CN107361773A - For detecting, alleviating the device of Parkinson's abnormal gait - Google Patents
For detecting, alleviating the device of Parkinson's abnormal gait Download PDFInfo
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- 206010017577 Gait disturbance Diseases 0.000 title claims abstract description 84
- 230000005021 gait Effects 0.000 claims abstract description 89
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- 238000004364 calculation method Methods 0.000 claims abstract description 73
- 238000012937 correction Methods 0.000 claims abstract description 32
- 238000012360 testing method Methods 0.000 claims abstract description 25
- 230000005856 abnormality Effects 0.000 claims abstract description 7
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- 239000003814 drug Substances 0.000 claims description 6
- 230000003183 myoelectrical effect Effects 0.000 claims description 6
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- 206010056242 Parkinsonian gait Diseases 0.000 claims description 5
- 230000004936 stimulating effect Effects 0.000 claims description 5
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- 210000004744 fore-foot Anatomy 0.000 claims description 3
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/389—Electromyography [EMG]
- A61B5/395—Details of stimulation, e.g. nerve stimulation to elicit EMG response
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Abstract
A kind of device for being used to detect Parkinson's abnormal gait, including：Gait detection sensor, for being detected to the gait of patient and exporting detection signal；Time-domain analysis module, for carrying out time-domain analysis to detection signal to obtain time domain index；Frequency-domain analysis module, for carrying out frequency-domain analysis to detection signal to obtain frequency-domain index；Frequency-domain calculations module, for judging the gait of patient according to the frequency-domain index and frequency domain judged result being exported when patient's gait is abnormal gait；Time-domain calculation module, for the gait that the patient is judged according to parameter preset and time domain index and real-time output abnormality gait testing result；And the correction module, for generating correction factor according to frequency domain judged result and abnormal gait testing result；Time-domain calculation module is additionally operable to be corrected parameter preset according to correction factor.Said apparatus has the advantages of accuracy in detection height and preferable real-time.Present invention also offers a kind of device for being used to alleviate Parkinson's abnormal gait.
Description
Technical field
The present invention relates to technical field of medical instruments, is used to detect more particularly to one kind, alleviates Parkinson's abnormal gait
Device.
Background technology
Parkinson's are a kind of chronic CNS degenerative disorder diseases.The patient for suffering from Parkinson's occurs
Dyskinesia symptom, so that its gait has exception for normal gait.With the disturbances in patients with Parkinson disease state of an illness
Aggravation, have that festinating gait occurs in greater probability, gait is freezed and an abnormal gait such as the difficulty that starts to walk.It is different in traditional Parkinson
In normal gait detection process, it is defined mostly by patient's subjective test results, the degree of accuracy of detection is relatively low, it is impossible to meet that detection will
Ask.
The content of the invention
Based on this, it is necessary to provide a kind of degree of accuracy and real-time it is higher be used for detect, alleviate Parkinson's abnormal gait
Device.
A kind of device for being used to detect Parkinson's abnormal gait, including：Gait detection sensor, for the gait to patient
Detected and export detection signal；Time-domain analysis module, it is connected with the gait detection sensor, for believing the detection
Number time-domain analysis is carried out to obtain the time domain index of the detection signal；Frequency-domain analysis module, with the gait detection sensor
Connection, for carrying out frequency-domain analysis to the detection signal to obtain the frequency-domain index of the detection signal；Frequency-domain calculations module,
It is connected with the frequency-domain analysis module, the frequency-domain index for being obtained according to the frequency-domain analysis module judges the step of the patient
State, and frequency domain judged result is exported when patient's gait is abnormal gait；Time-domain calculation module, with correction module and described
Time-domain analysis module connects, and the time domain index for being got according to parameter preset and the time-domain analysis module judges the trouble
The gait of person and in real time output abnormality gait testing result；And the correction module, the correction module also with the frequency domain
Computing module connects, for being generated according to the frequency domain judged result and the abnormal gait testing result for the time domain
The correction factor that the parameter preset of computing module is corrected；The time-domain calculation module be additionally operable to according to the correction because
It is several that the parameter preset is corrected.
