CN106295508A - Emotion identification System and method for - Google Patents
Emotion identification System and method for Download PDFInfo
- Publication number
- CN106295508A CN106295508A CN201610594758.1A CN201610594758A CN106295508A CN 106295508 A CN106295508 A CN 106295508A CN 201610594758 A CN201610594758 A CN 201610594758A CN 106295508 A CN106295508 A CN 106295508A
- Authority
- CN
- China
- Prior art keywords
- index
- emotion
- domain index
- frequency
- spacing
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/12—Classification; Matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/08—Feature extraction
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Signal Processing (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Measurement And Recording Of Electrical Phenomena And Electrical Characteristics Of The Living Body (AREA)
Abstract
The present invention relates to a kind of Emotion identification system, run in electronic installation, this system includes: acquisition module, RR spacing between adjacent R ripple in the ECG data obtaining user；Computing module, for calculating time domain index, frequency-domain index and the nonlinear indicator of described RR spacing；Processing module, for analyzing the energy value of user emotion according to described time domain index, frequency-domain index and nonlinear indicator；And identification module, for according to described energy value identification user emotion.By the present invention so that user emotion more readily identifies, improve discrimination.
Description
[technical field]
The present invention relates to ecg analysis technical field, particularly relate to a kind of Emotion identification system and method.
[background technology]
Research shows, emotion have impact on health to a great extent.The negative feeling such as dejected, anxious, angry will resistance
Human immune system is hindered to work so that body resistance is deteriorated, and is more easy to bacterial infection and virus, or is difficult to rehabilitation from disease.And
Happily, loosen etc. active mood and be then of value to physically and mentally healthy.Therefore, emotional health will be related to daily life, and and
Time ground understand in self in emotion and take measures on customs clearance and be adjusted being particularly important.
The mental status that human body is current can be analyzed traditionally by analyzing brain wave and the dependency of emotion.But the method
There is brain electricity equipment the most portable, gather inconvenience, the shortcomings such as Consumer's Experience is bad.
Prior art there is also emotion analysis based on heart rate variability time domain and frequency domain index of correlation.Heart rate variability
(Heart Rate Variability, HRV) is a kind of common and ripe ecg analysis method, and usual method is from electrocardiogram
(Electrocardiography, ECG) extract R crest value, the time of an adjacent R heart beating of crest value time interval, i.e.
Analyze the change at RR interval.Heart except itself rhythmical discharge activiy cause beat in addition to, also by autonomic nervous system institute
Regulation and control.The regulation and control of past 20 years existing many documents display autonomic nervous system and cardiovascular system and parasympathetic life
There is significant relation in reason function, i.e. heart rate variability performance the most dynamically reflects human body emotional state.But this kind of side
Method have ignored the non-linear factor between heart rate variability and emotion, and analytical effect is the best.
There is also a common face Emotion identification method at present, i.e. by camera acquisition facial image is analyzed,
Obtain current emotional state.The final result that the method obtains is the expression emotion that face is corresponding, there is bigger subjective shadow
Ring, and variant in actual emotion in human body, i.e. can not react current emotional states exactly.
[summary of the invention]
Embodiment of the present invention is mainly solving the technical problems that provide a kind of Emotion identification method, it is possible to identify user's
Different emotions.
For solving above-mentioned technical problem, the technical scheme that embodiment of the present invention uses is:
The present invention provides a kind of Emotion identification method, is applied in electronic installation, and the method includes: obtaining step, obtains
RR spacing between adjacent R ripple in the ECG data of user；Calculation procedure, calculates the time domain index of described RR spacing, frequency domain refers to
Mark and nonlinear indicator；Process step, analyze the work of user emotion according to described time domain index, frequency-domain index and nonlinear indicator
Force value；And identification step, according to described energy value identification user emotion.
Further, described obtaining step includes: use Kalman filter to carry out described ECG data at denoising
Reason；ECG R wave extraction algorithm is used to extract the R crest value in described ECG data；Calculate again in described ECG data
RR spacing between adjacent R ripple.
Further, described time domain index includes short distance heart rate fluctuations index, and described frequency-domain index includes parasympathetic god
Through activity index；
Further, described short distance heart rate fluctuations index is come by obtaining the root-mean-square of described RR spacing difference quadratic sum
Calculate；Described parasympathetic nervous activity index is calculated by fast Fourier transform；Described nonlinear indicator passes through FRACTAL DIMENSION
Number calculating method calculates.
