CN112674762A - Parkinson tremble evaluation device based on wearable inertial sensor - Google Patents
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Abstract
The invention provides a Parkinson tremor evaluation device based on a wearable inertial sensor, which comprises: the design module is used for designing an action paradigm for detecting the tremor index; the selection module is used for selecting each evaluation index for evaluating the Parkinson's tremor symptom; the wearable inertial sensor is used for acquiring triaxial acceleration signals of a test object under different action paradigms; the preprocessing module is used for preprocessing the triaxial acceleration signal to obtain a three-dimensional effective tremor signal; the integrated dimension reduction module is used for performing integrated dimension reduction processing on the three-dimensional effective tremor signal to obtain a one-dimensional effective tremor signal; and the calculation module is used for respectively calculating index result information of each evaluation index according to the one-dimensional effective tremor signal so as to evaluate the Parkinson tremor of the test object. The invention can carry out quantitative measurement on Parkinson's tremor, gives objective evaluation results, can be used for clinical diagnosis of Parkinson's disease as an aid, increases the diagnosis accuracy and reduces the burden of medical care personnel and patients.
Description
Technical Field
The invention belongs to the technical field of tremor assessment, and particularly relates to a Parkinson tremor assessment device based on a wearable inertial sensor.
Background
Parkinson's disease is a common chronic degenerative disease of nervous system, and the clinical manifestations include motor symptoms such as bradykinesia, myotonia, tremor and the like, and non-motor symptoms such as hyposmia, pain and the like. Tremor, one of the important items for detecting parkinson's disease, is tested in the world motor disability association parkinson's disease composite score scale (MDS-UPDRS) mainly by the resting tremor item, postural tremor item, and the action tremor item. At present, tremor assessment of parkinson's disease is mainly achieved by means of subjective judgment of a specialist. However, the results obtained by this method are greatly influenced by the level of the test physician and subjective factors, and at the same time, the whole evaluation process consumes a lot of time and effort by the medical care workers and the patients.
With the progress of microelectronics and sensor technologies and the wide application of computer technologies, the means and applications for quantitatively measuring the level of motor symptoms by using instruments and further evaluating the state of the Parkinson's disease are increasing. Wearable inertial sensors have been used in the diagnosis of parkinsonian motion symptoms such as gait, joint flexibility, and the like.
Disclosure of Invention
The invention aims to solve at least one technical problem in the prior art and provides a Parkinson tremor evaluation device based on a wearable inertial sensor.
The invention provides a Parkinson tremor evaluation device based on a wearable inertial sensor, which comprises:
the device comprises a design module, a detection module and a display module, wherein the design module is used for designing each action mode for detecting the tremor index, and the action modes comprise a static tremor action mode, a postural tremor action mode and an action tremor action mode;
the system comprises a selection module, a judgment module and a display module, wherein the selection module is used for selecting each evaluation index for evaluating the Parkinson's tremor symptom, and the evaluation indexes comprise time domain indexes and frequency domain indexes;
the wearable inertial sensor is used for acquiring triaxial acceleration signals of the test object under different action paradigms;
the preprocessing module is used for preprocessing the triaxial acceleration signal to obtain a three-dimensional effective tremor signal;
the integration dimension reduction module is used for performing integration dimension reduction processing on the three-dimensional effective tremor signal to obtain a one-dimensional effective tremor signal;
and the calculation module is used for respectively calculating index result information of each evaluation index according to the one-dimensional effective tremor signal so as to evaluate the Parkinson tremor of the test object.
In some optional embodiments, the time domain indicator comprises a tremor amplitude, and the calculation module is configured to:
removing the influence of a gravity baseline on acceleration data in the one-dimensional effective tremor signal, calculating an integral, performing linear fitting on an integral result to obtain a first trend line, and removing the first trend line in a segmented manner to obtain a speed signal;
and calculating integral of the speed signal, performing linear fitting on an integral result to obtain a second trend line, and removing the second trend line in a segmented manner to obtain a tremor amplitude curve for representing the tremor amplitude.