In one of the embodiments, the frequency-domain calculations module is additionally operable to judging patient's gait for abnormal gait
When, the abnormal gait species of the patient is determined according to the frequency-domain index；The abnormal gait species include it is completely inactive,
At least two in hardly possible are freezed and started to walk to festinating gait, gait；The time-domain calculation module is additionally operable to judging patient's step
When state is abnormal gait, the abnormal gait species of the patient is determined according to the time domain index, and exports the abnormal step of determination
State kind of information.
In one of the embodiments, the time-domain calculation module is used for the time domain index and time domain targets threshold ratio
Compared with so as to judge the gait of patient；The frequency-domain calculations module is used for the frequency-domain index and frequency domain targets threshold ratio
Compared with so as to judge the gait of patient.
In one of the embodiments, in addition to Disease index computing module；The Disease index computing module respectively with
The time-domain calculation module, frequency-domain calculations module connection；The Disease index computing module is used for exception occur to patient
The duration and number of gait are counted, to export after Disease index is calculated；The Disease index includes abnormal gait
Maintain duration, probability of occurrence, the frequency of occurrences and relative to the previous preset time period state of an illness development percentage at least one
Kind index parameter.
In one of the embodiments, the Disease index computing module is additionally operable to obtain the administration time of patient；It is described
Disease index computing module be used for count the appearance duration and number for abnormal gait occur interior per hour after patient medication, by and in terms of
Calculation exports after obtaining the Disease index.
In one of the embodiments, in addition to memory；The memory is used to store patient information, and described in storage
The abnormal gait testing result and time domain index corresponding with the abnormal gait testing result and frequency-domain index of device output.
In one of the embodiments, the gait detection sensor include pressure sensor, acceleration transducer and and
At least one of myoelectric sensor sensor；The time-domain analysis module includes multiple time-domain analysis units；Each time domain point
Analysis unit is connected with a sensor, when carrying out time-domain analysis with the detection signal exported to the sensor and exporting corresponding
Domain index；The frequency-domain analysis module includes multiple frequency-domain analysis units；Each frequency-domain analysis unit is connected with a sensor,
Frequency-domain analysis is carried out with the detection signal exported to the sensor and exports corresponding frequency-domain index；The frequency-domain calculations module
Frequency-domain index for being exported in real time according to each frequency-domain analysis unit judges the gait of patient；The time-domain calculation module is used for root
The time domain index exported in real time according to each time-domain analysis unit judges patient's gait.
In one of the embodiments, the pressure sensor is used to gather the pressure at patient's forefoot and rear heel；
The acceleration transducer is used to gather the acceleration at patient's ankle perpendicular to shank forward；The myoelectric sensor is used for
Gather the electromyographic signal at patient's shank gastrocnemius and tibialis.
In one of the embodiments, the gait detection sensor includes wireless communication unit；The time-domain analysis mould
Wireless communication unit is provided with block and the frequency-domain analysis module, to establish channel radio with the gait detection sensor
Letter.
A kind of device for being used to alleviate Parkinson's abnormal gait, including：Detection means, the detection means are included as foregoing
The device for being used to detect Parkinson's abnormal gait described in any embodiment；And stimulating apparatus, it is connected with the detection means,
For exported according to the detection means abnormal gait testing result when fixed point stimulation is carried out to patient.
The above-mentioned device for being used to detect Parkinson's abnormal gait, gait detection sensor detect to the gait of patient,
Then detection signal is analyzed by time-domain analysis module to obtain time domain index, and detection believed by frequency-domain analysis module
Number analyzed to obtain frequency-domain index.Frequency-domain calculations module judges the gait of patient according to obtained frequency-domain index, and is judging
Go out output frequency domain judged result when patient is abnormal gait.The time domain that time-domain calculation module is then got according to time-domain analysis module
Index and parameter preset judge the gait of patient and in real time output abnormality gait testing result, so that it is guaranteed that testing result has
Higher real-time.Correction module is then generated for time domain according to the result of calculation of time-domain calculation module and frequency-domain calculations module
The correction factor that the parameter preset of computing module is corrected so that time-domain calculation module according to the correction factor to default
Parameter is corrected, and so as to improve the degree of accuracy of time-domain calculation module output, disclosure satisfy that detection demand.
