CN107300971B - The intelligent input method and system propagated based on osteoacusis vibration signal - Google Patents
The intelligent input method and system propagated based on osteoacusis vibration signal Download PDFInfo
- Publication number
- CN107300971B CN107300971B CN201710433231.5A CN201710433231A CN107300971B CN 107300971 B CN107300971 B CN 107300971B CN 201710433231 A CN201710433231 A CN 201710433231A CN 107300971 B CN107300971 B CN 107300971B
- Authority
- CN
- China
- Prior art keywords
- signal
- vibration signal
- training sample
- central point
- class
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/014—Hand-worn input/output arrangements, e.g. data gloves
-
- G—PHYSICS
- G04—HOROLOGY
- G04G—ELECTRONIC TIME-PIECES
- G04G21/00—Input or output devices integrated in time-pieces
- G04G21/02—Detectors of external physical values, e.g. temperature
- G04G21/025—Detectors of external physical values, e.g. temperature for measuring physiological data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24143—Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Biophysics (AREA)
- General Health & Medical Sciences (AREA)
- Physiology (AREA)
- Human Computer Interaction (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- User Interface Of Digital Computer (AREA)
Abstract
The present invention provides a kind of intelligent input method propagated based on osteoacusis vibration signal and system, which includes: S1. receiving step, and smart machine vibrating sensor receives vibration signal, and carries out noise reduction process to vibration signal；S2. extraction step is detected using double threshold end-point detection method and extracts the vibration signal segment generated due to tapping human body appointed part；S3. processing step extracts signal characteristic, based on RNM algorithm come category signal position.The beneficial effects of the present invention are: the present invention not only solves the problems, such as smartwatch text input mode, moreover it is possible to reach high discrimination, and can quickly be inputted, the cruise duration of smartwatch is not lost yet.
Description
Technical field
The present invention relates to intelligent wearable device technical fields, more particularly to the intelligence propagated based on osteoacusis vibration signal
Input method and system.
Background technique
In recent years, we witnessed the fast development of intelligent wearable device, Intelligent bracelet, intelligent earphone, Brilliant Eyes
The wearable devices such as mirror, intelligent helmet and smartwatch increased popularity in people's daily life, is received by people.In intelligence
In the case that the innovative space of mobile phone constantly reduces and the market growth approaches saturation, intelligent wearable device is as mobile terminal
Next hot spot of industry is accepted by market extensively, is foretold invention for that will replace mobile phone.
Smartwatch is popular as a kind of portable device.But it small and exquisite and light also faces and can not evade
Technical problem.Since screen is smaller, it is only able to display several keys every time, and the display of other content can be blocked, efficiency is very low.
There are mainly three types of the modes of the text input of smartwatch realization at present: conventional keyboard, text prediction and speech recognition.But with top
Formula or not convenient, flexible enough or not safe enough.In the case where there is ambient noise conditions, phonetic recognization rate is extremely difficult to ideal effect,
It the use of voice input is in public that significant discomfort is closed and in terms of for protection user password and other privacies.Although
Multinational Research Team develops finger tracking identification technology, but the experience that user uses keyboard always can not be with large screen intelligence
The effectively quick comfortable text input comparison of energy mobile phone, such as the Research Team of Washington, DC university in 2016 realize millimeter
The accurate finger tracking technology of grade, allows user to realize the handwriting input based on acoustic location, but handwriting input on the mobile apparatus
Always still too slow, it is not able to satisfy the demand of people.Want to expand the market demand of smartwatch, just must depth excavate it and answer
With and solve the problems, such as text input.
Summary of the invention
The present invention provides a kind of intelligent input methods propagated based on osteoacusis vibration signal, include the following steps:
S1. receiving step, smart machine vibrating sensor receives vibration signal, and carries out at noise reduction to vibration signal
Reason；
S2. extraction step is detected using double threshold end-point detection method and is extracted due to tapping the generation of human body appointed part
Vibration signal segment；
S3. processing step extracts signal characteristic, based on RNM algorithm come category signal position.
