on the local acceleration of the global coordinate system. First, the acceleration is calculatedConverting from the sensor coordinate system to the earth coordinate system, removing the gravitational acceleration to obtain a local acceleration +.>
wherein ,for the acceleration values measured in the sensor coordinate system, the quaternion q (t) represents the sensor orientation, and the gravitational acceleration component g= [0,0,9.81 ]]。
Using acceleration of motion in global coordinate systemIs a double of (2)The foot displacement is calculated by integration. With zero speed update (ZVU), the continuous motion is divided into a series of gait cycles, integrating from HO to FF events in each cycle, and the foot integrating speed is cleared during the Flat Foot (FF) to achieve reduced drift error during integration:
wherein ,for each moment by acceleration->Integrating the instantaneous velocity, v Fo (t HO ) Let 0, p be the three-dimensional displacement of the foot from HO to the next FF in one gait cycle.
Another source of error in step estimation is accelerometer noise or measurement error that accumulates during integration, especially under motion experimental conditions. Acceleration compensation algorithms need to be considered. The measured local acceleration can be regarded as a linear combination of the true acceleration and offset errors generated by various factors such as scale factor errors, coordinate cross-coupling errors, random noise and the like. It can be assumed that the offset error is constant over a short period of time:
wherein ,for the acceleration measured, ++>For true acceleration, ε is the offset error constant.
Calculating velocity v by time integration Fo (t FF ) When the foot is fully in contact with the ground, the ideal speed should be zero. The calculation formula of ε is as follows:
in the formula ,t HO and tFF The moments of HO and next FF in one gait cycle respectively,is the local acceleration measured in the earth coordinate system.
The stride estimation compensation is performed by adopting an acceleration correction method based on gait cycle, and the stride can be calculated as follows:
the walking speed may be calculated from the estimated stride and stride time.
Fig. 4 is a gait task recognition result.
Gait parameters such as stride time, stance time and swing time during walking and turning are calculated respectively on the basis of gait task and gait phase recognition. Fig. 5 is a gait spatiotemporal parameter verification result.
The recognition accuracy of the gait phase detection algorithm in the straight walking and turning process is 100% for 4 gait events, and good accuracy (> 98.83%) is obtained in the straight walking and turning task recognition. The absolute average deviation of the gait parameters during straight-through travel is less than 2.24ms and during cornering is less than 4.81ms. The absolute average deviation of the stride is below 2cm, and the absolute average deviation of the walking speed is below 0.01 m/s.
In summary, the invention provides a wearable IMU-based walking-turning multitasking-oriented PD patient gait space-time parameter model. Based on inertial sensors placed on the left and right lower legs, the left and right feet and the waist, the invention can accurately identify straight-going and turning tasks, detect HS, FF, HO, TO gait events and calculate step length and step speed parameters through novel filtering processing methods, threshold value and peak value identification rules, acceleration correction technology and other data processing methods. The model precision of the invention meets the requirement of motion analysis, can effectively and accurately perform gait evaluation on the Parkinson patient, and provides a technical basis for clinical motion evaluation of the PD patient.
The invention is not limited to the above embodiments, but can be used to limit the scope of the invention, and any modification made on the basis of the technical scheme according to the technical idea of the invention falls within the scope of the invention.
