CN106901751A - A kind of recognition methods of the speed movement status based on brain hemoglobin information - Google Patents
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Abstract
The invention discloses a kind of recognition methods of the speed movement status based on brain hemoglobin information, its step includes：(1), subject is autonomous under low middle three kinds of friction speed states high performs motion of riding；(2), the cortex HC information recorded for motion initial time, to close the difference of oxygen hemoglobin and deoxyhemoglobin as analytical parameters, rate of change average value based on corresponding difference, divides four frequency ranges to consider the parameter attribute of emphasis passage respectively；(3) three kinds of friction speed states, are recognized：Directly apply four frequency ranges under emphasis passage oxygen-containing and deoxyhemoglobin difference rate of change average value as characteristic vector, using extreme learning machine ELM algorithm recognition speed state grades.
Description
Technical field
It is more particularly to a kind of to be known based on cortex hemoglobin information the invention belongs to intelligent walk help, rehabilitation training technology
The implementation method of other lower extremity movement speed state.
Background technology
Shown according to Association for the Handicapped's data statistics, China's deformity quantity about 85,000,000, wherein physical disabilities number is accounted for
29.08%, wherein the lower limb walking disorder caused by the reason such as cerebral apoplexy and brain trauma more comes the more, wherein only cerebral apoplexy is annual
The patient of neopathy reaches 2,000,000 or so, and 70%~80% or so patient because deformity can not live on one's own life, their limb
Body obstacle brings very big burden to family and society, therefore the prognosis rehabilitation of these physical handicaps patient is particularly significant.
Due to China to rehabilitation prognosis train and recognize it is later, add at present be mostly on the market non intelligent passive type training aids
Tool, causes Rehabilitation training effect not good, and provide a kind of rehabilitation training mode with patient's active consciousness will to suffer from
Very big positive role is played in the prognosis rehabilitation of person, and for they live on one's own life again, the great possibility of offer of being socially reintegrated.
In order to improve the intelligent and rehabilitation training effect of rehabilitation training equipment, many research institutions are devoted to researching and developing base
Product is trained in the Novel rehabilitation of brain-computer interface technology.However, also there is following subject matter in current brain-computer interface technology：
1st, the brain-computer interface technology of implanted or semi-implantation type has been achieved for breakthrough, however it is necessary that will be miniature
In the cerebral gray matter of electrode implantation experimenter or on subdural cerebral cortex, immune response and callus group may be triggered
Knit, but also deposit psychology and ethics problem after the implantation, extensive use is still unsuitable at present.
2nd, the brain information measuring technology of non-intrusion type includes electroencephalogram (EEG), magneticencephalogram (MEG), functional magnetic resonance imaging
The technologies, wherein fMRI such as image (fMRI), Squares Framework for PET Image (PET) and near infrared spectrum cerebral function imaging (NIRS)
Spatial resolution with PET technologies is higher, but temporal resolution is low, and body is often confined to inactive state in test process,
There is very big binding character；The application requirement of MEG is fully shielded to external magnetic field, so presently mainly EEG and NIRS skills
Art is applied to help the elderly in the research and development of products help the disabled.But in the brain based on EEG signal~machine interface system research, conventional base
There is provided to stimulate and in VEP (VEP) and the extra stimulating apparatus of event related potential (P300) this two classes method needs
Produce Evoked ptential, and depend on people certain feel (such as vision), force experimenter synchronous with outside stimulus, due to it is long when
Between operation easily cause visual fatigue or reduce P300 current potentials conspicuousness, corresponding brain~the machine interface operation time should not mistake
It is long.And spontaneous brain electricity figure again relies on the spontaneous cerebration of user, only special thinking processes could produce detectable
Cerebration, it is necessary to experimenter carry out it is substantial amounts of training come produce AD HOC brain electricity, influenceed larger by subjective factor.Therefore,
Experiment is more to be completed, it is necessary to experimenter focuses under given conditions, and the action of realization is simply limited, lacks naturality with spirit
Activity, practicality is not strong.