As a further improvement of the present invention, include: in the S2. extraction step
High and low two thresholdings are arranged to the signal of processing in step S21；
Step S22 primarily determines knocking starting point, and works as signal when the energy or zero-crossing rate of signal surmount low threshold
Energy or zero-crossing rate break through high threshold, just determination the real starting point of knocking；When signal energy and zero-crossing rate are lower than simultaneously
When low threshold, signaling destination point is determined；
Step S23 retains the data of starting point to the end, obtains the vibration signal segment by tapping the back of the hand generation.
As a further improvement of the present invention, include: in the S3. processing step
Dissection signal is normalized in step S31, extracts Mel-frequency Cepstral Coefficients, obtains signal characteristic；
Step S32 divides signal characteristic using the RNM algorithm based on stochastic subspace and nearest central point algorithm
Class, and then judge beating position；
The step S32 includes:
Step S321, according to the difference of beating position, the signal characteristic that acquisition step S31 is obtained makees training sample, goes forward side by side
Row classification；
Step S322 calculates every a kind of central point of training sample using nearest central point algorithm；
Step S323, based on stochastic subspace, the class central point of test sample and training sample is repeatedly compared in subspace,
Obtain multiple classification results；
Step S324 uses simple majority Voting principle to classification results, and certain poll ratio is arranged, and obtains most
Ticket obtains final classification result in the case where reaching certain poll simultaneously；
Step S325, the successful sample that will classify are included into new training sample, recalculate in the new class of new training sample
Heart point.
As a further improvement of the present invention, in the step S321, the back of the hand different location is tapped, step is collected
The signal characteristic of rapid S31 is divided into n class as training sample, mark position and according to the difference of position, and every class percussion m times, wherein n
It is greater than or equal to 1 with m；
In the step S323, T attribute of random sampling, weight are distinguished into the class center generated each time in step S322
Multiple above operation Q times, obtains Q sub-spaces, each class center of contrast test sample and training sample one by one in subspace
The Euclidean distance of point finds nearest central point to get to the classification of Q sub-spaces as a result, wherein T, Q are greater than 1.
As a further improvement of the present invention, in the S2. extraction step, human body appointed part is the back of the hand；
Vibrating sensor is piezoelectric ceramic vibration sensor；
In the S1. receiving step, carrying out noise reduction process to vibration signal includes:
Step S11 filters DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Step S12 filters high-frequency noise using 800Hz low-pass filtering.
The present invention also provides a kind of intelligent input systems propagated based on osteoacusis vibration signal, comprising:
Receiving module, smart machine vibrating sensor receives vibration signal, and carries out noise reduction process to vibration signal；
Extraction module is detected using double threshold end-point detection method and extracts the vibration generated due to tapping human body appointed part
Signal segment；
Processing module extracts signal characteristic, based on RNM algorithm come category signal position.
As a further improvement of the present invention, include: in the extraction module
First extraction module, for high and low two thresholdings to be arranged to the signal of processing；
Second extraction module surmounts low threshold for the energy or zero-crossing rate when signal, primarily determines knocking starting point,
And when the energy of signal or zero-crossing rate breakthrough high threshold, just determine the real starting point of knocking；When signal energy and zero-crossing rate
When being lower than low threshold simultaneously, signaling destination point is determined；
Third extraction module obtains the vibration signal piece by tapping the back of the hand generation for retaining the data of starting point to the end
Section.
As a further improvement of the present invention, include: in the processing module
First processing module extracts Mel-frequency Cepstral Coefficients, obtains signal for dissection signal to be normalized
Feature；
Second processing module is calculated for using signal characteristic based on stochastic subspace and the RNM of nearest central point algorithm
Method is classified, and then judges beating position；
The Second processing module includes:
First processing units, for the difference according to beating position, the signal characteristic that acquisition step S31 is obtained makees training sample
This, and classify；
The second processing unit, for using nearest central point algorithm to calculate every a kind of central point of training sample；
Third processing unit, for being based on stochastic subspace, the class central point of test sample and training sample is repeatedly in son
Spatial contrast obtains multiple classification results；
For using simple majority Voting principle to classification results, and certain poll ratio is arranged in fourth processing unit,
It gains the majority while obtaining final classification result in the case where reaching certain poll；
5th processing unit, for that will classify, successful sample is included into new training sample, recalculates new training sample
New class central point.