Claims (4)
1. A method for evaluating motor function of a parkinson's patient based on a MEMS sensor, comprising the steps of:
s1, a subject is adopted to conduct a group of straight walking and turning gait tasks to conduct data acquisition;
s2, detecting four periods of gait HS, FF, HO, TO by adopting a threshold value based on rules and a peak value detection method, and calculating stride time, supporting phase time and swing phase time;
s3, performing multitasking identification by adopting a rule-based threshold detection method, and outputting turning and straight task types;
s4, calculating the step length and the pace by combining a step length estimation algorithm of double integration and acceleration compensation;
in the step S1, a subject is adopted to perform a group of dynamic actions to perform data acquisition, and the specific steps include:
according to the motion function evaluation device of the Parkinson patient based on the MEMS sensor, data acquisition is carried out, and attitude angle and acceleration signals of left and right foot parts and waist sensors which are arranged on left and right lower legs of a subject are output;
in the step S2, a threshold based on a rule is adopted, and the peak detection method is used for detecting the gait cycle, which specifically comprises the following steps:
s2.1, according TO the output attitude angle signals installed on the lower leg, using peak detection TO identify TO and HS gait events;
s2.2, performing first-order differentiation according to the output attitude angle signal installed on the foot to obtain the pitch angle angular speed of the foot sensor;
s2.3, filtering the pitch angle angular velocity of the foot sensor through a third-order zero-lag high-pass filter to obtain the pitch angle angular velocity of the filtered foot sensor;
s2.4, identifying HO and FF gait events by using a threshold detection and peak detection method for the pitch angular velocity, and adjusting parameters of the threshold to enable the parameters to meet the precision requirement;
s2.5, calculating according to HO, TO, HS, FF gait events, and outputting gait time parameters;
in the step S3, a rule-based threshold detection method is adopted to identify the type of the output task by multitasking turning and straight running, and the specific steps include:
s3.1, according to the output attitude angle signals of the sensors arranged on the left foot, the right foot and the waist, using threshold detection to identify the straight walking and turning gait tasks:
in the step S4, based on the gait cycle, the foot acceleration is used as input, and the step length and the speed are estimated by combining the double-integral step length estimation algorithm of error correction, and the specific steps include:
s4.1, eliminating the influence of gravity acceleration in a global coordinate system according to the output acceleration signal installed on the foot;
s4.2, carrying out zero-speed update on the acceleration signal subjected to the influence of the gravity cancellation according to the output gait time parameter, and calculating an integral error by using a zero-speed correction method;
s4.3, calculating displacement, step length and speed of the corrected acceleration signal by double integration.
2. The method for evaluating motor functions of a parkinson' S patient based on a MEMS sensor according to claim 1, wherein said step S4 uses a double integration method based on local acceleration of global coordinate system to obtain three-dimensional position of foot: first, the acceleration is calculatedConverting from the sensor coordinate system to the earth coordinate system, removing the gravitational acceleration to obtain the local acceleration
wherein ,for the acceleration values measured in the sensor coordinate system, the quaternion q (t) represents the sensor orientation, and the gravitational acceleration component g= [0,0,9.81 ]]；
Using acceleration of motion in global coordinate systemCalculating foot displacement by double integration;
with zero speed update (ZVU), the continuous motion is divided into a series of gait cycles, integrating from HO to FF events in each cycle, and the foot integrating speed is cleared during the Flat Foot (FF) to achieve reduced drift error during integration:
wherein ,for each moment by acceleration->Integrating the instantaneous velocity, v Fo (t HO ) Set to 0, P is the three-dimensional displacement of the foot from HO to the next FF in one gait cycle;
another source of error in step estimation is accelerometer noise or measurement error that accumulates during integration, especially under motion experimental conditions, where the acceleration compensation algorithm needs to be considered, assuming that the offset error is constant over a short period of time:
wherein ,for the acceleration measured, ++>The real acceleration is epsilon, and the offset error constant is epsilon;
calculating velocity v by time integration Fo (t FF ) When the foot is fully in contact with the ground, the ideal speed should be zero;
the calculation formula of ε is as follows:
in the formula ,t HO and tFF The moment in a gait cycle for HO and next FF, respectively, +.>Is a local acceleration measured in the earth coordinate system;
the stride estimation compensation is performed by adopting an acceleration correction method based on gait cycle, and the stride can be calculated as follows:
the walking speed may be calculated from the estimated stride and stride time.
3. The MEMS sensor-based motor function evaluation method of parkinson's disease patient according to claim 1, wherein the method uses a device comprising: an IMU measurement unit; the IMU measuring unit is arranged on the left and right lower legs, the left and right feet and the waist of the Parkinson patient, a motion evaluation model is built, and gait time parameters and gait space parameters of a human body are evaluated.
4. The MEMS sensor-based motor function evaluation method of parkinson's disease according to claim 1, wherein the device incorporates a nine-axis inertial sensor for motor function evaluation of parkinson's disease.
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