Brief description of the drawings
Fig. 1 is the structured flowchart for being used to detect the device of Parkinson's abnormal gait in an embodiment；
Fig. 2 is the structured flowchart for being used to detect the device of Parkinson's abnormal gait in another embodiment；
Fig. 3 is the structured flowchart for being used to alleviate the device of Parkinson's abnormal gait in an embodiment.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
Fig. 1 is the structured flowchart for being used to detect the device 100 of Parkinson's abnormal gait in an embodiment.This is used to detect
The device 100 (hereinafter referred to as device 100) of Parkinson's abnormal gait can detect to the gait of disturbances in patients with Parkinson disease, so that
Output abnormality gait testing result when patient is in abnormal gait.Referring to Fig. 1, the device 100 includes gait detection sensor
110th, time-domain analysis module 120, frequency-domain analysis module 130, frequency-domain calculations module 140, correction module 150 and time-domain calculation mould
Block 160.
Gait detection sensor 110 is used to detect the gait of patient.Gait detection sensor 110 can be by right
Electromyographic signal of acceleration or shank etc. is detected to obtain detection signal at the plantar pressure of patient, ankle.Due to suffering from
When person is in abnormal gait, the ginseng such as its plantar pressure, the signal amplitude of ankle acceleration and shank electromyographic signal and frequency
There is more obvious difference in number index, therefore the gait of patient can be entered according to detection signal for normal gait
Row judges, judges whether abnormal gait occur.Wireless communication unit such as bluetooth module is provided with gait detection sensor 110
Deng.In the present embodiment, time-domain analysis module 120, frequency-domain analysis module 130, frequency-domain calculations module 140, correction module 150 with
And time domain computing module 160 can be integrated in same terminal, such as it is integrated on same computing unit (processor).The meter
Calculating unit equally includes wireless communication module, is connected with carrying out radio communication with gait detection sensor 110.
Time-domain analysis module 120 is connected with gait detection sensor 110, for what is exported to gait detection sensor 110
Detection signal carries out time-domain analysis.Time-domain analysis module 120 obtains such as cadence, step-length and amplitude time domain by time-domain analysis
Index.Time-domain analysis process can obtain the gait information of patient in real time.
Frequency-domain analysis module 130 is connected with gait detection sensor 110, for what is exported to gait detection sensor 110
Detection signal carries out frequency-domain analysis.Frequency-domain analysis module 130 is obtained such as normal (0~3Hz) frequency band and exception by frequency-domain analysis
The frequency-domain index such as the energy ratio, median frequency and crest frequency of (3~8Hz) frequency band.Frequency-domain analysis module 130 is slided using band
The Fast Fourier Transform (FFT) of window carries out frequency-domain transform.Due to Fourier transformation can according to the time delayses of the length of data window,
Real-time is inadequate, but quantity is big, and accuracy rate is higher.
Frequency-domain calculations module 140 is connected with frequency-domain analysis module 130.Frequency-domain calculations module 140 is used to receive frequency-domain analysis
The frequency-domain index that module 130 exports, judges the gait of patient according to the frequency-domain index, to determine the current gait of patient
Whether it is abnormal gait.Frequency-domain calculations module 140 is realized when carrying out Gait Recognition using Fuzzy Logic Reasoning Algorithm.Frequency-domain calculations
Module 140 compared with frequency domain targets threshold by each frequency-domain index by judging whether the current gait of patient is abnormal step
State.In the present embodiment, when it is abnormal gait to judge gait, abnormal gait species can also be determined whether.Common is different
Normal gait category includes completely inactive, festinating gait, the species such as difficulty are freezed and started to walk to gait.Specifically, frequency-domain calculations mould
Block 140 is used to carry out Fuzzy processing to each frequency-domain index value, then obtains current gait by fuzzy reasoning as each abnormal step
Probability of state, and the probability is determined to the abnormal gait species of current gait compared with targets threshold.Frequency-domain calculations module 140 is entered
The principle of row fuzzy reasoning is as follows：
When abnormal gait is completely inactive, gastrocnemius and the power spectral integral of tibialis electromyographic signal are more than target threshold
Value, the pressure mean values (namely amplitude of detection signal) that pressure sensor detects have high-frequency signal close to patient body weight.When
When abnormal gait is festinating gait, plantar pressure and the termination frequency of acceleration pressure at ankle are being specified in section with frequency domain.