In some optional embodiments, the time domain indicator further comprises a tremor acceleration root mean square, and the calculation module is configured to:
filtering the one-dimensional effective tremor signal, wherein a filtering passband is a neighborhood of the center frequency of the one-dimensional effective tremor signal, and the tremor acceleration root mean square is obtained by calculation according to the following relational expression (1):
wherein RMS is the tremor acceleration root mean square, N is the number of the one-dimensional effective tremor signals after filtering, aiIs the ith one-dimensional effective tremor signal in the set of N filtered one-dimensional tremor signals.
In some optional embodiments, the frequency domain indicator comprises a tremor peak power and a tremor peak frequency, and the calculation module is configured to:
calculating the power spectrum of the one-dimensional effective tremor signal by adopting periodogram estimation or Welch estimation according to the one-dimensional effective tremor signal; wherein,
the tremor peak power is the peak of the power spectrum of the one-dimensional effective tremor signal;
the tremor peak frequency is the frequency corresponding to the peak of the power spectrum of the one-dimensional effective tremor signal.
In some optional embodiments, the frequency domain indicator further comprises a tremor mean power spectrum, and the calculation module is configured to:
calculating the tremor average power spectrum according to the one-dimensional effective tremor signal by adopting the following relation (2):
wherein meanPSD is the average power spectrum of tremor, fiIs the frequency of the ith one-dimensional effective tremor signal, and p is the discrete power spectrum of the one-dimensional effective tremor signal.
In some optional embodiments, the frequency domain indicator further comprises a tremor power spectral entropy, and the calculation module is configured to:
calculating the tremor power spectrum entropy by adopting the following relation (3) according to the one-dimensional effective tremor signal:
wherein E is the tremor power spectrum entropy, p (f)i) Is the frequency f of the ith one-dimensional effective tremor signaliThe corresponding normalized power spectrum, k, is the discrete frequency range of the one-dimensional effective tremor signal.
In some alternative embodiments, the resting tremor action pattern comprises a resting action and a resting standing action;
the postural tremor motion paradigm comprises an arm stretching motion;
the motion tremor motion pattern includes a finger-nose slow motion.
In some optional embodiments, the sedentary action is in particular: the test subject sits on the chair, naturally places both hands on the legs, and stands still for 30-60 seconds;
the static standing action is specifically as follows: the heels of the tested object are closed, the two arms naturally droop, and the body does not contact any object;
the arm stretching action specifically comprises the following steps: the test subject sits on a chair, and the arms of both hands are lifted in front of the chest and kept still for 30 seconds; and the number of the first and second groups,
the slow finger-nose action of the hand is as follows: the test object sits on the chair, the arms stretch to the farthest extent firstly, then the arms slowly turn back to point to the nose tip of the test object, the fingers slowly point to the nose and act for 3-5 times, and the two hands act respectively.
The Parkinson tremor evaluation device based on the wearable inertial sensor can quantitatively measure the Parkinson tremor, give objective evaluation results, can be used for clinically diagnosing the Parkinson's disease as an auxiliary, increases the accuracy of diagnosis, and reduces the burden of medical staff and patients.
Drawings
Fig. 1 is a schematic structural diagram of a parkinson's tremor evaluation device based on a wearable inertial sensor according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a three-dimensional effective tremor signal and a one-dimensional effective tremor signal according to another embodiment of the present invention;
FIG. 3 is a flow chart of an algorithm for tremor amplitude profile according to another embodiment of the present invention;
FIG. 4 is a schematic diagram of the power spectrum of a one-dimensional effective tremor signal according to another embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, the present invention provides a wearable inertial sensor-based parkinson's tremor assessment apparatus 100, wherein the apparatus 100 includes a design module 110, a selection module 120, a wearable inertial sensor 130, a preprocessing module 140, an integrated dimension reduction module 150, and a calculation module 160.
The design module 110 is configured to design each motion pattern of the detected tremor index, where the motion patterns include a resting tremor motion pattern, a postural tremor motion pattern, and an actional tremor motion pattern.