As a further improvement of the present invention, in the first processing units, the back of the hand different location is tapped, is acquired
Signal characteristic to step S31 is divided into n class as training sample, mark position and according to the difference of position, and every class taps m times,
Wherein n and m is greater than or equal to 1；
In the third processing unit, random sampling T is distinguished at the class center generated each time in the second processing unit
A attribute, repeats above operation Q times, obtains Q sub-spaces, and contrast test sample and training sample one by one is each in subspace
The Euclidean distance of a class central point finds nearest central point to get to the classification of Q sub-spaces as a result, wherein T, Q are greater than 1.
As a further improvement of the present invention,
In the extraction module, human body appointed part is the back of the hand；
Vibrating sensor is piezoelectric ceramic vibration sensor；
In the receiving module, carrying out noise reduction process to vibration signal includes:
First receiving module, for filtering DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Second receiving module, for filtering high-frequency noise using 800Hz low-pass filtering.
The beneficial effects of the present invention are: the present invention not only solves the problems, such as smartwatch text input mode, moreover it is possible to reach
High discrimination, and can quickly be inputted, the cruise duration of smartwatch is not lost yet.
Detailed description of the invention
Fig. 1 is user in the virtual nine grids typewriting schematic diagram of the back of the hand percussion；
Fig. 2 is the schematic diagram of piezoelectric ceramic vibration sensor；
Fig. 3 is the structure chart of piezoelectric ceramic vibration sensor；
Fig. 4 is original signal waveform figure；
Fig. 5 is adaptive-filtering figure；
Fig. 6 is low-pass filtering figure；
Fig. 7 is RNM algorithm flow chart.
Specific embodiment
As shown in Figure 1, the invention discloses a kind of intelligent input methods propagated based on osteoacusis vibration signal, including such as
Lower step:
S1. receiving step, smart machine vibrating sensor receives vibration signal, and carries out at noise reduction to vibration signal
Reason；
S2. extraction step is detected using double threshold end-point detection method and is extracted due to tapping the generation of human body appointed part
Vibration signal segment；
S3. processing step extracts signal characteristic, based on RNM algorithm come category signal position.
In the S2. extraction step, human body appointed part is the back of the hand；
Vibrating sensor is piezoelectric ceramic vibration sensor, and smart machine includes smartwatch, piezoelectric ceramic vibration sensing
Device is built in smartwatch, Fig. 2,3 be piezoelectric ceramic vibration sensor schematic diagram and structure chart.Because piezoelectric effect makes inside
Polarity generates variation, externally shows the variation of voltage, and operator is allowed to tap the back of the hand, and acquisition taps generated vibration signal.
Fig. 4 is original signal waveform figure, it can be seen that original signal collected to extraneous strong antijamming capability, noise compared with
It is few.Fig. 5 uses the waveform diagram after low-pass filtering using adaptive-filtering and Fig. 6.It is more special that low-pass filtering remains signal
Sign, it is more preferable to the effect of the vibration signal for minute differences of classifying later.
In the S1. receiving step, carrying out noise reduction process to vibration signal includes:
Step S11 filters DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Step S12 filters high-frequency noise using 800Hz low-pass filtering.
Include: in the S2. extraction step
High and low two thresholdings are arranged to the signal of processing in step S21；
Step S22 primarily determines knocking starting point, and works as signal when the energy or zero-crossing rate of signal surmount low threshold
Energy or zero-crossing rate break through high threshold, just determination the real starting point of knocking；When signal energy and zero-crossing rate are lower than simultaneously
When low threshold, signaling destination point is determined；
Step S23 only retains the data of starting point to the end, obtains the vibration signal segment by tapping the back of the hand generation.