When abnormal gait freezes for starting, plantar pressure and 0~3Hz energy of acceleration signal and the ratio of 3~8Hz energy at ankle
Value is more than threshold value.When asynchronous mode is starts to walk difficult, namely patient, by static more difficult to the lift leg first step, the phenomenon is with walking
State is freezed similar, but signal intensity is weaker, according to the historical data on frequency domain and time domain, and carry out fuzzy reasoning can be right
The state is judged.Therefore by the frequency-domain index detected and frequency domain targets threshold relatively after can judge abnormal gait,
And determine abnormal gait species.
Targets threshold can be determined according to the experience of Parkinson expert, can also be obtained according to a large amount of sampled datas
Statistical law determines to determine, or according to the data message of the state of an illness of patient generation.Generated in real time online according to patient's state of an illness
Data message when determining targets threshold, targets threshold can be adjusted according to change of illness state, so that obtained data are more
It is accurate to add, and meets actual test needs.Frequency-domain calculations module 140 exports frequency domain judged result after abnormal gait is judged.It is defeated
Abnormal gait information can be included in the judged result gone out, such as abnormal gait species, and frequency corresponding with abnormal gait species
Domain index.In the present embodiment, the degree of membership of abnormal gait species is further comprises in the frequency domain judged result of output.Degree of membership is used
In representing that current gait is to belong to the probability of the abnormal gait species.
Correction module 150 is connected with frequency-domain calculations module 140, time-domain calculation module 160.Correction module 150 is used for basis
The abnormal gait testing result generation that the frequency domain judged result and time-domain calculation module 160 that frequency-domain calculations module 140 exports export
Correction factor.The correction factor of generation is used to be corrected the relevant parameter in the processing procedure of time-domain calculation module 160.Specifically
Ground, correction factor can be used for being modified the rule of the fuzzy reasoning of time-domain calculation module 160 or to reasoning by analogy mistakes
Targets threshold in journey etc. is corrected, so as to improve the degree of accuracy of the output result of time-domain calculation module 160.Correction module
150 are additionally operable to after stable disease, and control correction factor is in steady state value, so that the calculating process of time-domain calculation module 160
In parameter preset be in it is constant.
Time-domain calculation module 160 is connected with time-domain analysis module 130, and is connected with correction module 150.Time-domain calculation module
160 time domain indexes for being used to be got according to parameter preset and time-domain analysis module 130 judge the gait of patient and exported in real time
Abnormal gait testing result.Because parameter preset can be corrected by correction module 150, so that time-domain calculation module
The 160 abnormal gait testing results exported in real time equally have higher precision, meet the accuracy requirement measured in real time.Specifically
Ground, time-domain calculation module 160 can be corrected or to relevant time domain mesh using the correction factor to the rule of fuzzy reasoning
Mark threshold value is corrected.Time-domain calculation module 160 after calibration, by the time domain target threshold after the time domain index got and correction
Value is compared, so as to judge whether patient's gait is abnormal gait.Likewise, time-domain calculation module 160 can also judged
When the gait of patient is abnormal gait, the species of abnormal gait is judged, and output abnormality gait testing result.Time domain meter
Abnormal gait species and its corresponding degree of membership can be included by calculating the abnormal gait testing result that module 160 exports.In this reality
Apply in example, the calculating process of time-domain calculation module 160 has preferable real-time, so as to the real-time detection as the device 100
As a result export to user.And not asked for long-term monitoring etc. in the application process of real-time, then using the higher frequency of precision
Domain testing result.The above-mentioned device for being used to detect Parkinson's abnormal gait, gait detection sensor 110 are carried out to the gait of patient
Detection, is then analyzed detection signal to obtain time domain index, and pass through frequency-domain analysis module by time-domain analysis module 120
130 pairs of detection signals are analyzed to obtain frequency-domain index.Frequency-domain calculations module 140 judges patient's according to obtained frequency-domain index
Gait, and frequency domain judged result is exported when judging that patient is abnormal gait.Time-domain calculation module 160 is then according to time-domain analysis
The time domain index and parameter preset that module 120 is got judge the gait of patient and in real time output abnormality gait testing result,
So that it is guaranteed that testing result has higher real-time.Correction module 150 is then according to time-domain calculation module 160 and frequency-domain calculations mould
The result of calculation of block 140 generates the correction factor for being corrected to the parameter preset of time-domain calculation module 160, so that
Time-domain calculation module 160 is corrected according to the correction factor to parameter preset, so as to improve the output of time-domain calculation module 160
The degree of accuracy, it disclosure satisfy that detection demand.Fig. 2 is the device 200 for being used to detect Parkinson's abnormal gait in another embodiment
Structured flowchart.Gait detection sensor in the device 200 includes pressure sensor 212, acceleration transducer 214 and myoelectricity and passed
Sensor 216.Wherein, pressure sensor 212 can be FSR pressure sensors.Pressure sensor 212 can be arranged on the footwear of patient
In bottom or shoe-pad, to gather the pressure at patient's forefoot and rear heel.Acceleration transducer 214 is then arranged on patient's ankle
Place, for being acquired to the acceleration at ankle perpendicular to shank forward.Myoelectric sensor 216 includes shank gastrocnemius myoelectricity
Sensor and tibialis myoelectric sensor, so as to be acquired to the electromyographic signal of shank gastrocnemius and tibialis.By more
Individual sensor detects to parameter of different nature, so as to overcome single detection mode in terms of sensitivity and the degree of accuracy
The defects of existing, meet detection demand.
