CN107361773B - 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 83
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- 238000004364 calculation method Methods 0.000 claims abstract description 68
- 238000012937 correction Methods 0.000 claims abstract description 32
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- 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
It is a kind of for detecting the device of Parkinson's abnormal gait, comprising: gait detection sensor detects for the gait to patient and exports 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 the output frequency domain judging result when patient's gait is abnormal gait；Time-domain calculation module, for judging the gait and output abnormality gait testing result in real time of the patient according to parameter preset and time domain index；And the correction module, for generating correction factor according to frequency domain judging result and abnormal gait testing result；Time-domain calculation module is also used to be corrected parameter preset according to correction factor.Above-mentioned apparatus has the advantages that accuracy in detection is high and real-time is preferable.The present invention also provides a kind of for alleviating the device of Parkinson's abnormal gait.
Description
Technical field
The present invention relates to the field of medical instrument technology, more particularly to one kind for detecting, alleviating Parkinson's abnormal gait
Device.
Background technique
Parkinson's disease is a kind of chronic CNS degenerative disorder disease.The patient for suffering from Parkinson's disease will appear
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 the abnormal gaits such as the difficulty that starts to walk.It is different in traditional Parkinson
In normal gait detection process, it is subject to patient's subjective test results mostly, the accuracy of detection is lower, is not able to satisfy detection and wants
It asks.
Summary of the invention
Based on this, it is necessary to provide a kind of accuracy and real-time is higher for detecting, alleviating Parkinson's abnormal gait
Device.
It is a kind of for detecting the device of Parkinson's abnormal gait, comprising: gait detection sensor, for the gait to patient
It is detected and exports detection signal；Time-domain analysis module is connect with the gait detection sensor, for believing the detection
Number carry out time-domain analysis with obtain it is described detection signal time domain index；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 connect 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 judging result is exported when patient's gait is abnormal gait；Time-domain calculation module, with correction module and described
The connection of time-domain analysis module, the time domain index for being got according to parameter preset and the time-domain analysis module judge 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 connection, for being generated according to the frequency domain judging result and the abnormal gait testing result for the time domain
The corrected correction factor of the parameter preset of computing module；The time-domain calculation module be also used to according to the correction because
It is several that the parameter preset is corrected.
The frequency-domain calculations module is also used to judging patient's gait for abnormal gait in one of the embodiments,
When, the abnormal gait type of the patient is determined according to the frequency-domain index；The abnormal gait type 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 also used to judging patient's step
When state is abnormal gait, the abnormal gait type of the patient is determined according to the time domain index, and exports determining abnormal step
State information.
The time-domain calculation module is used for the time domain index and time domain targets threshold ratio in one of the embodiments,
Compared with 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 to judge the gait of patient.
It in one of the embodiments, further include 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 to patient occur abnormal
The duration and number of gait are counted, to export after Disease index is calculated；The Disease index includes abnormal gait
Maintenance duration, probability of occurrence, the frequency of occurrences and relative to the previous preset time period state of an illness develop percentage at least one
Kind index parameter.
The Disease index computing module is also used to obtain the administration time of patient in one of the embodiments,；It is described
There is the appearance duration of abnormal gait and number for counting after patient medication in per hour in Disease index computing module, by and in terms of
Calculation exports after obtaining the Disease index.
It in one of the embodiments, further include memory；The memory is used to store patient information, and described in storage
The abnormal gait testing result of device output and time domain index corresponding with the abnormal gait testing result and frequency-domain index.
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 connect with a sensor, when being carried out time-domain analysis with the detection signal exported to the sensor and exported corresponding
Domain index；The frequency-domain analysis module includes multiple frequency-domain analysis units；Each frequency-domain analysis unit is connect 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
Patient's gait is judged according to the time domain index that each time-domain analysis unit exports in real time.
The pressure sensor is used to acquire the pressure at patient's forefoot and rear heel in one of the embodiments,；
The acceleration transducer is with for acquiring at patient's ankle perpendicular to the forward acceleration of shank；The myoelectric sensor is used for
Acquire the electromyography signal at patient's shank gastrocnemius and tibialis.