In S3. processing step, extracting signal characteristic includes: that starting point and terminal in initialization training sample is longest
The length unified as dissection signal is normalized signal consistent in length after dissection, uses formula are as follows:Its
Middle x is vibration signal, and n is the dimension of signal.Feature extraction is carried out to the signal after normalization, reduces calculation amount, and retain original
Signal most information, feature extraction is the Mel-frequency Cepstral Coefficients of signal, while remaining the spy of time domain and frequency domain
Sign.
Include: in the S3. processing step
Dissection signal is normalized in step S31, extracts Mel-frequency Cepstral Coefficients, obtains signal characteristic；
Step S32 divides signal characteristic using the RNM algorithm based on stochastic subspace and nearest central point algorithm
Class, and then judge beating position；
It come category signal position include: to include: based on RNM algorithm as shown in fig. 7, in the step S32
Step S321, according to the difference of beating position, the signal characteristic that acquisition step S31 is obtained makees training sample, goes forward side by side
Row classification (being divided into 9 classes according to nine grids position)；
Step S322 calculates every a kind of central point of training sample using nearest central point algorithm；
Step S323, based on stochastic subspace, the class central point of test sample and training sample is repeatedly compared in subspace,
Obtain multiple classification results；
Step S324 uses simple majority Voting principle to classification results, and certain poll ratio is arranged, and obtains most
Ticket obtains final classification result in the case where reaching certain poll simultaneously；
Step S325, the successful sample that will classify are included into new training sample, recalculate in the new class of new training sample
Heart point.
In the step S321, the back of the hand different location is tapped, collects the signal characteristic of step S31 as training
Sample, mark position are simultaneously divided into n class according to the difference of position, and every class taps m times, and wherein n and m is greater than or equal to 1；
In the step S323, T attribute of random sampling, weight are distinguished into the class center generated each time in step S322
Multiple above operation Q times, obtains Q sub-spaces, each class center of contrast test sample and training sample one by one in subspace
The Euclidean distance of point finds nearest central point to get to the classification of Q sub-spaces as a result, wherein T, Q are greater than 1.
The invention also discloses a kind of intelligent input systems propagated based on osteoacusis vibration signal, comprising:
Receiving module, smart machine vibrating sensor receives vibration signal, and carries out noise reduction process to vibration signal；
Extraction module is detected using double threshold end-point detection method and extracts the vibration generated due to tapping human body appointed part
Signal segment；
Processing module extracts signal characteristic, based on RNM algorithm come category signal position.
Include: in the extraction module
First extraction module, for high and low two thresholdings to be arranged to the signal of processing；
Second extraction module surmounts low threshold for the energy or zero-crossing rate when signal, primarily determines knocking starting point,
And when the energy of signal or zero-crossing rate breakthrough high threshold, just determine the real starting point of knocking；When signal energy and zero-crossing rate
When being lower than low threshold simultaneously, signaling destination point is determined；
Third extraction module, for only retaining the data of starting point to the end, remaining makees dissection processing, obtains by tapping the back of the hand
The vibration signal segment of generation.
Include: in the processing module
First processing module extracts Mel-frequency Cepstral Coefficients, obtains signal for dissection signal to be normalized
Feature；
Second processing module is calculated for using signal characteristic based on stochastic subspace and the RNM of nearest central point algorithm
Method is classified, and then judges beating position；
The Second processing module includes:
First processing units, for the difference according to beating position, the signal characteristic that acquisition step S31 is obtained makees training sample
This, and classify；
The second processing unit, for using nearest central point algorithm to calculate every a kind of central point of training sample；
Third processing unit, for being based on stochastic subspace, the class central point of test sample and training sample is repeatedly in son
Spatial contrast obtains multiple classification results；
For using simple majority Voting principle to classification results, and certain poll ratio is arranged in fourth processing unit,
It gains the majority while obtaining final classification result in the case where reaching certain poll；
5th processing unit, for that will classify, successful sample is included into new training sample, recalculates new training sample
New class central point.