In the present embodiment, time-domain analysis module includes multiple time-domain analysis units 220.Each time-domain analysis unit 220
It is connected with a sensor, so as to carry out time-domain analysis to the detection signal of sensor output.Frequency-domain analysis module is equally wrapped
Include multiple frequency-domain analysis units 230.Each frequency-domain analysis unit 230 is connected with a sensor, so as to be exported to the sensor
Detection signal carry out frequency-domain analysis.Frequency-domain calculations module 240 is then used for the frequency exported in real time according to each frequency-domain analysis unit 230
Domain index judges the gait of patient, and exports frequency domain judged result to correction module 250.Time-domain calculation module 260 is then used for root
The frequency-domain index exported in real time according to each time-domain analysis unit 220 judges the gait of patient.
In the present embodiment, device 200 also includes Disease index computing module 270.Disease index computing module 270 is distinguished
It is connected with time-domain calculation module 260, frequency-domain calculations module 240, and occurs the duration of abnormal gait and number progress to patient
Statistics, so as to the maintenance duration of the abnormal gait of patient, the frequency of occurrences, probability of occurrence and relative to previous preset time period
The development percentage of the state of an illness such as (one day, a week or one month) exports after being counted as Disease index, realizes the state of an illness
Quantization detection, aid in diagnosis.In the present embodiment, Disease index computing module 270 is additionally operable to receive the medication of patient
Time, and by newest administration time be defined statistics medication after the appearance duration of abnormal gait and number etc. in each hour,
So as to be counted to the Disease index of abnormal gait, exported after forming relative indicatrix.By entering to the data of different time points
Row compares, it can be deduced that the progress curve of the state of an illness.Meanwhile according to the administration time of patient, after the Disease index after statistics medication
Contrasted with the data before patient medication, can so as to draw patient one day, one week, the disease development curve in January
To record disease development well, diagnosis are aided in.
Said apparatus 200 also includes memory.Memory can be arranged in device 200, can be that independent storage takes
Business device.Memory is used for abnormal gait testing result and the Disease index computing module 270 exported to time-domain analysis module 260
The Disease index of output is stored, and with the disease development data message of record storage patient, facilitates doctor to be checked.Simultaneously
The data message of memory memory storage is also used as studying the basic data of Parkinson's state of an illness, so as to be used as frequency-domain calculations module
240 and time-domain calculation module 260 carry out the benchmark of gait judgement, further improve the degree of accuracy of each deterministic process.
The present invention also provides a kind of device 300 for being used to alleviate Parkinson's abnormal gait, as shown in Figure 3.Device 300 includes
Detection means 310 and stimulating apparatus 320.Detection means 310 includes being used to detect Parkinson's exception in foregoing any embodiment
The device of gait.Stimulating apparatus 320 is connected with detection means 310, for the abnormal gait detection exported according to detection means 310
As a result fixed point stimulation is carried out to patient, so as to help patient to recover normal.Fixed point stimulation is carried out by stimulating apparatus 320, can be with
Avoid always to patient apply stimulation cause to occur to be immunized, decreased effectiveness problem.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope that this specification is recorded all is considered to be.