Further, what described time domain index according to described energy value, frequency-domain index and nonlinear indicator were set up is polynary
The calculated value of equation of linear regression.
For solving above-mentioned technical problem, another technical scheme that embodiment of the present invention uses is:
The present invention provides a kind of Emotion identification system, runs in electronic installation, and this system includes: acquisition module, is used for
RR spacing between adjacent R ripple in the ECG data of acquisition user；Computing module, refers to for calculating the time domain of described RR spacing
Mark, frequency-domain index and nonlinear indicator；Processing module, for dividing according to described time domain index, frequency-domain index and nonlinear indicator
The energy value of analysis user emotion；And identification module, for according to described energy value identification user emotion.
Further, use Kalman filter that described ECG data is carried out denoising；Employing ECG R wave carries
Take algorithm and extract the R crest value in described ECG data；Calculate in described ECG data RR spacing between adjacent R ripple again.
Further, described time domain index includes short distance heart rate fluctuations index, and described frequency-domain index includes parasympathetic god
Through activity index.
Further, described short distance heart rate fluctuations index is come by obtaining the root-mean-square of described RR spacing difference quadratic sum
Calculate；Described parasympathetic nervous activity index is calculated by fast Fourier transform；Described nonlinear indicator passes through FRACTAL DIMENSION
Number calculating method calculates.
Further, the multiple linear that according to energy value, described time domain index, frequency-domain index and nonlinear indicator are set up
The value that regression equation calculation obtains.
Embodiment of the present invention provides the benefit that: be different from the situation of prior art, and embodiment of the present invention is comprehensively examined
Consider significant linearity and non-linearity factor between heart rate variability and emotion so that user emotion more readily identifies, and improves knowledge
Not rate.
[accompanying drawing explanation]
Fig. 1 is the running environment figure of Emotion identification system preferred embodiment of the present invention.
Fig. 2 is the functional block diagram of Emotion identification system preferred embodiment of the present invention.
Fig. 3 is the flow chart of the preferred embodiment of Emotion identification method of the present invention.
Fig. 4 is the detail flowchart of step S32 in Fig. 3.
Reference:
[detailed description of the invention]
In order to make the purpose of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, right
The present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, not
For limiting the present invention.
As long as additionally, technical characteristic involved in each embodiment of invention described below is the most not
The conflict of composition just can be mutually combined.
Refering to shown in Fig. 1, it it is the running environment figure of Emotion identification system preferred embodiment of the present invention.Described Emotion identification system
System 10 runs in electronic installation 1, and described electronic installation 1 can be desktop computer, notebook computer, panel computer, intelligence
Can mobile phone, personal digital assistant (Personnal Digital Assistant, PDA) etc..In the present embodiment, described shifting electricity
Sub-device 1 includes, but not limited to display screen 11, network modules 12, memorizer 13 and processor 14.Between each element above-mentioned
Electrical connection.
In the present embodiment, this display screen 11 can have touch function, such as liquid crystal (Liquid Crystal
Display, LCD) display screen or Organic Light Emitting Diode (Organic Light-Emitting Diode, OLED) display screen.
This display screen 11 is used for showing that Emotion identification result is for reference.