Illustratively, in the present module, corresponding motion paradigms, including a stationary tremor motion paradigms, a postural tremor motion paradigms and an action tremor motion paradigms, are respectively designed for the detection requirements of the MDS-UPDRS for the stationary tremor, postural tremor and action tremor. The static tremor motion paradigm is mainly used to collect tremor signals of the hands and other parts of the body of the test subject. The postural tremor motion paradigm is mainly used to collect tremor signals of the hands of the test subject. The action tremor action paradigm is mainly used to collect tremor signals of the hands of the test subject.
Preferably, the resting tremor movement pattern comprises a resting movement and a resting standing movement.
Specifically, the resting motion may be: the test subjects were seated in a chair with both hands naturally on the legs and held still for 30-60 seconds. The stationary standing action may be: the heels of the tested object are closed, the two arms naturally droop, and the body does not contact any object.
Preferably, the postural tremor motion paradigm comprises an arm stretching motion.
Specifically, the arm stretching action may be: the test subjects were seated in a chair with both arms flat against the chest and held stationary for 30 seconds.
Preferably, the tremor action pattern includes a finger-nose movement.
Specifically, the slow finger-nose movement of the hand may be: the test subject sits on the chair, the arms are firstly extended to be as far as possible, then the arms are slowly turned back to point to the nose tip of the test subject, the fingers and the nose slowly move for 3-5 times, and the two hands respectively move.
The selection module 120 is configured to select each evaluation index for evaluating the parkinsonism tremor symptom, where the evaluation index includes a time domain index and a frequency domain index.
Illustratively, in the module, the tremor-related time domain parameters and frequency domain parameters are mainly selected as evaluation indexes. The time domain indexes mainly comprise tremor amplitude, acceleration related quantity and statistical parameters, and specifically comprise parameters such as tremor amplitude curves and acceleration root mean square. The frequency domain indexes mainly comprise frequency, power spectrum and related quantity, and specifically comprise tremor peak power, peak frequency, average power spectral density, power spectral entropy and the like. Of course, those skilled in the art may select other parameters as the evaluation index according to actual needs, which is not limited in this embodiment.
The wearable inertial sensor 130 is used for acquiring triaxial acceleration signals of the test object under different action paradigms.
For example, wearable inertial sensor 130 may employ a wearable inertial sensor with a range of ± 5 g. Of course, those skilled in the art can select wearable inertial sensors with other ranges according to actual needs, which is not limited by the embodiment.
For example, the subject may place the wearable inertial sensor 130 on the wrist, chest, waist, thigh, etc. of the subject in the resting tremor motion paradigm to acquire tremor signals from the hand and other parts of the body, such as the chest, waist, thigh, etc. The wearable inertial sensor 130 can be placed only at the wrist of the test subject in the postural tremor motion pattern and the action tremor motion pattern of the test subject to acquire tremor signals of the hand of the test subject. In this embodiment, place the position at the test object wrist through placing wearable inertial sensor, can make the process of wearing convenient high efficiency more.
The preprocessing module 140 is used for preprocessing the triaxial acceleration signal to obtain a three-dimensional effective tremor signal.
Illustratively, in this module, the step of pre-processing consists essentially of removal of non-tremor signals and screening for non-tremor conditions.
Specifically, when the non-tremor signal is removed, a 2Hz-10Hz zero-phase band-pass filter can be adopted to remove the normal action signal and the high-frequency interference signal of the slow movement of the human body. When non-tremor condition screening is carried out, a segmented windowing method can be used for obtaining low-frequency-band energy of signal segmentation, then a threshold value is set, and if the low-frequency-band energy of a certain segment of signal is higher than the set threshold value, data of the segment of signal is excluded so as to prevent severe action of a human body from influencing tremor evaluation.
In this embodiment, a three-dimensional effective tremor signal can be obtained by preprocessing the acquired triaxial acceleration signal such as non-tremor signal removal and non-tremor condition screening. Of course, those skilled in the art may also perform preprocessing on the three-axis acceleration signal in other ways according to actual needs to obtain a three-dimensional effective tremor signal, which is not limited in this embodiment.