In the first processing units, the back of the hand different location is tapped, the signal characteristic conduct of step S31 is collected
Training sample, mark position are simultaneously divided into n class according to the difference of position, and every class taps m times, and wherein n and m is greater than or equal to 1；
In the third processing unit, random sampling T is distinguished at the class center generated each time in the second processing unit
A attribute, repeats above operation Q times, obtains Q sub-spaces, and contrast test sample and training sample one by one is each in subspace
The Euclidean distance of a class central point finds nearest central point to get to the classification of Q sub-spaces as a result, wherein T, Q are greater than 1.
In the extraction module, human body appointed part is the back of the hand；Vibrating sensor is piezoelectric ceramic vibration sensor.
In the receiving module, carrying out noise reduction process to vibration signal includes:
First receiving module, for filtering DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Second receiving module, for filtering high-frequency noise using 800Hz low-pass filtering.
The present invention is embedded in small thin thin piezoelectric transducer on smartwatch, and machinery may be implemented in this piezoelectric transducer
The conversion of electric energy can be arrived.A virtual nine grids keyboard on the back of the hand, when the grid of finger tapping different location, mechanical wave meeting
It blazes abroad from all directions, encounters object back reflection and return.So the broadcast nature based on mechanical wave, piezoelectric transducer one
It is secondary to receive the mechanical wave signals with different multipath transmisstions.On the one hand this mechanical wave signals are disseminated to by voice signal
In air, on the other hand in the internal communication of hand and so-called osteoacusis.This some mechanical wave signal is not by the shadow of ambient noise
It rings, can preferably be received by piezoelectric transducer, be converted to electric signal and handled by the controller of smartwatch.Because different grid produce
Raw mechanical wave multipath effect is different, and the signal that smartwatch receives is with regard to different, using this species diversity, in conjunction with engineering
The sorting algorithm of habit can sort out each key of nine grids.Thus, it is possible to realize the intelligence based on the back of the hand bone conduction technology
It can wrist-watch text entry method and system.
The present invention for the first time makees the collected vibration for tapping the back of the hand in piezoelectric ceramic vibration sensor built in smartwatch
It is defeated to be easy to implement text using the back of the hand as the virtual big screen of smartwatch the small screen for the text input mode of smartwatch
Enter；Collected is to tap vibration signal on manpower after multipath transmisstion after the back of the hand, strong interference immunity, and is carried out to signal
Noise reduction, dissection, normalization, after extracting the processing such as Mel-frequency Cepstral Coefficients, the RNM algorithm for reusing invention is classified,
Its discrimination reaches 92%.Algorithm complexity as used herein is also linear rank, it is possible to realize the quick of text
Input.In addition, piezoelectric ceramic vibration sensor power consumption pole bottom, will not be greatly decreased the cruise duration of smartwatch.
The present invention not only solves the problems, such as smartwatch text input mode, moreover it is possible to reach high discrimination, and can be fast
Speed is inputted, and the cruise duration of smartwatch is not lost yet.
For hardware of the invention at low, system is simple, easy to use, osteoacusis of the realization based on the back of the hand that can be simple and quick
The input for the smartwatch that vibration signal is propagated.
The above content is a further detailed description of the present invention in conjunction with specific preferred embodiments, and it cannot be said that
Specific implementation of the invention is only limited to these instructions.For those of ordinary skill in the art to which the present invention belongs, exist
Under the premise of not departing from present inventive concept, a number of simple deductions or replacements can also be made, all shall be regarded as belonging to of the invention
Protection scope.
Claims (10)
1. a kind of intelligent input method propagated based on osteoacusis vibration signal, which comprises the steps of:
S1. receiving step, smart machine vibrating sensor receives vibration signal, and carries out noise reduction process to vibration signal；
S2. extraction step is detected using double threshold end-point detection method and extracts the vibration generated due to tapping human body appointed part
Signal segment；
S3. processing step extracts signal characteristic, based on RNM algorithm come category signal position.