Described network modules 12 is for providing network communication merit by wired or wireless network transmission means for electronic installation 1
Energy.This cable network can be any type of traditional wire communication, such as the Internet, LAN.This wireless network can be
Any type of conventional wireless communication, such as radio, Wireless Fidelity (Wireless Fidelity, WIFI), honeycomb, satellite,
Broadcast etc..Wireless communication technique can include, but not limited to global system for mobile communications (Global System for
Mobile Communications, GSM), GPRS (general packet radio service) (General Packet Radio Service,
GPRS), CDMA (Code Division Multiple Access, CDMA), WCDMA (W-CDMA),
CDMA2000, IMT single carrier (IMT Single Carrier), enhanced data rates for gsm evolution (Enhanced Data
Rates for GSM Evolution, EDGE), Long Term Evolution (Long-Term Evolution, LTE), senior for a long time
Evolution technology, time-division Long Term Evolution (Time-Division LTE, TD-LTE), high performance radio LAN (High
Performance Radio Local Area Network, HiperLAN), high performance radio wide area network (High
Performance Radio Wide Area Network, HiperWAN), local multiple spot distribute business (Local
Multipoint Distribution Service, LMDS), full micro-wave access global inter communication (Worldwide
Interoperability for Microwave Access, WiMAX), ZigBee protocol (ZigBee), bluetooth, orthogonal frequency division multiplexing
With technology (Flash Orthogonal Frequency-Division Multiplexing, Flash-OFDM), Large Copacity sky
Division multiple access (High Capacity Spatial Division Multiple Access, HC-SDMA), General Mobile electricity
Communication system (Universal Mobile Telecommunications System, UMTS), Universal Mobile Telecommunications System time-division
Duplex (UMTS Time-Division Duplexing, UMTS-TDD), evolved high-speed packet access (Evolved High
Speed Packet Access, HSPA+), TD SDMA (Time Division Synchronous Code
Division Multiple Access, TD-SCDMA), evolution data optimization (Evolution-Data Optimized,
EV-DO), DECT (Digital Enhanced Cordless Telecommunications, DECT) and
Other.In the present embodiment, described electronic installation 1 is connected with ecg measurement instrument by described network modules 12.
Described memorizer 13 can be the internal memory of electronic installation 1 itself, it is also possible to be External memory equipment, such as smart media
Card (Smart Media Card), safe digital card (Secure Digital Card), flash memory cards (Flash Card)
Deng.Described memorizer 13 stores vibrant value table corresponding with emotion classification.Described energy value table corresponding with emotion classification describes
Be for user emotion classification corresponding when different numerical value or different numerical range.Such as, when described energy value be first preset
When value or the first preset range, corresponding user emotion is frightened, when described energy value is the second preset value or the second preset range
Time, corresponding user emotion is sad.
Also storing the program code of identification system 10 of being in a bad mood in described memorizer 13, this Emotion identification system 10 is used for leading to
Cross and analyze the ECG data gathered from described ecg measurement instrument, thus obtain user emotion energy value, and according to described
Energy value correspondence identification user's current emotional feature, detailed process sees the description of Fig. 3.
Described processor 14 can be CPU, or other are able to carry out the number of described Emotion identification system 10
According to processing chip.
Refering to shown in Fig. 2, described Emotion identification system 10 can be divided into one or more module, one or many
Individual module stores is in described memorizer 13, and is configured to one or more processor (the present embodiment is a processor 14)
Perform, to complete the present invention.Such as, described Emotion identification system 10 is divided into acquisition module 101, computing module 102, processes
Module 103 and identification module 104.Module alleged by the present invention is to complete the program segment of a specific function, suitableeer than program
Share in describing software execution process in the electronic apparatus 1, the detailed functions about each module will the most specifically be retouched
State.
Acquisition module 101 for obtaining the ECG data of user from described ecg measurement instrument.In present embodiment
In, use heart rate variability analysis to analyze the emotion of user.Described heart rate variability analysis is a kind of assessment autonomic nervous system
The important method of system function.Autonomic nervous system divides a sympathetic nervous system and parasympathetic nervous system.Sympathetic nervous system can
Make that palpitating speed, platycoria, enterogastric peristalsis are slack-off, perspire increase and muscle is stronger, to deal with emergency；Parasympathetic
Nervous system can make that heart beating is slack-off, contracted pupil, enterogastric peristalsis are accelerated, perspired the reduction of income and loosening all muscles, and allow human body in loosening
State；Both mutually keep balance.Heart rate fluctuations may utilize electrocardiogram and is analyzed, and in electrocardiogram, R ripple is the most significant
Waveform is easily detected, and R spacing represents heart rate, therefore the most often represents heartbeat interval with RR spacing.Heart rate fluctuations is analyzed
Time-domain analysis and frequency-domain analysis two large divisions can be divided into.
In the present embodiment, described ecg measurement instrument obtains with a Preset Time (such as five minutes) linear measure longimetry
The data that the ECG data of user obtains as described acquisition module 101 every time.