The gait detection sensor includes wireless communication unit in one of the embodiments,；The time-domain analysis mould
It is provided with wireless communication unit in block and the frequency-domain analysis module, to establish channel radio with the gait detection sensor
Letter.
It is a kind of for alleviating the device of Parkinson's abnormal gait, comprising: detection device, the detection device include as aforementioned
For detecting the device of Parkinson's abnormal gait described in any embodiment；And stimulating apparatus, it is connect with the detection device,
Fixed point stimulation is carried out to patient when abnormal gait testing result for exporting according to the detection device.
It is above-mentioned that for detecting the device of Parkinson's abnormal gait, gait detection sensor detects the gait of patient,
Then detection signal is analyzed by time-domain analysis module to obtain time domain index, and detection is believed by frequency-domain analysis module
It number is analyzed to obtain frequency-domain index.Frequency-domain calculations module judges the gait of patient according to obtained frequency-domain index, and is judging
Frequency domain judging result is exported when patient is abnormal gait out.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 according to the calculated result of time-domain calculation module and frequency-domain calculations module for time domain
The corrected correction factor of the parameter preset of computing module, so that time-domain calculation module is according to the correction factor to default
Parameter is corrected, to improve the accuracy of time-domain calculation module output, can satisfy detection demand.
Detailed description of the invention
Fig. 1 is the structural block diagram of the device for detecting Parkinson's abnormal gait in an embodiment；
Fig. 2 is the structural block diagram of the device for detecting Parkinson's abnormal gait in another embodiment；
Fig. 3 is the structural block diagram of the device for alleviating Parkinson's abnormal gait in an embodiment.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
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 structural block diagram of the device 100 for detecting 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 the gait of disturbances in patients with Parkinson disease, thus
Output abnormality gait testing result when patient is in abnormal gait.Referring to Fig. 1, which includes gait detection sensor
110, 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 for detecting the gait of patient.Gait detection sensor 110 can be by right
Acceleration or the electromyography signal of shank etc. are 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 plantar pressure, the signal amplitude of ankle acceleration and shank electromyography signal and frequency
There is more apparent difference for normal gait in number indexs, thus can according to detection signal to the gait of patient into
Row judgement, judges whether abnormal gait occur.Wireless communication unit such as bluetooth module is provided in 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 integrate in same terminal, such as be integrated on same computing unit (processor).The meter
Calculating unit equally includes wireless communication module, is connect with carrying out wireless communication with gait detection sensor 110.
Time-domain analysis module 120 is connect with gait detection sensor 110, for what is exported to gait detection sensor 110
It detects signal and 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 connect with gait detection sensor 110, for what is exported to gait detection sensor 110
It detects signal and carries out frequency-domain analysis.Frequency-domain analysis module 130 is obtained by frequency-domain analysis such as normal (0~3Hz) frequency band and exception
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 using band sliding
The Fast Fourier Transform (FFT) of window carries out frequency-domain transform.Due to Fourier transformation can according to the time delays 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 connect with frequency-domain analysis module 130.Frequency-domain calculations module 140 is for receiving frequency-domain analysis
The frequency-domain index that module 130 exports, judges according to gait of the frequency-domain index to patient, to determine the current gait of patient
It whether 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 is by judging whether the current gait of patient is abnormal step compared with frequency domain targets threshold for each frequency-domain index
State.In the present embodiment, when judging gait is abnormal gait, abnormal gait type can also be further judged.Common is different
Normal gait category freezes and starts to walk the types such as difficulty including completely inactive, festinating gait, gait.Specifically, frequency-domain calculations mould
Block 140 is used to carry out Fuzzy processing to each frequency-domain index value, and then obtaining current gait by fuzzy reasoning is each abnormal step
Probability of state, and the probability is determined to the abnormal gait type of current gait compared with targets threshold.Frequency-domain calculations module 140 into
The principle of row fuzzy reasoning is as follows:
When abnormal gait is completely inactive, the power spectral integral of gastrocnemius and tibialis electromyography signal is greater 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, the termination frequency of acceleration pressure and frequency domain are in specified section at plantar pressure and ankle.