2. intelligent input method according to claim 1, which is characterized in that include: in the S2. extraction step
High and low two thresholdings are arranged to the signal of processing in step S21；
Step S22 primarily determines knocking starting point when the energy or zero-crossing rate of signal surmount low threshold, and works as the energy of signal
Amount or zero-crossing rate break through high threshold, just determine the real starting point of knocking；When signal energy and zero-crossing rate are lower than low door simultaneously
In limited time, signaling destination point is determined；
Step S23 retains the data of starting point to the end, obtains the vibration signal segment by tapping the back of the hand generation.
3. intelligent input method according to claim 1, which is characterized in that include: in the S3. processing step
The dissection signal in vibration signal segment is normalized in step S31, extracts Mel-frequency Cepstral Coefficients, obtains
Signal characteristic；
Step S32 classifies to signal characteristic using the RNM algorithm based on stochastic subspace and nearest central point algorithm, into
And judge beating position；
The step S32 includes:
Step S321, according to the difference of beating position, the signal characteristic that acquisition step S31 is obtained makees training sample, and is divided
Class；
Step S322 calculates every a kind of central point of training sample using nearest central point algorithm；
Step S323, based on stochastic subspace, the class central point of test sample and training sample is repeatedly compared in subspace, is obtained
Multiple classification results；
Step S324 uses simple majority Voting principle to classification results, and certain poll ratio is arranged, and gains the majority same
When reach certain poll in the case where obtain final classification result；
Step S325, the successful sample that will classify are included into new training sample, recalculate the new class central point of new training sample.
4. intelligent input method according to claim 3, it is characterised in that:
In the step S321, the back of the hand different location is tapped, collects the signal characteristic of step S31 as training sample
This, mark position is simultaneously divided into n class according to the difference of position, and every class taps m times, and wherein n and m is greater than or equal to 1；
In the step S323, T attribute of random sampling is distinguished at the class center generated each time in step S322, is repeated
S322, S323 step operation Q times obtains Q sub-spaces, and contrast test sample and training sample one by one is each in subspace
The Euclidean distance of class central point finds nearest central point to get to the classification of Q sub-spaces as a result, wherein T, Q are greater than 1.
5. intelligent input method according to claim 1, it is characterised in that:
In the S2. extraction step, human body appointed part is the back of the hand；
Vibrating sensor is piezoelectric ceramic vibration sensor；
In the S1. receiving step, carrying out noise reduction process to vibration signal includes:
Step S11 filters DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Step S12 filters high-frequency noise using 800Hz low-pass filtering.
6. a kind of intelligent input system propagated based on osteoacusis vibration signal characterized by comprising
Receiving module, smart machine vibrating sensor receives vibration signal, and carries out noise reduction process to vibration signal；
Extraction module is detected using double threshold end-point detection method and extracts the vibration signal generated due to tapping human body appointed part
Segment；
Processing module extracts signal characteristic, based on RNM algorithm come category signal position.
7. intelligent input system according to claim 6, which is characterized in that include: in the extraction module
First extraction module, for high and low two thresholdings to be arranged to the signal of processing；
Second extraction module surmounts low threshold for the energy or zero-crossing rate when signal, primarily determines knocking starting point, and works as
The energy or zero-crossing rate of signal break through high threshold, just determine the real starting point of knocking；When signal energy and zero-crossing rate simultaneously
When lower than low threshold, signaling destination point is determined；
Third extraction module obtains the vibration signal segment by tapping the back of the hand generation for retaining the data of starting point to the end.
8. intelligent input system according to claim 6, which is characterized in that include: in the processing module
First processing module extracts mel-frequency cepstral for the dissection signal in vibration signal segment to be normalized
Coefficient obtains signal characteristic；
Second processing module, for signal characteristic using the RNM algorithm based on stochastic subspace and nearest central point algorithm into
Row classification, and then judge beating position；
The Second processing module includes:
First processing units acquire the signal characteristic that first processing module obtains and train for the difference according to beating position
Sample, and classify；
The second processing unit, for using nearest central point algorithm to calculate every a kind of central point of training sample；
Third processing unit, for being based on stochastic subspace, the class central point of test sample and training sample is repeatedly in subspace
Comparison, obtains multiple classification results；
Fourth processing unit for using simple majority Voting principle to classification results, and is arranged certain poll ratio, obtains
Majority vote obtains final classification result in the case where reaching certain poll simultaneously；
5th processing unit, for that will classify, successful sample is included into new training sample, recalculates the new of new training sample
Class central point.