Described acquisition module 101 is additionally operable to obtain in described ECG data RR spacing (R-R between adjacent R ripple
interval)。
Specifically, in the present embodiment, described ECG data is first carried out at denoising by described acquisition module 101
Reason.Such as, use Kalman filter that above-mentioned ECG data is filtered, remove noise and interference.Then, described acquisition
Module 101 uses ECG R wave extraction algorithm to extract the R crest value in described ECG data；Calculate described electrocardiogram number again
According to RR spacing between middle adjacent R ripple.In the present embodiment, described acquisition module 101 branch office time series frequency acquisition calculates
The time interval of adjacent two R ripples, i.e. can get in described ECG data RR spacing between adjacent R ripple.
Computing module 102 is for calculating time domain index, frequency-domain index and the nonlinear indicator of described RR spacing.
In the present embodiment, described time domain index includes short distance heart rate fluctuations index (RMSSD), described computing module
102 calculate the short distance heart rate fluctuations index of described RR spacing by the root-mean-square of acquisition heartbeat interval squared difference sum.?
In present embodiment, described heartbeat interval is RR spacing, and described short distance heart rate fluctuations index is obtained by described computing module 102
The root-mean-square taking RR spacing difference quadratic sum calculates.When the autonomic nervous system of human body is dominated by parasympathetic nervous, the most such as
When really user is in negative feeling, this time domain index value will present rising trend.
In the present embodiment, described frequency-domain index includes that parasympathetic nervous activity index, described computing module 102 pass through
Fast Fourier transform calculates described parasympathetic nervous activity index.It is 0.15-0.4Hz that described computing module 102 intercepts frequency
High frequency power represent described sense neural activity index.Described high frequency power describe be high-frequency range normal heartbeat between the phase
Variance.
In the present embodiment, described nonlinear indicator is obtained by fractal dimension computational methods.Described nonlinear indicator
Can the most significantly reflect cardiovascular system dependency rule, under user's negative feeling, this desired value has downward trend.
Processing module 103 is for analyzing the energy value of user emotion.In the present embodiment, institute according to described energy value
State the calculated value of multiple linear regression equations that time domain index, frequency-domain index and nonlinear indicator are set up.Such as, if vigor
Value is time domain index RMSSD for E, x, and y is frequency-domain index parasympathetic nervous activity index, and z is nonlinear indicator, then energy value table
The multiple linear regression reached can be: E=a*x+b*y+c*z (a, b, c are variation coefficient).
Identification module 104 is for according to described energy value identification user emotion.In the present embodiment, identification module 104
By the energy value table corresponding with emotion classification of storage in consults memory 13 to identify user emotion, and Query Result is shown
For reference in display screen 11.
Specifically, the emotion classification that described energy value size correspondence mappings user is different.Such as, described energy value from
The little emotion classification to big correspondence respectively is frightened, sad, tranquil, loosens, glad etc..
Refering to shown in Fig. 3, it it is the flow chart of Emotion identification method preferred embodiment of the present invention.According to different demands, this stream
In journey figure, the order of step can change, and some step can be omitted or merge.
Step S31, acquisition module 101 obtains the ECG data of user from described ecg measurement instrument.In this enforcement
In mode, the ECG data of the user that described ecg measurement instrument obtains with a Preset Time (such as five minutes) linear measure longimetry
The data every time obtained as described acquisition module 101..
Step S32, described acquisition module 101 obtains in described ECG data RR spacing between adjacent R ripple.Concrete and
Speech, described acquisition module 101 obtains idiographic flow Shenfu Fig. 4 of described RR spacing.
Step S33, computing module 102 calculates time domain index, frequency-domain index and the nonlinear indicator of described RR spacing.
In the present embodiment, described time domain index includes short distance heart rate fluctuations index (RMSSD), described computing module
The 102 short distance heart rate fluctuations calculating described RR spacing by obtaining the root-mean-square of phase squared difference sum between normal heartbeat refer to
Mark.When the autonomic nervous system of human body is dominated by parasympathetic nervous, if i.e. user is in negative feeling, this time domain index
Value will present rising trend.
In the present embodiment, described frequency-domain index includes that parasympathetic nervous activity index, described computing module 102 pass through
Fast Fourier transform calculates described parasympathetic nervous activity index.It is 0.15-0.4Hz that described computing module 102 intercepts frequency
High frequency power represent described sense neural activity index.Described high frequency power describe be high-frequency range normal heartbeat between the phase
Variance.