Embodiment described above only expresses the several embodiments of the present invention, and its description is more specific and detailed, but simultaneously
Can not therefore it be construed as limiting the scope of the patent.It should be pointed out that come for one of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (10)
- A kind of 1. device for being used to detect Parkinson's abnormal gait, it is characterised in that including：Gait detection sensor, for being detected to the gait of patient and exporting detection signal；Time-domain analysis module, it is connected with the gait detection sensor, for carrying out time-domain analysis to the detection signal to obtain Obtain the time domain index of the detection signal；Frequency-domain analysis module, it is connected with the gait detection sensor, for carrying out frequency-domain analysis to the detection signal to obtain Obtain the frequency-domain index of the detection signal；Frequency-domain calculations module, it is connected with the frequency-domain analysis module, the frequency domain for being obtained according to the frequency-domain analysis module refers to Mark judges the gait of the patient, and frequency domain judged result is exported when patient's gait is abnormal gait；Time-domain calculation module, it is connected with correction module and the time-domain analysis module, for according to parameter preset and the time domain The time domain index that analysis module is got judges the gait of the patient and in real time output abnormality gait testing result；AndThe correction module, the correction module are also connected with the frequency-domain calculations module, for judging to tie according to the frequency domain Fruit and the abnormal gait testing result generate the school for being corrected to the parameter preset of the time-domain calculation module Positive factor；The time-domain calculation module is additionally operable to be corrected the parameter preset according to the correction factor.
- 2. device according to claim 1, it is characterised in that the frequency-domain calculations module is additionally operable to judging the patient When gait is abnormal gait, the abnormal gait species of the patient is determined according to the frequency-domain index；The abnormal gait species Freeze and start to walk at least two in hardly possible including completely inactive, festinating gait, gait；The time-domain calculation module is additionally operable to When judging patient's gait for abnormal gait, the abnormal gait species of the patient is determined according to the time domain index, and it is defeated Go out the abnormal gait kind of information of determination.
- 3. device according to claim 1, it is characterised in that the time-domain calculation module be used for by the time domain index with Time domain targets threshold compares so as to judge the gait of patient；The frequency-domain calculations module be used for by the frequency-domain index with Frequency domain targets threshold compares so as to judge the gait of patient.
- 4. device according to claim 1, it is characterised in that also including Disease index computing module；The Disease index Computing module is connected with the time-domain calculation module, the frequency-domain calculations module respectively；The Disease index computing module is used for There is the duration of abnormal gait to patient and number counts, to export after Disease index is calculated；The state of an illness refers to Mark includes maintenance duration, probability of occurrence, the frequency of occurrences and the development hundred relative to the previous preset time period state of an illness of abnormal gait Divide at least one of ratio index parameter.
- 5. device according to claim 4, it is characterised in that the Disease index computing module is additionally operable to obtain patient's Administration time；The Disease index computing module is used to count the appearance duration for abnormal gait occur interior per hour after patient medication And number, with and export after the Disease index is calculated.
- 6. device according to claim 1, it is characterised in that also including memory；The memory is used to store patient Information, and store the abnormal gait testing result of described device output and time domain corresponding with the abnormal gait testing result Index and frequency-domain index.
- 7. device according to claim 1, it is characterised in that the gait detection sensor includes pressure sensor, added Velocity sensor and with least one of myoelectric sensor sensor；The time-domain analysis module includes multiple time-domain analysis lists Member；Each time-domain analysis unit is connected with a sensor, and time-domain analysis is carried out with the detection signal exported to the sensor And export corresponding time domain index；The frequency-domain analysis module includes multiple frequency-domain analysis units；Each frequency-domain analysis unit with One sensor connection, carries out frequency-domain analysis with the detection signal exported to the sensor and exports corresponding frequency-domain index； The frequency-domain index that the frequency-domain calculations module is used to be exported in real time according to each frequency-domain analysis unit judges the gait of patient；When described The time domain index that domain computing module is used to be exported in real time according to each time-domain analysis unit judges patient's gait.
- 8. device according to claim 7, it is characterised in that the pressure sensor is used to gather patient's forefoot with after Pressure at heel；The acceleration transducer is used to gather the acceleration at patient's ankle perpendicular to shank forward；It is described Myoelectric sensor is used to gather the electromyographic signal at patient's shank gastrocnemius and tibialis.
- 9. device according to claim 1, it is characterised in that the gait detection sensor includes wireless communication unit； Wireless communication unit is provided with the time-domain analysis module and the frequency-domain analysis module, is sensed with being detected with the gait Device establishes radio communication.