9. intelligent input system according to claim 8, it is characterised in that:
In the first processing units, the back of the hand different location is tapped, the signal characteristic for collecting first processing module is made
It is divided into n class for training sample, mark position and according to the difference of position, every class taps m times, and wherein n and m is greater than or equal to 1；
In the third processing unit, random sampling T category is distinguished at the class center generated each time in the second processing unit
Property, the second processing unit, third processing unit operation Q times are repeated, Q sub-spaces are obtained, the contrast test one by one in subspace
The Euclidean distance of each class central point of sample and training sample finds nearest central point to get the knot classified to Q sub-spaces
Fruit, wherein T, Q are greater than 1.
10. intelligent input system according to claim 6, it is characterised in that:
In the extraction module, human body appointed part is the back of the hand；
Vibrating sensor is piezoelectric ceramic vibration sensor；
In the receiving module, carrying out noise reduction process to vibration signal includes:
First receiving module, for filtering DC component and low-frequency noise using the butterworth high pass filter of 20Hz；
Second receiving module, for filtering high-frequency noise using 800Hz low-pass filtering.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710433231.5A CN107300971B (en) | 2017-06-09 | 2017-06-09 | The intelligent input method and system propagated based on osteoacusis vibration signal |
| PCT/CN2017/092769 WO2018223489A1 (en) | 2017-06-09 | 2017-07-13 | Intelligent input method and system based on bone conduction vibration signal propagation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710433231.5A CN107300971B (en) | 2017-06-09 | 2017-06-09 | The intelligent input method and system propagated based on osteoacusis vibration signal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107300971A CN107300971A (en) | 2017-10-27 |
| CN107300971B true CN107300971B (en) | 2019-04-02 |
Family
ID=60134750
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710433231.5A Active CN107300971B (en) | 2017-06-09 | 2017-06-09 | The intelligent input method and system propagated based on osteoacusis vibration signal |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107300971B (en) |
| WO (1) | WO2018223489A1 (en) |
Families Citing this family (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108681709B (en) * | 2018-05-16 | 2020-01-17 | 深圳大学 | Intelligent input method and system based on bone conduction vibration and machine learning |
| US20210255147A1 (en) * | 2018-06-22 | 2021-08-19 | iNDTact GmbH | Sensor arrangement, use of the sensor arrangement and method for detecting structure-borne noise |
| CN109840480B (en) * | 2019-01-04 | 2021-08-13 | 深圳大学 | Interaction method and interaction system of smart watch |
| CN109933202B (en) * | 2019-03-20 | 2021-11-30 | 深圳大学 | Intelligent input method and system based on bone conduction |
| CN110058689A (en) * | 2019-04-08 | 2019-07-26 | 深圳大学 | A kind of smart machine input method based on face's vibration |
| CN110414196B (en) * | 2019-07-29 | 2021-09-17 | 深圳大学 | Smart watch identity verification method based on vibration signal |
| CN110931031A (en) * | 2019-10-09 | 2020-03-27 | 大象声科（深圳）科技有限公司 | Deep learning voice extraction and noise reduction method fusing bone vibration sensor and microphone signals |
| CN111752388A (en) * | 2020-06-19 | 2020-10-09 | 深圳振科智能科技有限公司 | Application control method, device, equipment and storage medium |
| CN111741419B (en) * | 2020-08-21 | 2020-12-04 | 瑶芯微电子科技(上海)有限公司 | Bone conduction sound processing system, bone conduction microphone and signal processing method thereof |
| CN112272019A (en) * | 2020-10-22 | 2021-01-26 | 广东美的制冷设备有限公司 | Control method and device for sound-control knocking switch, household appliance and storage medium |
| CN113342159A (en) * | 2021-05-07 | 2021-09-03 | 哈尔滨工业大学 | Wrist wearable system identified through wrist vibration |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104461004A (en) * | 2014-12-12 | 2015-03-25 | 北京奇虎科技有限公司 | Wearable intelligent equipment |
