KR20190033802A - Device for quantitative measurement of freezing of gait in Parkinson`s disease and method using the same - Google Patents

Abstract

The present invention relates to a device for quantitative measurement of freezing of gait (FOG) in Parkinson′s disease and method using the same. More particularly, the present invention relates to a device and method for quantitatively measuring gait disturbances through a feature-by-feature algorithm of Parkinson′s disease gait, and the present invention can be used as information useful for discriminating the increase/decrease of FOG through long-term monitoring or building a system such as a notification service in case of FOG.

Description

[0001] The present invention relates to a device and a method for quantitative measurement of gait freezing in patients with Parkinson's disease,

The present invention relates to an apparatus and method for measuring freezing of gait (FOG) in a patient with Parkinson's disease, and more particularly, to an apparatus and a method for quantitatively measuring gait disturbance through a feature-by- .

High-level gait disorder is a gait disorder caused by abnormalities in the cortex or subcortical structures. Typical examples are walking freezing (FOG) and accelerated walking (festination).

Among these, gait freezing refers to the occurrence of an episodic gait during walking, and the condition is the same as when the patient's foot hangs on the floor for 1-10 seconds. (Giladi, 1992). This gait freezing is more frequent in initiation, turning, narrow pathway, just before arrival, and cognitive overload (Nieuwboer 2008).

The festination refers to a state in which the stride length is shortened and the cadence is accelerated regardless of the will during walking and is also known as a sequence effect or a motor instability have.

Parkinson's disease, progressive supranuclear palsy, and corticobasal degeneration are the degenerative brain diseases that cause such high-level gait disorder. Secondary Parkinson's disease, accompanied by hydrocephalus, Syndrome and vascular parkinsonism.

Parkinsonism or Parkinson ' s syndrome is referred to as a disorder having symptoms similar to that of Parkinson's disease.

Parkinson's disease is a disease in which neurotransmitter dopamine-containing neurons are degenerated. Recent brain protein alpha synuclein has been implicated in the destruction of dopaminergic neurons in Parkinson's disease, Many studies have been reported, as it is known to be related to induction of degeneration.

Among them, Bruce Yankner's team, when this brain protein, alpha-synuclein, binds to dopamine in the nerve, promotes the secretion of toxic reactive oxygen molecules (cytotoxic substances) and ultimately leads to nerve cell apoptosis Induced dopaminergic neuronal degeneration, and that the degeneration of certain neurons by such alpha-synuclein could ultimately provide a cause for sub-autonomic motor disorders, the main symptom of Parkinson's patients .

The prevalence of Parkinson's disease is estimated to be 780 per 100 000 population, with a prevalence of 1.47% in the population over 60 years of age (Seo WK et al. 2007) .

The gait freezing occurs in about half of patients with Parkinson's disease and increases with disease progression to 60% in more advanced stages.

Patients with Parkinson's disease often experience falls due to gait freezing and increase the risk of fracture, which has a significant impact on the patient's daily life and quality of life.

Currently, the diagnostic approach for patients with Parkinson's disease relies on a subjective clinical scale (UPDRS: Unified Parkinson's Disease Rating Scale), but there is no objective and quantitative diagnostic system. UPDRS is the most widely used clinical scoring system to assess the severity and progression of Parkinson 's disease. The UPDRS score is from 0 to 4 points (high scores indicate severe symptoms). However, the UPDRS was not objective in determining the score, and the intra-rater reliability and the inter-rater reliability of the clinical individual were all low, so that the accuracy of the diagnosis and the efficiency of the medication , There is a serious problem that it is necessary to repeat trial and error in order to determine the treatment suitable for the patient's condition.

In addition, the analysis of the existing gait freeze analysis is performed by applying time domain and fuzzy theory. This method can over-estimate or under-estimate the condition of gait freezing of various patients with Parkinson's disease.

In recent years, studies on inertial navigation devices that calculate the user's attitude, velocity, and travel distance using an inertial sensor including an accelerometer and a gyroscope are being accelerated. The gyroscopes and accelerometers included in the inertial sensors used are inertial sensors for measuring angular velocity and angular velocity, respectively, and they are used as the core sensors of the inertial navigation system which calculates the position, velocity and position of the moving antibody. At this time, the posture of the user is calculated by integrating the gyro output, and the velocity and position of the user are calculated by integrating the accelerometer output

The present invention aims to provide a gait freezing measuring device and a measuring method for quantitatively measuring gait disturbances through a characteristic-based algorithm of Parkinson's disease gait, and to provide an objective criterion for determining whether or not Parkinson's disease is present.

In order to solve the above problems,

A three-axis acceleration sensor for sensing acceleration;

A gyro sensor for sensing start, stop, walking and rotation;

A data extracting unit for processing the data provided from the acceleration and the gyro sensor;

A gait freezing predicting unit for predicting gait freezing when starting, stopping, walking and turning;

A data transmission module for transmitting data to a terminal;

And the terminal includes a storage unit for storing data.

The 'acceleration sensor' applies a force (F = ma) to a moving structure having a constant mass m to accelerate the moving structure to a constant acceleration (a), and a control signal , Pressure voltage, piezoresistance, capacitance, and the like are measured to obtain acceleration

The '3-axis acceleration sensor' recognizes the acceleration input through the X-axis acceleration sensor and the Y-axis acceleration sensor in the horizontal direction, and the Z-axis acceleration sensor recognizes the acceleration input through the vertical motion detection can do. In order to improve the sensing performance of the X-axis acceleration sensor and the Y-axis acceleration sensor, the horizontal movement of the sensing structure included in each acceleration sensor with respect to a constant acceleration input must be large. On the other hand, in a Z-axis acceleration sensor, the sensing structure can move up and down about a reference axis for a constant acceleration input. Therefore, in order to improve the sensing performance of the Z-axis acceleration sensor, the vertical movement of the sensing structure must be large for a constant acceleration input. In order to increase the movement in the vertical direction, it is necessary to increase the rotation radius of the sensing structure or increase the size of the sensing area.

The 'gyro sensor' is a sensor for detecting the angular velocity, and there are various types such as a rotary type, a vibration type, a fluid type, and an optical type. Vibrating sensors can be classified into two types, one of which is piezoelectric and the other of which is capacitive. Currently, the capacitive type comb structure occupies most of the vibration type sensors, and the piezoelectric type is also partially utilized. A vibrating gyro sensor usually detects angular velocity magnitude by the force of a Coriolis force.

The data transmission module transmits the signal output from the prediction unit to the outside via a wireless interface. The wireless interface may be any one of WiFi, Zigbee, RF, and wireless data communication (3G, LTE, etc.) have.

The storage unit may be a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (for example, SD or XD memory), a random access (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, or an optical disk And is a medium.

In addition,

And the gait freezing measuring device is worn on the ankle.

In addition,

Sensing an acceleration signal according to a motion of a pedestrian through a three-axis acceleration sensor;

Sensing start, stop, walk and rotation through a gyro sensor;

Processing the data provided from the acceleration and the gyro sensor through a data extraction unit;

Estimating a walking freezing by starting, stopping, walking, and rotating through the walking freezing prediction unit;

Transmitting data to a terminal through a data transmission module;

And storing data in a storage unit of the mobile terminal.

In addition,

Wherein the data extracting step is a method of collecting data of three-axis acceleration and angular velocity signals and extracting signals of acceleration and angular velocity of each axis.

In addition,

Wherein the data extracting step extracts data using a gyro sensor and an acceleration of 30-32 Hz per second.

In addition,

Wherein the data processing step removes a linear trend and uses a moving average, a median filter, and a band pass filter.

In addition,

Wherein the gait freezing prediction step is a method using an analysis of a time and a state interval and a discrete function algorithm.

In addition,

Wherein the gait freezing prediction step is divided into gait freezing at the time of walking, gait freezing at the time of rotation, and gait freezing at the time of stopping and starting, respectively.

The present invention quantitatively measures gait disturbances through a feature-by-feature algorithm of Parkinson's disease gait to provide an objective criterion for whether or not Parkinson's disease is present, and measures such as the increase or decrease of gait freezing through long-term monitoring, And can be used as information useful for building a system such as a service.

1 schematically shows the steps of data extraction, processing, freezing prediction, transmission and storage according to the present invention.
FIG. 2 illustrates an example in which an acceleration and a gyro sensor are worn on an ankle and a knee of a patient.
3 shows data of the extracted acceleration sensor.
4 shows data of the extracted gyro sensor.
FIG. 5 exemplarily shows a method of determining gait freezing by using phase difference distinguished from stable and existing gait through discrete transformation.
Fig. 6 shows that the sensor knee wear is more efficient than the ankle wear in the prediction of gait freezing.

The system consists of an acceleration sensor for detecting acceleration and a gyro sensor for detecting the inclination of the lower limb, that is, rotation (body turning) and stop-start of the lower limb, and acceleration and tilt data provided from the acceleration and gyro sensor And a data transfer module for transferring the data to the mobile station, and a data transfer module for transferring data to the mobile station. And a terminal having a storage unit for storing data.

Based on the clinical context, various patterns of walking, freezing, turning-hestitation, and walking-hesitation were analyzed by using time interval, situation interval, and discrete system theory (Fig. 1).

The analysis of time and situation and the gait freezing prediction system using the discrete function algorithm collect data of 3-axis acceleration and inertia signal according to the movement of ankle and knee of the patient and extract the signals of acceleration and angular velocity of each axis and then processed (Fig. 2).

To remove stationary noise, we removed the linear trend and used a moving average, a median filter, and a band pass filter to remove measurement noise, high frequency noise, and gravitational acceleration other than gait acceleration during walking.

Example 1: Walking data collection for patients with Parkinson's disease

Data were collected from 8 participants who showed frequent gait freezing according to the situation. The subject wore the accelerometer with three axes and the gyro sensor on the dominant ankle and knee and then walked with his or her own preferred walking speed without assistance. In order to predict freezing of walking during daily walking, I walked for 20 minutes so that at least one FOG including rotation and direction change was generated in the walking zone in I university. In addition, the gait images of all sections were photographed using a video camera to determine the gait freezing when walking. In this study, the three-axis velocity data is processed by using a band pass filter, and then the threshold level for distinguishing low power and freezing of standing and standing from the measured vertical, The time interval of gait freezing was determined by using the threshold level to distinguish between the power value of normal gait and gait freezing. In addition, the gyro sensor data was used to correct the situation section such as direction rotation, stop start, and so on. The patterns of walking freezing were divided into walking freezing at the time of walking, freezing at the time of turning, and freezing at the time of stop - start. Acceleration and gyro sensor were used to compare the judgment data of gait freezing with that of the clinician.

Example 2 Comparative Analysis of Gait Data of Patients with Parkinson's Disease

The extracted acceleration data of 30-32 Hz per second and gyro sensor data are collected (FIG. 3 and FIG. 4), and data extracted from each sensor is filtered to extract a data time interval judged to be over 2 seconds or more.

The gyro sensor data was extracted according to the extracted data time interval and context data such as walking, standing, walking, sitting and walking were extracted (Fig. 5).

The gait freezing was judged by using phase difference which is distinguished from stable and existing gait through discrete transformation. The time interval of the gait freezing should satisfy the case where the threshold value of vertical, left, and right vertical distance calculated by the discrete transformation of the comparison signal is larger than that at the stop and smaller than that at the time of walking, and the rotation, turning, Lt; / RTI >

The gyro sensor data showed a significant difference in the acceleration value due to the walking disconnection of the gait freezing section compared with the normal gait section which was confirmed through the acceleration signal data corrected by the gyro sensor data and was similar to the gait freezing section judged by the actual clinician, The estimated FOG interval using the sensor also shows the interval similar to the interval determined by the clinician. Moore et al reported higher predictability for knee wear than for the ankle trunk, but the predictability of gait freezing in this study was significantly better when wearing ankle than when wearing knee (Figure 6).

In this study, the statistical accuracy of the gait freezing prediction system can be confirmed by comparing the gait interval estimated by the algorithm implemented by the acceleration sensor and the gyro sensor, there was. The standard deviation of the gait freezing was estimated by 92% and 72% of the gait freezing, and the gait freezing was predicted with the sensitivity of 72%. Could.

Therefore, it is useful to determine the therapeutic effects (such as the increase / decrease of gait freezing) in the daily life rather than the in-hospital and laboratory-level examinations of Parkinson's patients through long-term monitoring or useful information It can be used as

Claims (9)

A three-axis acceleration sensor for sensing acceleration;
A gyro sensor for sensing start, stop, walking and rotation;
A data extracting unit for processing the data provided from the acceleration and the gyro sensor;
A gait freezing predicting unit for predicting gait freezing when starting, stopping, walking and turning;
A data transmission module for transmitting data to a terminal;
And the terminal includes a storage unit for storing data.
The method according to claim 1,
Wherein the gait freezing measuring device is worn on the ankle.
Sensing an acceleration signal according to a motion of a pedestrian through a three-axis acceleration sensor;
Sensing start, stop, walk and rotation through a gyro sensor;
Processing the data provided from the acceleration and the gyro sensor through a data extraction unit;
Estimating a walking freezing by starting, stopping, walking, and rotating through the walking freezing prediction unit;
Transmitting data to a terminal through a data transmission module;
And storing data in a storage unit of the mobile terminal.
The method of claim 3,
Wherein the step of extracting data is a method of collecting data of three-axis acceleration and angular velocity signals and extracting signals of acceleration and angular velocity of each axis.
The method of claim 3,
Wherein the data extraction step extracts data using a gyro sensor and an acceleration of 30-32 Hz per second.
The method of claim 3,
Wherein the data processing step removes a linear trend and uses a moving average, a median filter, and a band pass filter.
The method of claim 3,
Wherein the gait freezing prediction step is a method using an analysis of a time and a state interval and a discrete function algorithm.
The method of claim 3,
Wherein the walking freezing prediction step is performed by separately calculating walking freezing at the time of walking, freezing at the time of walking, and freezing at the time of stopping and starting.
The method of claim 3,
Wherein the gait freezing predicting unit determines that the vertical, the back and front, the left and right threshold levels calculated by the discrete transform of the signal are greater than the stop time, and is smaller than the walking time.

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