The integration dimension reduction module 150 is configured to perform integration dimension reduction processing on the three-dimensional effective tremor signal to obtain a one-dimensional effective tremor signal.
Illustratively, as shown in fig. 2, in the present module, the three-dimensional effective tremor signal can be subjected to an integrated dimension reduction process by using a principal component analysis method to obtain a one-dimensional effective tremor signal.
In the embodiment, the stability and the accuracy of the algorithm in the subsequent module can be improved by carrying out integration and dimension reduction processing on the three-dimensional effective tremor signal.
The calculation module 160 is configured to calculate index result information of each evaluation index according to the one-dimensional effective tremor signal, so as to evaluate the parkinson tremor of the test object.
Preferably, the time domain indicator comprises tremor amplitude.
Specifically, the tremor amplitude is the amplitude of a one-dimensional effective tremor signal obtained by the wearable inertial sensor, and can be represented by a tremor amplitude curve.
Preferably, the calculation module 160 is configured to:
and removing the influence of a gravity baseline on the acceleration data in the one-dimensional effective tremor signal, calculating an integral, performing linear fitting on an integral result to obtain a first trend line, and removing the first trend line in a segmented manner to obtain a speed signal.
For example, as shown in fig. 3, firstly, the acceleration data a (t) in the one-dimensional effective tremor signal is removed from the influence of the gravity baseline, the integral is calculated, the integration result is linearly fitted to obtain a first trend line, and then the trend line is removed in a segmented manner to obtain the velocity signal v (t). In this embodiment, removing the trend line, i.e., removing the accumulated error caused by the acceleration error, can improve the accuracy of the algorithm of this module.
And calculating integral of the speed signal, performing linear fitting on an integral result to obtain a second trend line, and removing the second trend line in a segmented manner to obtain a tremor amplitude curve for representing the tremor amplitude.
Illustratively, as shown in fig. 3, first, an integral is calculated for the velocity signal v (t), a linear fitting is performed on the integral result to obtain a second trend line, and then the trend line is removed in a segmented manner to obtain a tremor amplitude curve for representing tremor amplitude.
The embodiment can express the tremor condition of the test object simply and obviously through the tremor amplitude curve.
Preferably, the time domain indicator further comprises a tremor acceleration root mean square.
Specifically, the tremor acceleration root mean square is the root mean square of the acceleration of the one-dimensional effective tremor signal obtained by the wearable inertial sensor, and can reflect the tremor amplitude. The magnitude of the root mean square of the tremor acceleration is positively correlated with the severity of the tremor.
Preferably, the calculation module 160 is configured to:
and carrying out filtering processing on the one-dimensional effective tremor signal, wherein a filtering passband is a neighborhood of the center frequency of the one-dimensional effective tremor signal.
In this module, if a one-dimensional effective tremor signal is used directly, a large disturbance is introduced. Therefore, the module carries out filtering processing on the one-dimensional effective tremor signal, and can ensure the effectiveness of the filtered one-dimensional effective tremor signal.
Calculating the tremor acceleration root mean square according to the following relation (1):
wherein RMS is the tremor acceleration root mean square, N is the number of the one-dimensional effective tremor signals after filtering, aiIs the ith one-dimensional effective tremor signal in the set of N filtered one-dimensional tremor signals.
Preferably, the frequency domain indicator comprises a tremor peak power and a tremor peak frequency.
Specifically, the tremor peak power is a peak of a power spectrum extracted after a power spectrum of a one-dimensional effective tremor signal obtained by the wearable inertial sensor is calculated. The tremor peak frequency is the frequency corresponding to the peak value of the extracted power spectrum after the power spectrum of the one-dimensional effective tremor signal obtained by the wearable inertial sensor is calculated. In Parkinson's patients, the tremor peak frequency is usually within a certain range, and its peak energy is positively correlated with the severity of tremor. For example, the tremor peak frequency may be in the range of 4Hz to 8Hz, or in other ranges, and the present embodiment is not limited thereto.