- A kind of 10. device for being used to alleviate Parkinson's abnormal gait, it is characterised in that including：Detection means, the detection means include as described in claim 1~9 is any for detecting Parkinson's abnormal gait Device；And stimulating apparatus, it is connected with the detection means, the abnormal gait for being exported according to the detection means detects knot Fixed point stimulation is carried out to patient during fruit.
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| CN201611035951.8A CN107361773B (en) | 2016-11-18 | 2016-11-18 | For detecting, alleviating the device of Parkinson's abnormal gait |
| PCT/CN2017/087139 WO2018090604A1 (en) | 2016-11-18 | 2017-06-05 | Apparatus for detecting and mitigating parkinsonian gait |
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108063698A (en) * | 2017-12-15 | 2018-05-22 | 东软集团股份有限公司 | Unit exception detection method and device, program product and storage medium |
| CN108629304A (en) * | 2018-04-26 | 2018-10-09 | 深圳市臻络科技有限公司 | A kind of freezing of gait online test method |
| CN108814617A (en) * | 2018-04-26 | 2018-11-16 | 深圳市臻络科技有限公司 | Freezing of gait recognition methods and device and gait detector |
| CN109480857A (en) * | 2018-12-29 | 2019-03-19 | 中国科学院合肥物质科学研究院 | A kind of device and method for the detection of Parkinsonian's freezing of gait |
| CN110151190A (en) * | 2019-05-23 | 2019-08-23 | 西南科技大学 | A kind of orthopaedics postoperative rehabilitation monitoring method and system |
| CN110638457A (en) * | 2019-08-26 | 2020-01-03 | 广东省人民医院(广东省医学科学院) | Method and equipment for monitoring frozen gait of Parkinson disease patient |
| CN111631722A (en) * | 2020-05-18 | 2020-09-08 | 北京航空航天大学 | Parkinson's gait analysis system and method based on optical fiber microbend pressure sensing |
| CN113143251A (en) * | 2021-01-28 | 2021-07-23 | 胤迈医药科技(上海)有限公司 | Household wearable device based on stride monitoring |
| CN114271836A (en) * | 2022-01-25 | 2022-04-05 | 合肥学院 | Intelligent myoelectricity detection processing method and device based on wavelet transformation |
| CN114758746A (en) * | 2022-05-07 | 2022-07-15 | 北京中科睿医信息科技有限公司 | Method and device for determining dosage of neuropathy medicine |
| WO2022193850A1 (en) * | 2021-03-19 | 2022-09-22 | 深圳市韶音科技有限公司 | Exercise data processing method and exercise monitoring system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1671754A2 (en) * | 2000-11-17 | 2006-06-21 | Honda Giken Kogyo Kabushiki Kaisha | Gait generation system of legged mobile robot |
| CN101980659A (en) * | 2008-03-31 | 2011-02-23 | 夏普株式会社 | Body motion measuring device, mobile telephone, method for controlling the body motion measuring device, body motion measuring device control program, and computer-readable recording medium having the program recorded therein |
| CN102548474A (en) * | 2009-09-30 | 2012-07-04 | 三菱化学株式会社 | Body movement signal information processing method, information processing system and information processing device |
| CN102930133A (en) * | 2012-09-24 | 2013-02-13 | 安徽埃力智能科技有限公司 | Integrated rapid balance and gait assessment system and method |
| CN104834888A (en) * | 2014-12-04 | 2015-08-12 | 龙岩学院 | Abnormal gait identification method capable of facilitating screening Parkinsonism |
| WO2015164456A2 (en) * | 2014-04-22 | 2015-10-29 | The Trustees Of Columbia University In The City Of New York | Gait analysis devices, methods, and systems |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4630115B2 (en) * | 2005-04-19 | 2011-02-09 | 株式会社日立製作所 | Motion analysis display device |
| CN104606868B (en) * | 2015-02-10 | 2017-03-01 | 中国科学院苏州生物医学工程技术研究所 | A kind of Intelligent bracelet for alleviating Parkinsonian's freezing of gait |
| CN105361880B (en) * | 2015-11-30 | 2018-06-26 | 上海乃欣电子科技有限公司 | The identifying system and its method of muscular movement event |