In the present embodiment, described nonlinear indicator is obtained by fractal dimension computational methods.Described nonlinear indicator
Can the most significantly reflect cardiovascular system dependency rule, under user's negative feeling, this desired value has downward trend.
Step S34, processing module 103 analyzes the energy value of user emotion.In the present embodiment, described energy value is root
The calculated value of multiple linear regression equations set up according to described time domain index, frequency-domain index and nonlinear indicator.Such as, if
Energy value be E, x be time domain index RMSSD, y is frequency-domain index parasympathetic nervous activity index, and z is nonlinear indicator, then vigor
The multiple linear regression that value is expressed can be: E=a*x+b*y+c*z (a, b, c are variation coefficient).
Step S35, identification module 104 is for according to described energy value identification user emotion.In the present embodiment, identify
The energy value table corresponding with emotion classification that module 104 is passed through to store in consults memory 13 is to identify user emotion, and will inquire about
It is for reference that result is shown in display screen 11.Specifically, the emotion that described energy value size correspondence mappings user is different
Classification.Such as, the emotion classification that described energy value is the most corresponding from small to large is frightened, sad, tranquil, loosens, glad etc..
Refering to shown in Fig. 4, it it is the detailed step flow chart of step S32.According to different demands, in this flow chart, step is suitable
Sequence can change, and some step can be omitted or merge.
Step S320, carries out denoising to described ECG data.Such as, use Kalman filter to above-mentioned electrocardio
Diagram data filters, and removes noise and interference.
Step S321, uses ECG R wave extraction algorithm to extract the R crest value in described ECG data；
Step S322, calculates in described ECG data RR spacing between adjacent R ripple.In the present embodiment, obtain described in
Delivery block 101 calculates the time interval of adjacent two R ripples by time series frequency acquisition, i.e. can get described ECG data
RR spacing between middle adjacent R ripple.
The foregoing is only embodiments of the present invention, not thereby limit the scope of the claims of the present invention, every utilization is originally
Equivalent structure or equivalence flow process that description of the invention and accompanying drawing content are made convert, or are directly or indirectly used in what other were correlated with
Technical field, is the most in like manner included in the scope of patent protection of the present invention.
Claims (10)
1. an Emotion identification method, is applied in electronic installation, it is characterised in that the method includes:
Obtaining step, RR spacing between adjacent R ripple in the ECG data of acquisition user；
Calculation procedure, calculates time domain index, frequency-domain index and the nonlinear indicator of described RR spacing；
Process step, according to described time domain index, frequency-domain index and nonlinear indicator, analyze the energy value of user emotion；And
Identification step, according to described energy value identification user emotion.
2. Emotion identification method as claimed in claim 1, it is characterised in that described obtaining step includes: use Kalman's filter
Ripple device carries out denoising to described ECG data；ECG R wave extraction algorithm is used to extract the R in described ECG data
Crest value；Calculate in described ECG data RR spacing between adjacent R ripple again.
3. Emotion identification method as claimed in claim 1, it is characterised in that described time domain index includes short distance heart rate fluctuations
Index, described frequency-domain index includes parasympathetic nervous activity index.
4. Emotion identification method as claimed in claim 1, it is characterised in that described short distance heart rate fluctuations index is by obtaining
The root-mean-square of described RR spacing difference quadratic sum calculates；Described parasympathetic nervous activity index is come by fast Fourier transform
Calculate；Described nonlinear indicator is calculated by fractal dimension computational methods.
5. Emotion identification method as claimed in claim 1, it is characterised in that described time domain index according to described energy value,
The calculated value of multiple linear regression equations that frequency-domain index and nonlinear indicator are set up.
6. an Emotion identification system, runs in electronic installation, it is characterised in that this system includes:
Acquisition module, RR spacing between adjacent R ripple in the ECG data obtaining user；
Computing module, for calculating time domain index, frequency-domain index and the nonlinear indicator of described RR spacing；
Processing module, for analyzing the energy value of user emotion according to described time domain index, frequency-domain index and nonlinear indicator；And
Identification module, for according to described energy value identification user emotion.
7. Emotion identification system as claimed in claim 6, it is characterised in that described acquisition module is additionally operable to: use Kalman
Wave filter carries out denoising to described ECG data；ECG R wave extraction algorithm is used to extract in described ECG data
R crest value；Calculate in described ECG data RR spacing between adjacent R ripple again.