Comparatively, the non-intrusion type of NIRS technologies, to test environment and subject's limitation less, cognitive activities from
Support to measure, need not largely be trained for a long time under right scene, the feature with preferable room and time resolution ratio
The advantages of make its brain~machine Application of Interface field have very big advantage.
The content of the invention
Goal of the invention：A kind of recognition methods of the speed movement status based on brain hemoglobin information is proposed, using non-
Cerebral cortex hemoglobin information during intrusive mood NIRS technical notes human motions so that autonomous control motion is without extraneous
Stimulate and early stage training, the tracking measurement and Real time identification speed movement status of brain biological information are realized under natural situation；And
Further the recognized motor pattern of fusion is in motion control, with improve help the elderly help the disabled it is intelligent, be Based Intelligent Control walk help/
Rehabilitation training equipment has established important theoretical foundation.
To achieve these goals, the present invention provides following technical scheme：
A kind of recognition methods of the speed movement status based on brain hemoglobin information, its step includes：
1st, subject is autonomous under low middle three kinds of friction speed states high performs motion of riding；
2nd, the cortex HC information recorded for motion initial time, to close oxygen hemoglobin and deoxidation
The difference of hemoglobin starts from each sampled point and combines above the 4th sampled point as analytical parameters, from the 5th sampled point
The difference rate of change in numerical computations corresponding 5 sampling periods, divides four frequency ranges to consider the parameter attribute of emphasis passage respectively；
Specific analytical method is as follows：
(1) time domain angle, applied statistics analysis method determines the stress test passage under three kinds of speed states；
(2) frequency domain angle, according to the power spectral density distribution situation of each TCH test channel under three kinds of speed states, emphasis is seen
Examine four frequency range (the first frequency ranges：0.01~0.03Hz, the second frequency range：0.03~0.06Hz, the 3rd frequency range：0.06~0.09Hz,
4th frequency range：0.09~0.12Hz) in the difference of oxygen-containing hemoglobin and deoxyhemoglobin of each stress test passage change
Speed average value；
3rd, three kinds of friction speed states are recognized：Directly apply four frequency ranges under emphasis passage oxygen-containing and deoxyhemoglobin
Difference rate of change average value as characteristic vector, using extreme learning machine ELM algorithm recognition speed state grades.
Beneficial effect:
1. psychology and ethics problem after invading are solved using the NIRS brain technologies for information acquisition of non-intrusion type, in motion
During carry out test, it is ensured that by recognizing model of movement result be used for walk-aid equipment control in one apply premise；Motion
Autonomous control causes to obtain Cerebral cortex biological information under the Nature condition of cognitive activities, increased the practicality of speed movement status
Value.
2. the rate of change based on Cerebral cortex HC recognizes speed movement status, and uses the conjunction blood red egg of oxygen
In vain with the relative change (difference) of deoxyhemoglobin as main index, recognition rate can be improved, reduce brain blood oxygenation information
The negative effect of cognitive activities is lagged behind, is conducive to quick recognition mode, established to provide control information to walk-aid equipment in time
Important early stage basis.
3. time domain and frequency domain information are combined, is conducive to more fully extracting characteristic feature and improving discrimination.
Brief description of the drawings
Fig. 1 is that experimentation of the invention moves timing diagram；
Fig. 2 is deutocerebrum cortex motion association region of the present invention and TCH test channel distribution map；
Fig. 3 is to close oxygen hemoglobin in the present invention under three kinds of different riding speed states before and after motion in each TCH test channel
With the relative change schematic diagram of deoxyhemoglobin (T1, T2 are two time periods before motion, and T3 is the post exercise time period,
Each time period is 1.04s)；
Fig. 4 is the corresponding frequency point of each main power density of TCH test channel under three kinds of different riding speed states in the present invention
Butut；
Fig. 5 be three kinds of frequency range features of riding speed state in the present invention (I, II, III, IV represent respectively four frequency ranges as
0.01-0.03Hz, 0.03-0.06Hz, 0.06-0.09Hz and 0.09-0.12Hz).