-
2016
- 2016-11-18 CN CN201611035951.8A patent/CN107361773B/en active Active
-
2017
- 2017-06-05 WO PCT/CN2017/087139 patent/WO2018090604A1/en active Application Filing
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1671754A2 (en) * | 2000-11-17 | 2006-06-21 | Honda Giken Kogyo Kabushiki Kaisha | Gait generation system of legged mobile robot |
| CN101980659A (en) * | 2008-03-31 | 2011-02-23 | 夏普株式会社 | Body motion measuring device, mobile telephone, method for controlling the body motion measuring device, body motion measuring device control program, and computer-readable recording medium having the program recorded therein |
| CN102548474A (en) * | 2009-09-30 | 2012-07-04 | 三菱化学株式会社 | Body movement signal information processing method, information processing system and information processing device |
| CN102930133A (en) * | 2012-09-24 | 2013-02-13 | 安徽埃力智能科技有限公司 | Integrated rapid balance and gait assessment system and method |
| WO2015164456A2 (en) * | 2014-04-22 | 2015-10-29 | The Trustees Of Columbia University In The City Of New York | Gait analysis devices, methods, and systems |
| CN104834888A (en) * | 2014-12-04 | 2015-08-12 | 龙岩学院 | Abnormal gait identification method capable of facilitating screening Parkinsonism |
Non-Patent Citations (2)
| Title |
|---|
| YE CHEN等: "A Method to Calibrate Installation Orientation Errors of Inertial Sensors for Gait Analysis", 《PROCEEDING OF THE IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION》 * |
| 王刚等: "帕金森病步态障碍的诊断与治疗", 《中国现代神经疾病杂志》 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108063698A (en) * | 2017-12-15 | 2018-05-22 | 东软集团股份有限公司 | Unit exception detection method and device, program product and storage medium |
| CN108629304B (en) * | 2018-04-26 | 2020-12-08 | 深圳市臻络科技有限公司 | Freezing gait online detection method |
| CN108629304A (en) * | 2018-04-26 | 2018-10-09 | 深圳市臻络科技有限公司 | A kind of freezing of gait online test method |
| CN108814617A (en) * | 2018-04-26 | 2018-11-16 | 深圳市臻络科技有限公司 | Freezing of gait recognition methods and device and gait detector |
| CN109480857B (en) * | 2018-12-29 | 2021-09-14 | 中国科学院合肥物质科学研究院 | Device and method for detecting frozen gait of Parkinson disease patient |
| CN109480857A (en) * | 2018-12-29 | 2019-03-19 | 中国科学院合肥物质科学研究院 | A kind of device and method for the detection of Parkinsonian's freezing of gait |
| CN110151190A (en) * | 2019-05-23 | 2019-08-23 | 西南科技大学 | A kind of orthopaedics postoperative rehabilitation monitoring method and system |
| CN110638457A (en) * | 2019-08-26 | 2020-01-03 | 广东省人民医院(广东省医学科学院) | Method and equipment for monitoring frozen gait of Parkinson disease patient |
| CN110638457B (en) * | 2019-08-26 | 2023-02-21 | 广东省人民医院(广东省医学科学院) | Method and equipment for monitoring frozen gait of Parkinson disease patient |
| CN111631722A (en) * | 2020-05-18 | 2020-09-08 | 北京航空航天大学 | Parkinson's gait analysis system and method based on optical fiber microbend pressure sensing |
| CN113143251A (en) * | 2021-01-28 | 2021-07-23 | 胤迈医药科技(上海)有限公司 | Household wearable device based on stride monitoring |
| CN113143251B (en) * | 2021-01-28 | 2023-04-14 | 胤迈医药科技(上海)有限公司 | Household wearable device based on stride monitoring |
| WO2022193850A1 (en) * | 2021-03-19 | 2022-09-22 | 深圳市韶音科技有限公司 | Exercise data processing method and exercise monitoring system |
| CN114271836A (en) * | 2022-01-25 | 2022-04-05 | 合肥学院 | Intelligent myoelectricity detection processing method and device based on wavelet transformation |
| CN114758746A (en) * | 2022-05-07 | 2022-07-15 | 北京中科睿医信息科技有限公司 | Method and device for determining dosage of neuropathy medicine |
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| CN107361773B (en) | 2019-10-22 |
| WO2018090604A1 (en) | 2018-05-24 |
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