Preferably, the calculation module 160 is configured to:
calculating the power spectrum of the one-dimensional effective tremor signal by adopting periodogram estimation or Welch estimation according to the one-dimensional effective tremor signal; wherein,
the tremor peak power is the peak of the power spectrum of the one-dimensional effective tremor signal;
the tremor peak frequency is the frequency corresponding to the peak of the power spectrum of the one-dimensional effective tremor signal.
Illustratively, as shown in fig. 4, a power spectrum of the one-dimensional effective tremor signal is calculated by Welch estimation according to the one-dimensional effective tremor signal, the obtained tremor peak power is the peak in fig. 4, and the tremor peak frequency is the frequency corresponding to the peak in fig. 4.
Preferably, the frequency domain indicator further comprises a tremor mean power spectrum.
Specifically, the tremor average power spectrum is the average density of the power spectrum in the central frequency range extracted after the power spectrum of the one-dimensional effective tremor signal obtained by the wearable inertial sensor is calculated. The mean power spectrum of tremor is positively correlated with the severity of tremor.
Preferably, the calculation module 160 is configured to:
calculating the tremor average power spectrum according to the one-dimensional effective tremor signal by adopting the following relation (2):
wherein meanPSD is the average power spectrum of tremor, fiIs the frequency of the ith one-dimensional effective tremor signal, and p is the discrete power spectrum of the one-dimensional effective tremor signal.
For example, since the frequency of the one-dimensional effective tremor signal is mostly in the range of 4Hz to 8Hz, in this step, the average power spectrum of the one-dimensional effective tremor signal with the frequency in the range of 4Hz to 8Hz is selected as the tremor average power spectrum, which can reflect the magnitude of the tremor. That is, in the above formula (2), the discrete power spectrum p of the one-dimensional effective tremor signal may range from 4Hz to 8 Hz.
Preferably, the frequency domain indicator further comprises tremor power spectral entropy.
Specifically, the tremor power spectrum entropy is a measure of the degree of confusion of the tremor signal power spectrum components. The tremor of the Parkinson patients is unstable due to the reduction of the motor control function of the Parkinson patients, and the tremor power spectrum entropy value of the Parkinson patients is increased.
Preferably, the calculation module 160 is configured to:
calculating the tremor power spectrum entropy by adopting the following relation (3) according to the one-dimensional effective tremor signal:
wherein E is the tremor power spectrum entropy, p (f)i) Is the frequency f of the ith one-dimensional effective tremor signaliThe corresponding normalized power spectrum of the power spectrum,k is the discrete frequency range of the one-dimensional effective tremor signal.
The embodiment can quantitatively measure the Parkinson's tremor, give objective evaluation results, and be used for clinical diagnosis of the Parkinson's disease as an aid, so that the diagnosis accuracy is increased, and the burden of medical staff and patients is reduced.
It will be understood that the above embodiments are merely exemplary embodiments taken to illustrate the principles of the present invention, which is not limited thereto. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit and substance of the invention, and these modifications and improvements are also considered to be within the scope of the invention.
Claims (8)
1. A wearable inertial sensor-based Parkinson's tremor assessment device, the device comprising:
the device comprises a design module, a detection module and a display module, wherein the design module is used for designing each action mode for detecting the tremor index, and the action modes comprise a static tremor action mode, a postural tremor action mode and an action tremor action mode;
the system comprises a selection module, a judgment module and a display module, wherein the selection module is used for selecting each evaluation index for evaluating the Parkinson's tremor symptom, and the evaluation indexes comprise time domain indexes and frequency domain indexes;
the wearable inertial sensor is used for acquiring triaxial acceleration signals of the test object under different action paradigms;
the preprocessing module is used for preprocessing the triaxial acceleration signal to obtain a three-dimensional effective tremor signal;
the integration dimension reduction module is used for performing integration dimension reduction processing on the three-dimensional effective tremor signal to obtain a one-dimensional effective tremor signal;
and the calculation module is used for respectively calculating index result information of each evaluation index according to the one-dimensional effective tremor signal so as to evaluate the Parkinson tremor of the test object.