Specific embodiment
Embodiment：
1st, experimental design：Subject is in low middle (such as low speed high:30rpm, middling speed:60rpm, at a high speed:90rpm) friction speed
It is autonomous under state to perform motion of riding；The whole flow process and points for attention of experiment are explained to subject, in its natural state, successively
Complete motion of riding at different rates respectively；In whole experiment process, using near infrared light Brian Imaging equipment FORIE-3000
The tested cortex hemoglobin information of collection, each sampling period is 0.13 second.
Experiment idiographic flow：Before task starts, it is tested and keeps quiescent condition 2 minutes or so, the task of riding is started afterwards,
Task segment and rest section are alternately；The speed order for riding is successively respectively low speed, middling speed and high speed；Three tasks terminate
Afterwards, the task of riding of three kinds of speed is repeated.
The beginning and end of task completely by being tested oneself control, in spontaneous state, and the time of having a rest be also by
Tested control, informs the tested rest enough time before experiment, at least more than 25 seconds (but can not be controlled by several
System).
Experimental implementation person marks the beginning and end of tested task in experimentation with mark point.
The headgear with optical fiber is fixed on the subject crown during carrying out brain hemoglobin information gathering, during
Need head to have to rock too much, task 1, task 2 and task 3 represent ride section, middling speed of low speed and ride section and height respectively
Speed is ridden section (such as Fig. 1).
2nd, the cortex HC information recorded for motion initial time, to close oxygen hemoglobin and deoxidation
The difference of hemoglobin starts from each sampled point and combines above the 4th sampled point as analytical parameters, from the 5th sampled point
The difference rate of change in numerical computations corresponding 5 sampling periods, divides four frequency ranges to consider the parameter attribute of emphasis passage respectively；
1. time domain Step1：For each TCH test channel, calculated based on each sampled point and close oxygen hemoglobin and deoxidation blood
The difference (CZ) of Lactoferrin, based on characteristic index；
2. time domain Step2：For the corresponding difference of each TCH test channel (CZ), each is started from from the 5th sampled point
Sampled point combines the difference rate of change (CZ_K) in above numerical computations corresponding 5 sampling periods of the 4th sampled point, that is, exist
Data are smoothed in 0.65 (0.13*5) second；
3. time domain Step3：It is turnover to move starting point (the 2nd mark point position in Fig. 1), two times is taken before motion
Section T1 and T2, takes a time period T3 after motion, each time period is spaced 8 sampling periods, 9 sampled point (8 sampling periods
0.13*8=1.04 seconds altogether)；
4. time domain Step4：By analytic statistics variance (ANOVA1), if surveyed in the T1 and T2 of some TCH test channel
Difference rate of change average value (CZ_K) for obtaining is without significant difference, and T3 difference rate of changes respectively between T1 and T2
There were significant differences for average value (CZ_K), it is determined that it is emphasis TCH test channel to select；The corresponding stress test passage of each movement velocity
As shown in table 1 and Fig. 3；
5. frequency domain Step1：Difference (CZ) for each TCH test channel carries out power spectral-density analysis, >=0.01Hz
Band limits confirms main power density, excludes the influence of flip-flop, and main power density respective frequencies value is recorded respectively；
6. frequency domain Step2：The distribution (Fig. 4) of the main power density respective frequencies value according to three kinds of motions speed, cuts
Take (the filtering of four band informations：0.01-0.03Hz, 0.03-0.06Hz, 0.06-0.09Hz, 0.09-0.12Hz, due to indivedual
The main power spectral density respective frequencies value of some tested passages is in the range of 0.09-0.12Hz, so the frequency range retains), and pin
To data in the T3 time periods after motion, four statistical variances of frequency range (ANOVA1) are analyzed, from which further follow that conclusion：Friction speed
The magnitude relationship of each band energy and statistical discrepancy characteristic are significantly different (shown in Fig. 5) under state；Lower-speed state：Frequency range
Statistical average in 0.09-0.12Hz is significantly greater than other three frequency ranges, meanwhile, numerical value is obvious in frequency range 0.01-0.03Hz
More than numerical value in frequency range 0.06-0.09Hz；Middling speed state equally meets, and the statistical average in frequency range 0.09-0.12Hz is obvious
More than other three frequency ranges；Fast state：Statistical average in frequency range 0.01-0.03Hz is significantly less than other three frequency ranges.
3rd, three kinds of friction speed states are recognized：
1. pattern-recognition (training)：Using 9 emphasis passages in four CZ_K average values of frequency range as characteristic vector (4*
9=36), totally 42 groups of the test data (7 3 states of the people */repetitions of people * 2 times) at random from 7 people is trained；
2. pattern-recognition (differentiation)：Using ELM mode identification methods differentiate other two people 12 kinds of states (states of 2 people * 3/
2 repetitions of people *), differentiated according to the characteristic vector under every kind of state, and go out discrimination with actual result contrast conting；
3. average recognition rate is calculated：Repeat 1. 2. more than step 10 time, 7 personal datas to be randomly selected every time and is trained, separately
Outer 2 people is verified, based on the calculating average recognition rate of the recognition result of more than 10 times：Basic, normal, high fast three kinds of motions state it is flat
Equal discrimination is respectively 72.2%, 66.7%, 83.3%, and overall average discrimination is up to 74.1%.
The temporal signatures of the low middle three kinds of riding speed states high of table 1
Claims (5)
1. a kind of recognition methods of the speed movement status based on brain hemoglobin information, it is characterised in that its step includes：
(1), subject is autonomous under low middle three kinds of friction speed states high performs motion of riding；
(2), the cortex HC information recorded for motion initial time, to close oxygen hemoglobin and deoxidation blood
The difference of Lactoferrin starts from each sampled point and combines above the 4th number of sampled point as analytical parameters, from the 5th sampled point
Value calculates the difference rate of change in corresponding 5 sampling periods, divides four frequency ranges to consider the parameter attribute of emphasis passage respectively；
(3) three kinds of friction speed states, are recognized：Directly apply the oxygen-containing and deoxyhemoglobin of emphasis passage under four frequency ranges
Difference rate of change average value as characteristic vector, using extreme learning machine ELM algorithm recognition speed state grades.
2. the recognition methods of the speed movement status based on brain hemoglobin information according to claim 1, its feature
It is that in step (2), specific analytical method is as follows：
(1) time domain angle, applied statistics analysis method determines the stress test passage under three kinds of speed states；
(2) frequency domain angle, for each oxygen-containing hemoglobin of TCH test channel under three kinds of speed states and the difference of deoxyhemoglobin
Rate of change carries out the main power density respective frequencies of each TCH test channel in power spectral-density analysis, four frequency ranges of selective analysis
Distributional difference.
3. the recognition methods of the speed movement status based on brain hemoglobin information according to claim 1, its feature
It is, in step (1)：
Subject is in low speed:30rpm, middling speed:60rpm, at a high speed:It is autonomous under 90rpm friction speed states to perform motion of riding；It is whole
In individual experimentation, tested cortex hemoglobin information is gathered using near infrared light Brian Imaging equipment FORIE-3000, often
One sampling period is 0.13 second；
Experiment idiographic flow：Before task starts, it is tested and keeps quiescent condition 2 minutes or so, the task of riding, task is started afterwards
Section and rest section are alternately；The speed order for riding is successively respectively low speed, middling speed and high speed；After three tasks terminate, three
The planting speed of the task of riding is repeated；The beginning and end of task is controlled by being tested oneself completely, in spontaneous state,
And the time of having a rest is also, by tested control, the tested rest enough time to be informed before experiment, at least more than 25 seconds；It is real
Test the beginning and end that operator marks tested task in experimentation with mark point.
4. the recognition methods of the speed movement status based on brain hemoglobin information according to claim 2, its feature
It is that specific steps include：
1. time domain Step1：For each TCH test channel, calculated based on each sampled point and close oxygen hemoglobin and the blood red egg of deoxidation
White difference, based on characteristic index；
2. time domain Step2：For the corresponding difference of each TCH test channel, each sampled point knot is started from from the 5th sampled point
The difference rate of change in above numerical computations corresponding 5 sampling periods of the 4th sampled point is closed, so that data are carried out with smooth place
Reason；
3. time domain Step3：To move starting point as turnover, two time periods T1 and T2 are taken before motion, a time is taken after motion
Section T3, each time period is spaced 8 sampling periods, 9 sampled points；
4. time domain Step4：By analytic statistics variance (ANOVA1), if measured in the T1 and T2 of some TCH test channel
Difference rate of change average value does not have significant difference, and difference rate of change average values of the T3 respectively between T1 and T2 has aobvious
Write difference, it is determined that it is emphasis TCH test channel to select；
5. frequency domain Step1：Difference for each TCH test channel carries out power spectral-density analysis, in the band limits of >=0.01Hz
Confirm main power density, exclude the influence of flip-flop, main power density respective frequencies value is recorded respectively；
6. frequency domain Step2：The distribution of the main power density respective frequencies value according to three kinds of motions speed, intercepts four frequency ranges
Information, and for data in the T3 time periods after motion, four statistical variances of frequency range are analyzed, from which further follow that conclusion：It is not synchronized
The magnitude relationship of each band energy and statistical discrepancy characteristic are significantly different under degree state；Lower-speed state：Frequency range 0.09-0.12Hz
Interior statistical average is significantly greater than other three frequency ranges, meanwhile, numerical value is significantly greater than frequency range in frequency range 0.01-0.03Hz
Numerical value in 0.06-0.09Hz；Middling speed state equally meets, and the statistical average in frequency range 0.09-0.12Hz is significantly greater than other
Three frequency ranges；Fast state：Statistical average in frequency range 0.01-0.03Hz is significantly less than other three frequency ranges.
5. the recognition methods of the speed movement status based on brain hemoglobin information according to claim 1, its feature
It is that in step (3), specific steps include：
1. train：It is random to select using 9 emphasis passages in four difference rate of change average values of frequency range as characteristic vector
7 test datas of people are trained for 42 groups totally；
2. differentiate：12 kinds of states of other two people are differentiated using ELM mode identification methods, according to the characteristic vector under every kind of state
Differentiated, and gone out discrimination with actual result contrast conting；
3. average recognition rate is calculated：Repeat 1. 2. more than step 10 time, 7 personal datas to be randomly selected every time and is trained, in addition 2 people
Verified, average recognition rate is calculated based on the recognition result of more than 10 times：The average knowledge of basic, normal, high fast three kinds of motions state
Rate is not respectively 72.2%, 66.7%, 83.3%, and overall average discrimination is up to 74.1%.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563298A (en) * | 2017-08-08 | 2018-01-09 | 苏州大学 | The recognition methods for squatting up away state of imagination motion stage based on brain hemoglobin information |
| CN108932403A (en) * | 2018-07-02 | 2018-12-04 | 苏州大学 | Leave and the dynamic recognition methods of fortune based on brain hemoglobin information |
| CN109044365A (en) * | 2018-07-02 | 2018-12-21 | 苏州大学 | The recognition methods of two dimensional motion state based on brain hemoglobin information |
| CN109710065A (en) * | 2018-12-18 | 2019-05-03 | 苏州大学 | Walking based on brain hemoglobin information adjusts the recognition methods being intended to |
| CN113017622A (en) * | 2021-03-03 | 2021-06-25 | 苏州大学 | fNIRS-based imaginary object displacement direction decoding method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014128495A (en) * | 2012-12-28 | 2014-07-10 | Toshinori Kato | Biofunction measuring device, and biofunction measuring method and program |
| JP2014233458A (en) * | 2013-06-03 | 2014-12-15 | 株式会社島津製作所 | Moving image generation apparatus for brain function measurement and moving image generation system for brain function measurement |
| CN104375635A (en) * | 2014-08-14 | 2015-02-25 | 华中科技大学 | Quick near-infrared brain-computer interface method |
| CN104586407A (en) * | 2014-01-16 | 2015-05-06 | 清华大学 | Multi-parameter physiological indication detection device and detection method thereof |
| WO2016033118A1 (en) * | 2014-08-29 | 2016-03-03 | Incyphae Inc. | Method and apparatus for enhancing nervous function |
| CN105559760A (en) * | 2015-12-10 | 2016-05-11 | 深圳市智帽科技开发有限公司 | Human body pattern recognition method of head-mounted equipment |
-
2017
- 2017-01-06 CN CN201710009847.XA patent/CN106901751A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2014128495A (en) * | 2012-12-28 | 2014-07-10 | Toshinori Kato | Biofunction measuring device, and biofunction measuring method and program |
| JP2014233458A (en) * | 2013-06-03 | 2014-12-15 | 株式会社島津製作所 | Moving image generation apparatus for brain function measurement and moving image generation system for brain function measurement |
| CN104586407A (en) * | 2014-01-16 | 2015-05-06 | 清华大学 | Multi-parameter physiological indication detection device and detection method thereof |
| CN104375635A (en) * | 2014-08-14 | 2015-02-25 | 华中科技大学 | Quick near-infrared brain-computer interface method |
| WO2016033118A1 (en) * | 2014-08-29 | 2016-03-03 | Incyphae Inc. | Method and apparatus for enhancing nervous function |
| CN105559760A (en) * | 2015-12-10 | 2016-05-11 | 深圳市智帽科技开发有限公司 | Human body pattern recognition method of head-mounted equipment |
Non-Patent Citations (3)
| Title |
|---|
| HASHIMOTO等: ""Motion illusion activates the visual motion area of the brain: A near-infrared spectroscopy (NIRS) study"", 《BRAIN RESEARCH》 * |
| YANXIANGSUI等: ""Classification of Desired Motion Speed-based On Cerebral Hemoglobin Information"", 《2016 IEEE/SICE INTERNATIONAL SYMPOSIUM ON SYSTEM INTEGRATION》 * |
| 杜凯等: ""应用fNIRS技术对运动执行与运动想象脑激活模式的研究"", 《第三军医大学学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107563298A (en) * | 2017-08-08 | 2018-01-09 | 苏州大学 | The recognition methods for squatting up away state of imagination motion stage based on brain hemoglobin information |
| CN107563298B (en) * | 2017-08-08 | 2022-02-22 | 苏州大学 | Method for identifying squatting and walking state of imagination movement stage based on brain hemoglobin information |
| CN108932403A (en) * | 2018-07-02 | 2018-12-04 | 苏州大学 | Leave and the dynamic recognition methods of fortune based on brain hemoglobin information |
| CN109044365A (en) * | 2018-07-02 | 2018-12-21 | 苏州大学 | The recognition methods of two dimensional motion state based on brain hemoglobin information |
| CN108932403B (en) * | 2018-07-02 | 2021-09-14 | 苏州大学 | Brain hemoglobin information-based resting state and movement state identification method |
| CN109710065A (en) * | 2018-12-18 | 2019-05-03 | 苏州大学 | Walking based on brain hemoglobin information adjusts the recognition methods being intended to |
| CN109710065B (en) * | 2018-12-18 | 2021-12-28 | 苏州大学 | Method for recognizing walking regulation intention based on brain hemoglobin information |
| CN113017622A (en) * | 2021-03-03 | 2021-06-25 | 苏州大学 | fNIRS-based imaginary object displacement direction decoding method |
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Application publication date: 20170630 |