| CN106339104A (en) * | 2016-08-24 | 2017-01-18 | 广州市香港科大霍英东研究院 | Text input method and device for smart watch |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2011115035A1 (en) * | 2010-03-15 | 2011-09-22 | 日本電気株式会社 | Input device, input method and program |
| CN102436345A (en) * | 2011-10-31 | 2012-05-02 | 广州市动景计算机科技有限公司 | Method for interface operation and control of mobile device and implement device |
| US8798292B2 (en) * | 2012-06-06 | 2014-08-05 | Google Inc. | External vibration reduction in bone-conduction speaker |
| US9199098B2 (en) * | 2012-09-05 | 2015-12-01 | Olympus Corporation | Ultrasonic treatment device |
| CN106128452A (en) * | 2016-07-05 | 2016-11-16 | 深圳大学 | Acoustical signal detection keyboard is utilized to tap the system and method for content |
-
2017
- 2017-06-09 CN CN201710433231.5A patent/CN107300971B/en active Active
- 2017-07-13 WO PCT/CN2017/092769 patent/WO2018223489A1/en active Application Filing
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104461004A (en) * | 2014-12-12 | 2015-03-25 | 北京奇虎科技有限公司 | Wearable intelligent equipment |
| CN106339104A (en) * | 2016-08-24 | 2017-01-18 | 广州市香港科大霍英东研究院 | Text input method and device for smart watch |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2018223489A1 (en) | 2018-12-13 |
| CN107300971A (en) | 2017-10-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107300971B (en) | The intelligent input method and system propagated based on osteoacusis vibration signal | |
| Zhang et al. | Data augmentation and dense-LSTM for human activity recognition using WiFi signal | |
| US10061389B2 (en) | Gesture recognition system and gesture recognition method | |
| CN106658590B (en) | Design and implementation of multi-person indoor environment state monitoring system based on WiFi channel state information | |
| Qifan et al. | Dolphin: Ultrasonic-based gesture recognition on smartphone platform | |
| CN106128452A (en) | Acoustical signal detection keyboard is utilized to tap the system and method for content | |
| Du et al. | Wordrecorder: Accurate acoustic-based handwriting recognition using deep learning | |
| WO2019218725A1 (en) | Intelligent input method and system based on bone-conduction vibration and machine learning | |
| CN106919958B (en) | Human body finger action recognition method based on smart watch | |
| CN109508728B (en) | Novel identity authentication method for wearable equipment | |
| CN105938399B (en) | The text input recognition methods of smart machine based on acoustics | |
| CN110176226A (en) | A kind of speech recognition and speech recognition modeling training method and device | |
| CN106899968A (en) | A kind of active noncontact identity identifying method based on WiFi channel condition informations | |
| CN106237604A (en) | Wearable device and the method utilizing its monitoring kinestate | |
| CN105956558B (en) | One kind being based on 3-axis acceleration sensor human motion recognition method | |
| CN103948398B (en) | Be applicable to the heart sound location segmentation method of android system | |
| CN110069199A (en) | A kind of skin-type finger gesture recognition methods based on smartwatch | |
| CN109271901A (en) | A kind of sign Language Recognition Method based on Multi-source Information Fusion | |
| CN110059612B (en) | Position-independent gesture recognition method and system based on channel state information | |
| Xu et al. | Attention-based gait recognition and walking direction estimation in wi-fi networks | |
| CN108182418A (en) | A kind of thump recognition methods based on multidimensional acoustic characteristic | |
| Zou et al. | AcouDigits: Enabling users to input digits in the air | |
| CN103070682A (en) | Extraction method and extraction device for direction of eye movement based on electro-oculogram signals | |
| CN107491254A (en) | A kind of gesture operation method, device and mobile terminal | |
| CN108108015A (en) | A kind of action gesture recognition methods based on mobile phone gyroscope and dynamic time warping |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |