KR101747866B1 - Parkinson's disease indicator evaluation device and method using accelerator sensor - Google Patents

Parkinson's disease indicator evaluation device and method using accelerator sensor Download PDF

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KR101747866B1
KR101747866B1 KR1020150165634A KR20150165634A KR101747866B1 KR 101747866 B1 KR101747866 B1 KR 101747866B1 KR 1020150165634 A KR1020150165634 A KR 1020150165634A KR 20150165634 A KR20150165634 A KR 20150165634A KR 101747866 B1 KR101747866 B1 KR 101747866B1
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signal
unit
acceleration
frequency
sound
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KR1020150165634A
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KR20170060893A (en
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이은주
김윤중
마효일
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한림대학교 산학협력단
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/40—Detecting, measuring or recording for evaluating the nervous system
    • A61B5/4076—Diagnosing or monitoring particular conditions of the nervous system
    • A61B5/4082—Diagnosing or monitoring movement diseases, e.g. Parkinson, Huntington or Tourette
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A61B5/1116—Determining posture transitions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
    • A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
    • A61B5/6898—Portable consumer electronic devices, e.g. music players, telephones, tablet computers
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7235—Details of waveform analysis
    • A61B5/7253—Details of waveform analysis characterised by using transforms
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • G01P15/18—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration in two or more dimensions
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Abstract

The present invention relates to an apparatus and method for evaluating a Parkinson's disease index using an acceleration sensor, and an apparatus for evaluating a Parkinson's disease index according to the present invention includes an acceleration sensor unit for outputting an acceleration signal according to a motion of a hand of a subject, A time domain calculation unit for calculating a time domain signal value using the acceleration signal outputted from the acceleration sensor unit; and a time domain calculation unit for outputting a sound for generating the motion of the subject, A delay time calculation unit for calculating a delay time related to the sound signal of the sound unit using the acceleration signal output from the acceleration sensor unit and a control unit for controlling the sound signal of the sound unit, Analyzing the progress of the subject's hand using a signal, Up signal values of the calculated time domain to the delay by the delay time calculated by the time calculation unit analyzes the motion-related conditions, and provides call control evaluation that evaluates the state of Parkinson's disease. The present invention enables not only the presence of Parkinson's disease but also an initial analysis of the subject.

Description

[0001] The present invention relates to an apparatus and method for evaluating a Parkinson's disease index using an acceleration sensor,

The present invention relates to an apparatus and method for evaluating a Parkinson's disease index using an acceleration sensor, and more particularly, to an apparatus and a method for evaluating a Parkinson's disease index that enable early diagnosis of a subject's Parkinson's disease using an acceleration signal output from an acceleration sensor .

Parkinson's disease is caused by the gradual disappearance of dopamine neurons in the substantia nigra of the brain. The chronic progression of the nervous system characterized by progression of stasis, exercise restlessness (slow motion), stiffness, posture instability, It is a degenerative disease.

Most of the patients with Parkinson's disease develop clinical symptoms in their 60s. Among the symptomatic symptoms of Parkinson's disease, exercise-induced relaxation is one of the major causes of PD in patients with Parkinson's disease. Is a representative index for evaluating

Accordingly, a clinical scoring method for physicians to evaluate and evaluate the patient's motion in examining Parkinson's disease has been generally used. However, since these test methods are performed by subjective evaluations of physicians, there is a problem that the objectivity deterioration and minute changes due to the difference between the evaluator individual and the proficiency level can not be reflected.

Techniques for overcoming these problems and securing objectivity of evaluation have been developed. Japanese Unexamined Patent Publication No. 2009-291379 discloses such a device for evaluating Parkinson's disease. However, this Japanese Laid-Open Patent Application No. 2009-291379 is not suitable for initial diagnosis or the like because the bioinformation of continuous period is detected for a considerable period of time to evaluate the degree of severity or state change of Parkinson's disease.

Patent Document 1: JP-A-2009-291379

In order to solve the above-described problems, the present invention aims to provide an apparatus and method for evaluating a Parkinson's disease index which enables an initial analysis of a subject's Parkinson's disease using an acceleration signal outputted from an acceleration sensor.

According to an aspect of the present invention, there is provided an apparatus for evaluating a Parkinson's disease index, comprising: an acceleration sensor unit for outputting an acceleration signal according to movement of a hand of a subject; A time domain calculator for calculating a signal value in a time domain using the acceleration signal output from the acceleration sensor; a sound part for outputting a sound for generating a motion of a subject; A delay time calculator for calculating a delay time related to the sound signal of the sound unit using the acceleration signal output from the acceleration sensor unit, and a control unit for controlling the sound signal of the sound unit, Analyzing the progress of the subject's hand using the signal, Using the delay time calculated in the signal value and the delay time calculation unit for calculating a time-domain analysis of the movement-related symptoms, and provides call control evaluation that evaluates the state of Parkinson's disease.

The frequency-domain transform unit may transform the acceleration signal output from the acceleration sensor unit into a frequency domain using wavelet transform, and the control evaluating unit may use the wavelet- You can analyze progress.

The motion-related symptom may include exercise looseness, stiffness and posture instability, and the control evaluation unit may include an effective value and standard deviation of the acceleration, an effective value and a standard deviation of the velocity, an effective value and a standard deviation of the jerk, The at least one of the out-of-cycle cycle frequencies, and the at least one of the in-motion average delay, the out-of-a-cycle average frequency, the in-cycle cycle average frequency, At least one of vertical deviation, average horizontal deviation, standard deviation of speed, standard deviation of acceleration, standard deviation of jerk, mean delay time of inverse time, mean outward delay time, mean time of in- Can be analyzed.

The control evaluating unit may analyze the motion-related symptom using the acceleration signal output from the acceleration sensor unit obtained in the task of spinning? Stop? Stop? Outside? Stop repetitive task? .

The control evaluating unit may further analyze the motion-related symptom using the acceleration signal output from the acceleration sensor unit obtained from the bending task, the bending-in-rotation task, and the bending-out task.

The Parkinson's disease indicator evaluating apparatus may be configured as a mobile phone, and the mobile phone may further include a display unit for displaying an evaluation result evaluated by the control evaluating unit and a wireless transmitting unit for wirelessly transmitting the evaluation result.

According to another aspect of the present invention, there is provided a method for evaluating a Parkinson's disease index, comprising: a signal output step of outputting an acceleration signal according to a motion of a hand of a subject in an acceleration sensor unit; A time domain calculation step of calculating a time domain signal value by using the acceleration signal outputted from the signal output step; a time domain calculation step of calculating a time domain signal value by using the acceleration signal outputted from the signal output step, And a controller for analyzing the progress of the hand of the subject using the frequency signal converted in the frequency domain transforming step and calculating a signal value of the time domain calculated in the time domain calculating step The delay time calculated in the delay time calculation step is used to calculate a delay time By analyzing the symptoms, including the state evaluation step of evaluating the state of Parkinson's disease, it is possible to achieve the aforementioned objectives.

According to the above-described configuration, the present invention can readily perform initial analysis as well as the presence or absence of Parkinson's disease in a subject.

The present invention can obtain an accurate value by converting the acceleration signal into the frequency domain and analyzing the progress of the hand.

The present invention can more accurately analyze motion-related symptoms by using various signals converted from an acceleration signal as well as an acceleration signal.

1 is a block diagram of a device for evaluating a Parkinson's disease index according to an embodiment of the present invention.
Fig. 2 is a diagram showing three axes of the acceleration sensor unit which can be used in Fig. 1. Fig.
3 is a graph showing an acceleration signal waveform of a male in twenties repeatedly repeating in-out and out-of-continuation.
4 is a graph showing an acceleration signal waveform of a male person in the seventies who repeatedly performs in-out and out-of-continuation.
5 is a flowchart illustrating a method of evaluating a Parkinson's disease index according to an embodiment of the present invention.
FIG. 6 is a flowchart illustrating a hand-held in-motion and out-of-motion task according to an embodiment of the present invention.
7 is a view showing an embodiment of Fig.
FIG. 8 is a diagram showing the relationship between minutiae points extracted from the acceleration signals obtained according to the flowchart of FIG. 6 of the present invention and the main symptom indicators of Parkinson's disease.

Hereinafter, preferred embodiments of an apparatus and method for evaluating a Parkinson's disease index using an acceleration sensor according to the present invention will be described with reference to the accompanying drawings. In the following description of the present invention, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the technical scope of the present invention. Will be.

FIG. 1 is a block diagram of an apparatus for evaluating a Parkinson's disease index according to an embodiment of the present invention. FIG. 2 is a view showing three axes of an acceleration sensor unit which can be used in FIG. 1, FIG. 4 is a graph showing an acceleration signal waveform of a male person in the seventies who repeatedly repeats in-out and out-of-sequence.

The main symptoms of Parkinson's disease are resting tremor, bradykinesia, rigidity, and posture instability. These main symptoms of motion are evaluation index, which can be classified into the progression indicator, and the exercise looseness, stiffness, and posture instability index related to movement.

1, the Parkinson's disease marker evaluation apparatus includes an acceleration sensor unit 110, a low pass filter unit 120, an analog-to-digital conversion unit 130, a sound unit 140, a signal storage unit 150, A frequency domain transformer 160, a time domain calculator 170, a delay time calculator 180, and a control evaluator 190.

The acceleration sensor unit 110 is a sensor capable of detecting a change in speed per unit time, and can detect acceleration, vibration, impact, and the like. 2, the acceleration sensor unit 110 may be an acceleration sensor built in a smart phone. In this case, the acceleration sensor unit 110 may be a smart phone for X-axis (lateral), Y-axis (longitudinal) Can be measured. The acceleration signal output from the acceleration sensor unit 110 may be an acceleration signal due to pronation and / or supination of the hand.

The low-pass filter unit 120 removes unnecessary high-frequency components included in the acceleration signal output from the acceleration sensor unit 110 and outputs the filtered signals. The analog-to-digital conversion unit 130 passes the low- The low frequency acceleration signal can be sampled at a predetermined frequency, for example, 100 Hz and converted into a digital signal.

The sound unit 140 is related to the cognitive ability evaluation of the subject, and outputs a sound of a beep or the like to cause movement. The sound unit 140 can output sound at predetermined time intervals, for example, 10 seconds, and can also output a random sound focusing on the reaction of the subject.

In the signal storage unit 150, the digital acceleration signal converted and outputted by the analog-digital converter 130 is stored. Therefore, a series of acceleration waveforms of the subject to be analyzed can be stored in the signal storage unit 150 as one digital file. In the signal storage unit 150, a digital acceleration signal converted and outputted by the analog-digital conversion unit 130 is stored in association with the sound signal of the sound unit 140. Here, the signal storage unit 150 may be a DRAM or a flash memory.

FIG. 3 shows acceleration signal waveforms of a twenties man who repeatedly repeats in-out and out-of-continuation stored in such a digital file, and FIG. 4 shows an acceleration signal waveform of a man in his seventies . 3 and 4, the acceleration signals output from the acceleration sensor unit 110 are signals indicating different biometric information for each subject.

The frequency domain transformer 160 transforms a digital acceleration signal of one digital file stored in the signal storage 150 into a frequency domain. The frequency domain transform unit 160 uses the wavelet transform to convert the digital acceleration signal into the frequency domain, and obtains the effective value rsm from the transformed frequency function.

The time domain calculator 170 calculates the effective value and the standard deviation of the acceleration using the digital acceleration signal of one digital file stored in the signal storage unit 150. The time domain calculator 170 also calculates an average frequency, i.e., a number of counts per unit time, according to hand rotation and rotation using the digital acceleration signal of one digital file stored in the signal storage unit 150. [

The time domain calculator 170 may also integrate the digital acceleration signal of one digital file stored in the signal storage unit 150 and convert the digital acceleration signal into a velocity signal and calculate an effective value and a standard deviation from the velocity signal, The digital acceleration signal of one digital file stored in the memory 150 may be differentiated and converted into a jerk signal, and an effective value and a standard deviation may be calculated from the jerk signal. The time domain calculation section 170 can also integrate the velocity signal and convert it into a displacement signal, and calculate an average vertical displacement and an average horizontal signal from the converted displacement signal.

The delay time calculation unit 180 may calculate an average delay time in the circuit and an average delay time in the circuit with respect to the sound signal, and calculate an average delay time from the in-phase and out-of-phase signals.

The control evaluation unit 190 uses the frequency signal converted by the frequency domain transform unit 160 to analyze the progress of the subject's hand. Specifically, the frequency domain transform unit 160 transforms the wavelet- Can be used. The control evaluating unit 190 uses the signal value of the time domain calculated by the time domain calculating unit 170 to analyze the motion related symptoms. Specifically, the control evaluating unit 190 calculates the time And an average frequency according to out-of-sight motion. The control evaluating unit 190 can further analyze the motion-related symptoms using the average delay time in the round and the average round trip time calculated in the delay time calculating unit 180. [ As a result, the apparatus for evaluating a PD can measure not only the presence of Parkinson's disease but also an initial analysis.

1, the frequency domain transform unit 160 and the time domain calculator 170 analyze the frequency domain and the time domain using the acceleration signal stored in the signal storage unit 150. However, the acceleration sensor unit 110 can be used as it is.

1 may be one device such as a smart phone, but depending on the implementation, the acceleration sensor unit 110 and other components may be wired or wirelessly connected devices.

5 is a flowchart illustrating a method of evaluating a Parkinson's disease index according to an embodiment of the present invention.

For example, the subject drives the hands-on and out-of-home exercise apps (hereinafter referred to as "exercise task apps") of the smartphone's hands (S502). The exercise task app displays a bill on the display unit, and when the subject reads the bill and agrees with the bill, the exercise task app can input predetermined information.

The subject can then, for example, grasp the smartphone by hand (S504). In this case, the subject can put the hand holding the smartphone on the table.

When the motion data acquisition mode is started in the exercise task app, the subject can hold the smartphone for a predetermined time, for example, 30 seconds, while holding the smartphone. In this state, the acceleration sensor unit 110 outputs the acceleration signal, so that the acceleration signal in the stopped state is stored in the signal storage unit 150 (S506).

For example, the subject can repeat the inward and outward motion of the hand for a predetermined time, for example, 30 seconds, together with the beep sound. The acceleration sensor unit 110 outputs an acceleration signal corresponding to the motion, and the acceleration signal of the motion state is stored in the signal storage unit 150 (S508).

In addition, the subject can repeat the process of moving his / her hand to the inside of the circle at first when the beep sounds and moving the hands out of the next beep for a predetermined time, for example. The acceleration sensor unit 110 outputs an acceleration signal according to the motion, and the acceleration signal in the motion state is stored in the signal storage unit 150 together with the sound signal (S510).

When the motion data acquisition mode is completed in the motion task app, the frequency domain converter 160 converts the digital acceleration signal of one digital file stored in the signal storage unit 150 into the frequency domain (S512). The frequency domain transform unit 160 uses the wavelet transform to transform the digital acceleration signal into the frequency domain, and calculates the effective value rsm from the transformed frequency function.

The time domain calculator 170 calculates the time domain signal values using the digital acceleration signal of one digital file stored in the signal storage unit 150 (S514). The time domain calculator 170 calculates the effective value and the standard deviation of the acceleration using the digital acceleration signal of one digital file stored in the signal storage unit 150 and also calculates an average frequency Calculate the number of counts per unit time.

The time domain calculator 170 may also integrate the digital acceleration signal of one digital file stored in the signal storage unit 150 and convert the digital acceleration signal into a velocity signal and calculate an effective value and a standard deviation from the velocity signal, The digital acceleration signal of one digital file stored in the memory 150 may be differentiated and converted into a jerk signal, and an effective value and a standard deviation may be calculated from the jerk signal. The time domain calculation section 170 can also integrate the velocity signal and convert it into a displacement signal, and calculate an average vertical displacement and an average horizontal signal from the converted displacement signal.

The delay time calculation unit 180 calculates an average delay time and an average delay time in the cycle using the sound signal stored in the signal storage unit 150 and the related motion data (S516) The average delay time can be calculated.

The control evaluating unit 190 analyzes the progress of the hand of the subject using the frequency signal converted by the frequency-domain transforming unit 160, specifically using the frequency-domain-transformed frequency signal in the frequency-domain transforming unit 160 (S518). The control evaluating unit 190 uses the signal value of the time domain calculated by the time domain calculating unit 170 to calculate an average frequency according to the motion of the hand and the out of the hand calculated by the time domain calculating unit 170 To analyze the motion-related symptoms (S520). The control evaluating unit 190 can further analyze the motion-related symptoms using the average delay time in the round and the average round trip time calculated in the delay time calculating unit 180. [

The exercise task app can then display the evaluation result on the display unit of the smartphone (S522).

FIG. 6 is a flowchart illustrating a hand in-and-out-out-of-motion task according to an embodiment of the present invention, and FIG. 7 is a view illustrating an embodiment of FIG.

When the user clicks on the exercise task application stored in the smartphone used as the Parkinson's disease index evaluation device, the operation system of the smart phone drives the exercise task application (S602).

The exercise task app displays the bill as shown in FIG. 7A on the display unit, the subject agrees with the bill (S604), and clicks the start button at the bottom, the exercise task app is displayed on the display unit An information input screen as shown in FIG. 7 (b) is displayed (S606). Name, Age, Gender, Hand usage, etc. may be displayed on the information input screen. The user inputs information according to the items of the information input screen. If such information has already been input, the information input on the information input screen is also displayed. When the user clicks the register button after inputting the information according to the items of the information input screen, the input information is stored in the signal storage unit.

When the input information is stored, the exercise task app displays the exercise task mode screen as shown in FIG. 7C on the display unit (S608). The user can select either Flat or Up displayed on the motion task mode screen. A flat means that the forearm and hand start on the table, and the up refers to the forearm starting with the hand off the table.

Whether the user selects the flat or the up, the exercise task app first performs a resting task for a predetermined time, for example, 30 seconds (S610). If the user does not select a flat after the stop task is completed (S612), the exercise task app outputs a beep through the sound unit 140, thereby performing an LRRR task for a predetermined time, for example, 30 seconds, The operation is repeatedly performed in the order of stop → outdoors → stop (S614). In this case, the operation after the stop can be started randomly with the beep of the sound unit 140, and the acceleration sensor output from the acceleration sensor unit 110 is stored in the signal storage unit 150.

After the completion of the LRRR task, the exercise task app outputs a beep through the sound unit 140, thereby causing the operation to be repeatedly performed in the LRR task for a predetermined time, for example, 30 seconds (i.e., S616). In this case, the operation after the stop can be started randomly with the beep of the sound unit 140, and the acceleration sensor output from the acceleration sensor unit 110 is stored in the signal storage unit 150.

After the completion of the LRR task, the exercise task app outputs a beep through the sound unit 140, thereby causing the operation to be repeatedly performed in the LR task for a predetermined time, for example, 30 seconds, ).

However, if the user selects a flat after the stop task is completed (S612), the exercise task app outputs a beep through the sound unit 140 to bend the arm from the table for a predetermined time, (Operation S620).

After the bending task is completed, the exercise task app outputs a beep through the sound unit 140, thereby performing the bending out-of-bend task, that is, the bending operation? After the out-of-bend out-of-task is completed, the motion task app outputs a beep through the sound unit 140, thereby performing the bend in-turn task, that is, bending operation?

Then, the exercise task app executes the LRRR task (S614), performs the LRR task (S616), and performs the LR task (S618).

Meanwhile, the exercise task app can evaluate the Parkinson's disease indicators using the acceleration signals obtained from the stop task, the LRRR task, the LRR task, and the LR task, the bending task, the out-of-bending task, and the bending in-wheel task, respectively (S626).

The exercise task app displays the Parkinson's disease evaluation result on the display unit (S628), and transmits the evaluation result through a wireless network such as a data network (S630).

FIG. 8 is a diagram showing a relationship between feature points extracted from acceleration signals obtained according to the flowchart of FIG. 6 of the present invention and the main symptom indicators of Parkinson's disease.

Stability progression, one of the main symptoms of Parkinson's disease, can be assessed from the rms value of the wavelet transform. The rms value includes the rms value and standard deviation of velocity, rms value and standard deviation of acceleration, rms value and standard deviation of jerk, Can be evaluated from the average frequency of the in-and-out cycle. Stiffness can be assessed from the average delay in the circuit, the average delay in the circuit, the average delay in the circuit and the circuit in the circuit, and the average frequency of the circuit in and out of the circuit. Positional instability is the average vertical deviation, average horizontal deviation, Standard deviation, standard deviation of jerk, mean delay in circulation, mean delay in conception, mean delay in conception and conception, and average frequency of in-and-out cycle.

The Unified Parkinson's Disease Rating Scale (UPDRS) is a representative Parkinson's disease scale that was first published and used in 1987.

Symptom responsiveness(%) Specificity (%) Steady-state progress 87.5 92.0 Exercise lover 100.0 87.5 Rigidity 75.0 100.0 Instability of posture 80.0 89.28 Average 85.63 89.07

Table 1 above shows the comparison of the postural stabilization progression, exercise restraint, rigidity and posture instability obtained according to the embodiment of the present invention to the UPDRS clinical scale, with an average sensitivity of 85.63% and a specificity of 89.07% .

The scope of protection of the present invention should be interpreted according to the claims. It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit and scope of the invention as defined by the appended claims. It should be interpreted that it is included in the scope of right.

110: acceleration sensor unit 120: low pass filter unit
130: Analog-to-digital conversion unit 140:
150: Signal storage unit 160: Frequency domain transform unit
170: time domain calculation unit 180: delay time calculation unit
190: Control evaluation section

Claims (8)

An acceleration sensor unit for outputting an acceleration signal according to a motion of a hand of the subject,
A frequency domain conversion unit for converting the acceleration signal output from the acceleration sensor unit into a frequency domain,
A time domain calculator for calculating a time domain signal value using the acceleration signal outputted from the acceleration sensor;
A sound part for outputting a sound for causing movement of the subject,
A delay time calculator for calculating a delay time associated with the sound signal of the sound unit using the acceleration signal output from the acceleration sensor unit,
And a controller for controlling the sound signal of the sound unit, analyzing the progress of the hand of the subject using the frequency signal converted by the frequency domain converter, calculating a signal value of the time domain calculated by the time domain calculator, And a control evaluating unit for evaluating the state of Parkinson's disease by analyzing the motion-related symptoms using the calculated delay time. The method according to claim 1,
The frequency domain transform unit may convert the acceleration signal output from the acceleration sensor unit into a frequency domain using wavelet transform,
Wherein the control evaluating unit analyzes the progress of the hand of the subject using the frequency-domain-converted frequency signal in the frequency-domain transforming unit. 3. The method of claim 2,
The motion-related symptoms include exercise looseness, rigidity and posture instability,
The control evaluating unit analyzes the exercise tracker using at least one of an effective value and standard deviation of acceleration, an effective value and a standard deviation of velocity, an effective value and standard deviation of jerk, an average value of a cycle time cycle, The average deviation, the mean deviation of the mean speed, the standard deviation of the acceleration, the standard deviation of the acceleration, the jerk of the jerk Wherein the postural instability is analyzed using at least one of a standard deviation, a mean time delay, an out-of-period average delay, an in-cycle average frequency, and an out-of-cycle average frequency. 4. The method according to any one of claims 1 to 3,
The control evaluation unit analyzes the motion-related symptom using the acceleration signal output from the acceleration sensor unit obtained in the task of spinning? Stop? Stop? Outside? Stop repetitive task? Gt; < tb >< / TABLE > 5. The method of claim 4,
Wherein the control evaluating unit analyzes the motion related symptoms by further using an acceleration signal output from the acceleration sensor unit obtained in the bending task, the bending in-wheel task, and the out-of-bend task. 4. The method according to any one of claims 1 to 3,
The device for evaluating a Parkinson's disease index comprises a mobile phone,
Wherein the mobile phone further comprises a display unit for displaying the evaluation result evaluated by the control evaluation unit and a wireless transmission unit for wirelessly transmitting the evaluation result. A signal output step of outputting an acceleration signal according to the movement of a hand of the subject in the acceleration sensor unit,
A frequency domain conversion step of converting the acceleration signal output from the signal output step into a frequency domain;
A time domain calculation step of calculating a time domain signal value using the acceleration signal outputted from the signal output step;
A delay time calculation step of calculating a delay time with respect to the sound signal of the sound part by using the acceleration signal outputted from the signal output step;
And analyzing the progress of the hand of the subject by using the frequency signal converted in the frequency domain transforming step and using the signal value of the time domain calculated in the time domain calculating step and the delay time calculated in the delay time calculating step And evaluating the condition of Parkinson's disease by analyzing the symptoms related to the movement. 8. The method of claim 7,
Wherein the frequency domain transforming step transforms the acceleration signal output from the signal outputting step into a frequency domain using wavelet transform,
Wherein the evaluating step analyzes the progress of the hand of the subject using the frequency-converted wavelet-transformed frequency signal in the frequency-domain transforming step.
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KR102087066B1 (en) * 2018-02-28 2020-03-10 한림대학교 산학협력단 Quantification system and method of gait test to extract features of freezing of gait sysptom of parkinson's disease
CN111870248A (en) * 2020-06-05 2020-11-03 安徽建筑大学 Motion state feature extraction and identification method based on 3D acceleration signal
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