8. Emotion identification system as claimed in claim 6, it is characterised in that described time domain index includes short distance heart rate fluctuations
Index, described frequency-domain index includes parasympathetic nervous activity index.
9. Emotion identification system as claimed in claim 6, it is characterised in that described short distance heart rate fluctuations index is by obtaining
The root-mean-square of described RR spacing difference quadratic sum calculates；Described parasympathetic nervous activity index is come by fast Fourier transform
Calculate；Described nonlinear indicator is calculated by fractal dimension computational methods.
10. Emotion identification system as claimed in claim 6, it is characterised in that described time domain index, frequency domain according to energy value
The calculated value of multiple linear regression equations that index and nonlinear indicator are set up.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610594758.1A CN106295508A (en) | 2016-07-26 | 2016-07-26 | Emotion identification System and method for |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610594758.1A CN106295508A (en) | 2016-07-26 | 2016-07-26 | Emotion identification System and method for |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106295508A true CN106295508A (en) | 2017-01-04 |
Family
ID=57652834
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610594758.1A Pending CN106295508A (en) | 2016-07-26 | 2016-07-26 | Emotion identification System and method for |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106295508A (en) |
Cited By (26)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107071154A (en) * | 2017-03-22 | 2017-08-18 | 广东小天才科技有限公司 | Prompting method in call process and mobile device |
| CN107195196A (en) * | 2017-06-21 | 2017-09-22 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system |
| CN107195195A (en) * | 2017-06-21 | 2017-09-22 | 深圳市盛路物联通讯技术有限公司 | A kind of parking guidance system and method based on parking lot |
| CN107203821A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall booking method and system |
| CN107204127A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of stopping guide system and method based on free parking lot |
| CN107204123A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of vehicle parking guiding system and method for combination Weather information |
| CN107230384A (en) * | 2017-06-21 | 2017-10-03 | 深圳市盛路物联通讯技术有限公司 | Based on the stopping guide system and method for expecting parking duration and Weather information |
| CN107230383A (en) * | 2017-06-21 | 2017-10-03 | 深圳市盛路物联通讯技术有限公司 | It is a kind of based on the parking position guiding method bidded and system |
| CN107240292A (en) * | 2017-06-21 | 2017-10-10 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system of technical ability of being stopped based on driver itself |
| CN107248315A (en) * | 2017-06-21 | 2017-10-13 | 深圳市盛路物联通讯技术有限公司 | The commending system and method in a kind of parking navigation path |
| CN107248314A (en) * | 2017-06-21 | 2017-10-13 | 深圳市盛路物联通讯技术有限公司 | A kind of stopping guide system and method based on parking lot |
| CN107256640A (en) * | 2017-06-21 | 2017-10-17 | 深圳市盛路物联通讯技术有限公司 | It is a kind of can free parking stall parking navigation path generating system and method |
| CN107293148A (en) * | 2017-06-21 | 2017-10-24 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall guidance method and system |
| CN107301786A (en) * | 2017-06-21 | 2017-10-27 | 深圳市盛路物联通讯技术有限公司 | A kind of generation method and system in stopping guide path |
| CN107424345A (en) * | 2017-06-19 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | A kind of outdoor parking charging method and system based on wireless vehicle mounted terminal |
| CN107424435A (en) * | 2017-06-21 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | Parking stall reservation system and method under a kind of urban environment |
| CN107424434A (en) * | 2017-06-21 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall reserving method and system applied to smart city |
| CN107452078A (en) * | 2017-06-19 | 2017-12-08 | 深圳市盛路物联通讯技术有限公司 | A kind of parking payment management method and system |
| CN107464442A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system of the duration that stopped based on expectation |
| CN107464443A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking lot booking system and method based on race |