2. The apparatus of claim 1, wherein the time domain indicator comprises a tremor amplitude, and wherein the computing module is configured to:
removing the influence of a gravity baseline on acceleration data in the one-dimensional effective tremor signal, calculating an integral, performing linear fitting on an integral result to obtain a first trend line, and removing the first trend line in a segmented manner to obtain a speed signal;
and calculating integral of the speed signal, performing linear fitting on an integral result to obtain a second trend line, and removing the second trend line in a segmented manner to obtain a tremor amplitude curve for representing the tremor amplitude.
3. The apparatus of claim 2, wherein the time domain indicator further comprises a tremor root mean square, and wherein the calculation module is configured to:
filtering the one-dimensional effective tremor signal, wherein a filtering passband is a neighborhood of the center frequency of the one-dimensional effective tremor signal, and the tremor acceleration root mean square is obtained by calculation according to the following relational expression (1):
wherein RMS is the tremor acceleration root mean square, N is the number of the one-dimensional effective tremor signals after filtering, aiIs the ith one-dimensional effective tremor signal in the set of N filtered one-dimensional tremor signals.
4. The apparatus of any one of claims 1 to 3, wherein the frequency domain indicator comprises a tremor peak power and a tremor peak frequency, and wherein the calculation module is configured to:
calculating the power spectrum of the one-dimensional effective tremor signal by adopting periodogram estimation or Welch estimation according to the one-dimensional effective tremor signal; wherein,
the tremor peak power is the peak of the power spectrum of the one-dimensional effective tremor signal;
the tremor peak frequency is the frequency corresponding to the peak of the power spectrum of the one-dimensional effective tremor signal.
5. The apparatus of claim 4, wherein the frequency domain indicator further comprises a trembling mean power spectrum, and wherein the calculation module is configured to:
calculating the tremor average power spectrum according to the one-dimensional effective tremor signal by adopting the following relation (2):
wherein meanPSD is the average power spectrum of tremor, fiIs the frequency of the ith one-dimensional effective tremor signal, and p is the discrete power spectrum of the one-dimensional effective tremor signal.
6. The apparatus of claim 5, wherein the frequency domain indicator further comprises a tremor power spectral entropy, and wherein the calculation module is configured to:
calculating the tremor power spectrum entropy by adopting the following relation (3) according to the one-dimensional effective tremor signal:
wherein E is the tremor power spectrum entropy, p (f)i) Is the frequency f of the ith one-dimensional effective tremor signaliThe corresponding normalized power spectrum, k, is the discrete frequency range of the one-dimensional effective tremor signal.
7. The device according to any one of claims 1 to 3,
the static tremor motion paradigm comprises a static sitting motion and a static standing motion;
the postural tremor motion paradigm comprises an arm stretching motion;
the motion tremor motion pattern includes a finger-nose slow motion.
8. The apparatus of claim 7,
the sitting movement is as follows: the test subject sits on the chair, naturally places both hands on the legs, and stands still for 30-60 seconds;
the static standing action is specifically as follows: the heels of the tested object are closed, the two arms naturally droop, and the body does not contact any object;
the arm stretching action specifically comprises the following steps: the test subject sits on a chair, and the arms of both hands are lifted in front of the chest and kept still for 30 seconds; and the number of the first and second groups,
the slow finger-nose action of the hand is as follows: the test object sits on the chair, the arms stretch to the farthest extent firstly, then the arms slowly turn back to point to the nose tip of the test object, the fingers slowly point to the nose and act for 3-5 times, and the two hands act respectively.
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| CN113616194A (en) * | 2021-08-05 | 2021-11-09 | 苏州小蓝医疗科技有限公司 | Device and method for monitoring hand tremor frequency and intensity |
| WO2023179218A1 (en) * | 2022-03-24 | 2023-09-28 | 凝动万生医疗科技(武汉)有限公司 | Quantitative evaluation method and system for static tremor of dyskinesia diseases |
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