When abnormal gait is that starting is freezed, 0~3Hz energy of acceleration signal and the ratio of 3~8Hz energy at plantar pressure and ankle
Value is greater than threshold value.When asynchronous mode is starting hardly possible namely patient is by static more difficult to the lift leg first step, the phenomenon and step
State is freezed similar, but signal strength is weaker, and according to the historical data on frequency domain and time domain, and carrying out fuzzy reasoning can be right
The state is judged.Therefore abnormal gait can be judged after the frequency-domain index that will test is compared with frequency domain targets threshold,
And determine abnormal gait type.
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 information that the state of an illness of patient generates.It is generated in real time online according to patient's state of an illness
Data information 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 judging result after judging abnormal gait.It is defeated
It may include abnormal gait information in judging result out, such as abnormal gait type, and frequency corresponding with abnormal gait type
Domain index.In the present embodiment, the degree of membership of abnormal gait type is further comprised in the frequency domain judging result of output.Degree of membership is used
In indicating that current gait is the probability for belonging to the abnormal gait type.
Correction module 150 is connect with frequency-domain calculations module 140, time-domain calculation module 160.Correction module 150 is used for basis
The abnormal gait testing result that the frequency domain judging result and time-domain calculation module 160 that frequency-domain calculations module 140 exports export generates
Correction factor.The correction factor of generation is for being corrected the relevant parameter in 160 treatment process of time domain computing module.Specifically
Ground, correction factor can be used for being modified the rule of the fuzzy reasoning of time domain computing module 160 or to reasoning by analogy mistake
Targets threshold etc. in journey is corrected, to improve the accuracy of the output result of time-domain calculation module 160.Correction module
150 are also used to after stable disease, and control correction factor is in steady state value, so that 160 calculating process of time-domain calculation module
In parameter preset be in it is constant.
Time-domain calculation module 160 is connect with time-domain analysis module 130, and is connect with correction module 150.Time-domain calculation module
160 for judging the gait of patient according to the time domain index that parameter preset and time-domain analysis module 130 are got and exporting in real time
Abnormal gait testing result.Since parameter preset can be corrected by correction module 150, so that time-domain calculation module
The 160 abnormal gait testing result precisions equally with higher exported in real time, meet the accuracy requirement of real-time measurement.Specifically
Ground, time-domain calculation module 160 can use the correction factor and be corrected to the rule of fuzzy reasoning or to relevant time domain mesh
Mark threshold value is corrected.Time-domain calculation module 160 after calibration, the time domain target threshold after the time domain index that will acquire and correction
Value is compared, 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 type of abnormal gait is judged, and output abnormality gait testing result.Time domain meter
Calculating the abnormal gait testing result that module 160 exports may include abnormal gait type and its corresponding degree of membership.In this reality
It applies in example, the calculating process of time-domain calculation module 160 has preferable real-time, thus the real-time detection as the device 100
As a result it exports to user.And long-term monitoring etc. is not asked in the application process of real-time, then use 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 carry out the gait of patient
Then detection is analyzed to obtain time domain index by 120 pairs of detection signals of time-domain analysis module, and passes through frequency-domain analysis module
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 judging 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 real-time with higher.Correction module 150 is then according to time-domain calculation module 160 and frequency-domain calculations mould
The calculated result of block 140 is generated for the corrected correction factor of parameter preset to time domain computing module 160, so that
Time-domain calculation module 160 is corrected parameter preset according to the correction factor, to improve the output of time-domain calculation module 160
Accuracy can satisfy detection demand.Fig. 2 is the device 200 for detecting Parkinson's abnormal gait in another embodiment
Structural block diagram.Gait detection sensor in the device 200 includes that pressure sensor 212, acceleration transducer 214 and myoelectricity pass
Sensor 216.Wherein, pressure sensor 212 can be FSR pressure sensor.The shoes in patient can be set in pressure sensor 212
In bottom or insole, to acquire the pressure at patient's forefoot and rear heel.Acceleration transducer 214 is then arranged in patient's ankle
Place, for being acquired at ankle perpendicular to the forward acceleration of shank.Myoelectric sensor 216 includes shank gastrocnemius myoelectricity
Sensor and tibialis myoelectric sensor, to be acquired to the electromyography signal of shank gastrocnemius and tibialis.By more
A sensor detects parameter of different nature, so as to overcome single detection mode in terms of sensitivity and accuracy
Existing defect meets 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 connect with a sensor, 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 connect with a sensor, to export to the sensor
Detection signal carry out frequency-domain analysis.The frequency that frequency-domain calculations module 240 then is used to be exported in real time according to each frequency-domain analysis unit 230
Domain index judges the gait of patient, and exports frequency domain judging result to correction module 250.Time-domain calculation module 260 is then used for root
The gait of patient is judged according to the frequency-domain index that each time-domain analysis unit 220 exports in real time.
In the present embodiment, device 200 further includes Disease index computing module 270.Disease index computing module 270 is distinguished
It is connect with time-domain calculation module 260, frequency-domain calculations module 240, and the duration of abnormal gait and number progress occurs to patient
Statistics, thus 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, assist diagnosis.In the present embodiment, Disease index computing module 270 is also used to receive the medication of patient
Time, and the appearance duration of abnormal gait and number etc. in each hour be subject to after the statistics medication of newest administration time,
To be counted to the Disease index of abnormal gait, exported after forming relative indicatrix.By the data to different time points into
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 counting medication
It is compared with the data before patient medication, it, can so as to obtain patient one day, one week, the progression of the disease curve in January
To record progression of the disease well, diagnosis is assisted.
Above-mentioned apparatus 200 further includes memory.Memory can be set in device 200, can be independent storage clothes
Business device.Abnormal gait testing result and Disease index computing module 270 of the memory for the output of clock synchronization domain analyzing module 260
The Disease index of output is stored, and with the progression of the disease data information of record storage patient, doctor is facilitated to check.Simultaneously
The data information stored in memory is also used as the basic data of research Parkinson's state of an illness, thus as frequency-domain calculations module
240 and time-domain calculation module 260 carry out gait judgement benchmark, further increase the accuracy of each deterministic process.
The present invention also provides a kind of for alleviating the device 300 of Parkinson's abnormal gait, as shown in Figure 3.Device 300 includes
Detection device 310 and stimulating apparatus 320.Detection device 310 includes in any of the preceding embodiments for detecting Parkinson's exception
The device of gait.Stimulating apparatus 320 is connect with detection device 310, the abnormal gait detection for being exported according to detection device 310
As a result fixed point stimulation is carried out to patient, so that patient be helped to restore normal.Fixed point stimulation is carried out by stimulating apparatus 320, it can be with
Avoid always to patient apply stimulation cause to occur being immunized, decreased effectiveness problem.
Each technical characteristic of embodiment described above can be combined arbitrarily, for simplicity of description, not to above-mentioned reality
It applies 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, all should be considered as described in this specification.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
It cannot therefore be construed as limiting the scope of the patent.It should be pointed out that coming for those of ordinary skill in the art
It says, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to protection of the invention
Range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. a kind of for detecting the device of Parkinson's abnormal gait characterized by comprising
Gait detection sensor detects for the gait to patient and exports detection signal；
Time-domain analysis module is connect 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 is connect 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 is connect with the frequency-domain analysis module, and 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 judging result is exported when patient's gait is abnormal gait；
Time-domain calculation module is connect 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；And
The correction module, the correction module are also connect with the frequency-domain calculations module, for judging to tie according to the frequency domain
Fruit and the abnormal gait testing result are generated for the corrected school of the parameter preset to the time-domain calculation module
Positive factor；The time-domain calculation module is also used to be corrected the parameter preset according to the correction factor.
2. the apparatus according to claim 1, which is characterized in that the frequency-domain calculations module is also used to judging the patient
When gait is abnormal gait, the abnormal gait type of the patient is determined according to the frequency-domain index；The abnormal gait type
Freeze and start to walk at least two in hardly possible including completely inactive, festinating gait, gait；The time-domain calculation module is also used to
When judging patient's gait for abnormal gait, the abnormal gait type of the patient is determined according to the time domain index, and defeated
Determining abnormal gait information out.
3. the apparatus according to claim 1, which is characterized in that the time-domain calculation module be used for by the time domain index with
Time domain targets threshold compares 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 to judge the gait of patient.
4. the apparatus according to claim 1, which is characterized in that further include Disease index computing module；The Disease index
Computing module is connect 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.
5. device according to claim 4, which is characterized in that the Disease index computing module is also used to obtain patient's
Administration time；There is the appearance duration of abnormal gait for counting after patient medication in per hour in the Disease index computing module
And number, with and export after the Disease index is calculated.
6. the apparatus according to claim 1, which is characterized in that further include memory；The memory is for storing patient
Information, and store the abnormal gait testing result and time domain corresponding with the abnormal gait testing result of described device output
Index and frequency-domain index.
7. the apparatus according to claim 1, which is characterized in that the gait detection sensor includes pressure sensor, adds
At least one of velocity sensor and myoelectric sensor sensor；The time-domain analysis module includes multiple time-domain analysis lists
Member；Each time-domain analysis unit is connect with a sensor, carries out time-domain analysis 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 calculations module is used to judge according to the frequency-domain index that each frequency-domain analysis unit exports in real time the gait of patient；When described
Domain computing module is used to judge patient's gait according to the time domain index that each time-domain analysis unit exports in real time.
8. device according to claim 7, which is characterized in that the pressure sensor is for acquiring patient's forefoot with after
Pressure at heel；The acceleration transducer is for acquiring at patient's ankle perpendicular to the forward acceleration of shank；The flesh
Electric transducer is used to acquire the electromyography signal at patient's shank gastrocnemius and tibialis.
9. the apparatus according to claim 1, which is characterized in that the gait detection sensor includes wireless communication unit；
It is provided with wireless communication unit in the time-domain analysis module and the frequency-domain analysis module, is sensed with being detected with the gait
Device establishes wireless communication.
10. a kind of for alleviating the device of Parkinson's abnormal gait characterized by comprising
Detection device, the detection device include as described in claim 1~9 is any for detecting Parkinson's abnormal gait
Device；And stimulating apparatus, it is connect with the detection device, the abnormal gait for being exported according to the detection device detects knot
Fixed point stimulation is carried out to patient when fruit.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
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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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| CN201611035951.8A CN107361773B (en) | 2016-11-18 | 2016-11-18 | For detecting, alleviating the device of Parkinson's abnormal gait |
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| CN107361773A CN107361773A (en) | 2017-11-21 |
| CN107361773B true CN107361773B (en) | 2019-10-22 |
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| WO (1) | WO2018090604A1 (en) |
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| CN108063698B (en) * | 2017-12-15 | 2020-05-12 | 东软集团股份有限公司 | Device abnormality detection method and device, 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 |
| 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 |
| CN110151190A (en) * | 2019-05-23 | 2019-08-23 | 西南科技大学 | A method and system for postoperative rehabilitation monitoring in orthopedics |
| 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 |
| CN111631722B (en) * | 2020-05-18 | 2021-11-09 | 北京航空航天大学 | Parkinson's gait analysis system and method based on optical fiber microbend pressure sensing |
| 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 |
| WO2022193330A1 (en) * | 2021-03-19 | 2022-09-22 | 深圳市韶音科技有限公司 | Exercise monitoring method and system |
| CN114271836B (en) * | 2022-01-25 | 2023-08-29 | 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 |
| CN115824614B (en) * | 2022-12-23 | 2025-10-24 | 核工业理化工程研究院 | A sensor signal detection and analysis platform and its application in rotating machinery |
| CN119837522B (en) * | 2025-03-13 | 2025-05-27 | 深圳市睿法生物科技有限公司 | Establishment of a model, method and storage medium for assessing gait impairment in Parkinson's disease |
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| CN107361773A (en) | 2017-11-21 |
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