| CN107464441A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of quotation is the same as racing to be the first to answer a question the parking guide method being combined and system |
| CN107464444A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall booking method and system based on mobile network |
| CN108653898A (en) * | 2018-03-26 | 2018-10-16 | 广东小天才科技有限公司 | Music playing method for pacifying children and wearable device |
| CN108742660A (en) * | 2018-07-02 | 2018-11-06 | 西北工业大学 | A kind of Emotion identification method based on wearable device |
| CN110811646A (en) * | 2019-10-23 | 2020-02-21 | 北京点滴灵犀科技有限公司 | Emotional stress comprehensive detection and analysis method and device |
| FR3117330A1 (en) | 2020-12-14 | 2022-06-17 | Catie | METHOD FOR GENERATION OF A PHYSIOLOGICAL INDICATOR FROM THE HEART RATE, DEVICE |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100274147A1 (en) * | 2009-04-22 | 2010-10-28 | Abhilash Patangay | Detecting ischemia with nonlinear heart rate variability measures |
| CN102138789A (en) * | 2011-01-24 | 2011-08-03 | 无锡微感科技有限公司 | Dynamic electrocardiogram and motion recording and analyzing system |
| CN104644159A (en) * | 2015-03-04 | 2015-05-27 | 江苏物联网研究发展中心 | Tele electrocardiogram monitoring and diagnosing system |
| CN105193431A (en) * | 2015-09-02 | 2015-12-30 | 杨静 | Device for analyzing mental stress state of human body |
-
2016
- 2016-07-26 CN CN201610594758.1A patent/CN106295508A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100274147A1 (en) * | 2009-04-22 | 2010-10-28 | Abhilash Patangay | Detecting ischemia with nonlinear heart rate variability measures |
| CN102138789A (en) * | 2011-01-24 | 2011-08-03 | 无锡微感科技有限公司 | Dynamic electrocardiogram and motion recording and analyzing system |
| CN104644159A (en) * | 2015-03-04 | 2015-05-27 | 江苏物联网研究发展中心 | Tele electrocardiogram monitoring and diagnosing system |
| CN105193431A (en) * | 2015-09-02 | 2015-12-30 | 杨静 | Device for analyzing mental stress state of human body |
Non-Patent Citations (1)
| Title |
|---|
| 聂聃等: "基于脑电的情绪识别研究综述", 《中国生物医学工程学报》 * |
Cited By (29)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107071154A (en) * | 2017-03-22 | 2017-08-18 | 广东小天才科技有限公司 | Prompting method in call process and mobile device |
| CN107452078A (en) * | 2017-06-19 | 2017-12-08 | 深圳市盛路物联通讯技术有限公司 | A kind of parking payment management method and system |
| CN107424345A (en) * | 2017-06-19 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | A kind of outdoor parking charging method and system based on wireless vehicle mounted terminal |
| CN107230383A (en) * | 2017-06-21 | 2017-10-03 | 深圳市盛路物联通讯技术有限公司 | It is a kind of based on the parking position guiding method bidded and system |
| CN107424434A (en) * | 2017-06-21 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall reserving method and system applied to smart city |
| CN107204123A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of vehicle parking guiding system and method for combination Weather information |
| CN107230384A (en) * | 2017-06-21 | 2017-10-03 | 深圳市盛路物联通讯技术有限公司 | Based on the stopping guide system and method for expecting parking duration and Weather information |
| CN107203821A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall booking method and system |
| CN107240292A (en) * | 2017-06-21 | 2017-10-10 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system of technical ability of being stopped based on driver itself |
| CN107248315A (en) * | 2017-06-21 | 2017-10-13 | 深圳市盛路物联通讯技术有限公司 | The commending system and method in a kind of parking navigation path |
| CN107248314A (en) * | 2017-06-21 | 2017-10-13 | 深圳市盛路物联通讯技术有限公司 | A kind of stopping guide system and method based on parking lot |
| CN107256640A (en) * | 2017-06-21 | 2017-10-17 | 深圳市盛路物联通讯技术有限公司 | It is a kind of can free parking stall parking navigation path generating system and method |
| CN107293148A (en) * | 2017-06-21 | 2017-10-24 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall guidance method and system |
| CN107301786A (en) * | 2017-06-21 | 2017-10-27 | 深圳市盛路物联通讯技术有限公司 | A kind of generation method and system in stopping guide path |
| CN107195195A (en) * | 2017-06-21 | 2017-09-22 | 深圳市盛路物联通讯技术有限公司 | A kind of parking guidance system and method based on parking lot |
| CN107424435A (en) * | 2017-06-21 | 2017-12-01 | 深圳市盛路物联通讯技术有限公司 | Parking stall reservation system and method under a kind of urban environment |
| CN107204127A (en) * | 2017-06-21 | 2017-09-26 | 深圳市盛路物联通讯技术有限公司 | A kind of stopping guide system and method based on free parking lot |
| CN107195196A (en) * | 2017-06-21 | 2017-09-22 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system |
| CN107464442A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking induction method and system of the duration that stopped based on expectation |
| CN107464443A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking lot booking system and method based on race |
| CN107464441A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of quotation is the same as racing to be the first to answer a question the parking guide method being combined and system |
| CN107464444A (en) * | 2017-06-21 | 2017-12-12 | 深圳市盛路物联通讯技术有限公司 | A kind of parking stall booking method and system based on mobile network |
| CN107230384B (en) * | 2017-06-21 | 2020-09-25 | 深圳市盛路物联通讯技术有限公司 | Parking guidance system and method based on expected parking duration and weather information |
| WO2018233085A1 (en) * | 2017-06-21 | 2018-12-27 | 深圳市盛路物联通讯技术有限公司 | Mobile network based parking place reservation method and system |
| CN108653898A (en) * | 2018-03-26 | 2018-10-16 | 广东小天才科技有限公司 | Music playing method for pacifying children and wearable device |
| CN108742660A (en) * | 2018-07-02 | 2018-11-06 | 西北工业大学 | A kind of Emotion identification method based on wearable device |
| CN110811646A (en) * | 2019-10-23 | 2020-02-21 | 北京点滴灵犀科技有限公司 | Emotional stress comprehensive detection and analysis method and device |
| CN110811646B (en) * | 2019-10-23 | 2022-05-17 | 北京点滴灵犀科技有限公司 | Emotional stress comprehensive detection and analysis method and device |
| FR3117330A1 (en) | 2020-12-14 | 2022-06-17 | Catie | METHOD FOR GENERATION OF A PHYSIOLOGICAL INDICATOR FROM THE HEART RATE, DEVICE |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106295508A (en) | Emotion identification System and method for | |
| Yazdani et al. | Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier | |
| Sultan Qurraie et al. | ECG arrhythmia classification using time frequency distribution techniques | |
| Shenoy et al. | Shrinkage estimator based regularization for EEG motor imagery classification | |
| Dai et al. | Sparse EEG compressive sensing for web-enabled person identification | |
| Khowaja et al. | Toward soft real-time stress detection using wrist-worn devices for human workspaces | |
| EP2884884A1 (en) | Method and apparatus for low complexity spectral analysis of bio-signals | |
| Zarjam et al. | Characterization of memory load in an arithmetic task using non-linear analysis of EEG signals | |
| Leutheuser et al. | Comparison of real-time classification systems for arrhythmia detection on Android-based mobile devices | |
| Zhang et al. | Hierarchical feature fusion framework for frequency recognition in SSVEP-based BCIs | |
| Panahi et al. | Application of fractional Fourier transform in feature extraction from ELECTROCARDIOGRAM and GALVANIC SKIN RESPONSE for emotion recognition | |
| Athif et al. | WaveCSP: a robust motor imagery classifier for consumer EEG devices | |
| KR20120122617A (en) | Electroencephalography Classification Method for Movement Imagination and Apparatus Thereof | |
| Duan et al. | EEG feature selection method based on decision tree | |
| Khan et al. | Robust extraction of P300 using constrained ICA for BCI applications | |
| Lehtola et al. | Effects of noise and filtering on SVD-based morphological parameters of the T wave in the ECG | |
| Ahmadi et al. | Brain-computer interface signal processing algorithms: A computational cost vs. accuracy analysis for wearable computers | |
| Li et al. | Adaptation of motor imagery EEG classification model based on tensor decomposition | |
| Mukherjee et al. | Real-time mental stress detection technique using neural networks towards a wearable health monitor | |
| Zheng et al. | A novel consciousness emotion recognition method using ERP components and MMSE | |
| Bian et al. | Small data least-squares transformation (sd-LST) for fast calibration of SSVEP-based BCIs | |
| Guo et al. | Diagnosing Parkinson’s disease using multimodal physiological signals | |
| Kumar et al. | Integration of cloud computing in BCI: A review | |
| US20200337567A1 (en) | Systems and Methods of Arrhythmia Detection | |
| Lv et al. | A real-time physiological signal acquisition and analyzing method based on fractional calculus and stream computing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170104 |