Comprehensive_Slides_Ashikur_4_13

Signal Classification and Noise Cancellation in Doppler Radar Applications

Ashikur RahmanFirst Author Publication

Journal

[1] A. Rahman, E. Yavari, A. Singh, O. Boric-Lubecke and V. Lubecke, "A Low-IF Tag-Based Motion Compensation Technique for Mobile Doppler Radar Life Signs Monitoring," in IEEE Transactions on Microwave Theory and Techniques, Oct. 2015.[2] A. Rahman, E. Yavari, O. Boric-Lubecke and V. Lubecke, Noncontact Unique Identification Based on Cardiopulmonary Signatures," in preparation for IEEE Transactions on Microwave Theory and Techniques (2016) [3] A. Rahman, E. Yavari, O. Boric-Lubecke and V. Lubecke, See-Through-Wall Vital Sign Detection Using Mobile Radar for Unique Identification," in preparation for IEEE Transactions on Microwave Theory and Techniques (2016)

Conference Proceedings

[4] A. Rahman, E. Yavari, X. Gao and V. Lubecke, "Signal Processing Techniques for Vital Sign Monitoring Using Mobile Short Range Doppler Radar," in IEEE Radio Wireless Week, San Diego, 2015.

[5] A. Rahman, and A. Kipple, A Neural Network based Software Engine for Adaptive Power System Stability in IEEE PES, Orlando, Florida, US May , 2012.

[6] A. Rahman, E. Yavari, V. Lubecke and O. Boric-Lubecke, "Noncontact Doppler Radar Unique Identification System Using Neural Network Classifier on Life Signs," in IEEE Radio Wireless Week, Austin, TX 2016.

1Start: Fall - 2013

Conference Proceedings

[7] A. Rahman, E. Yavari, X. Gao and V. Lubecke, " AC/DC Coupling Effects on Low-IF Tag Assisted Mobile Doppler Radar Life Sign Detection," in IEEE International Microwave and RF Conference, Hyderabad, India 2015.

[8] A. Rahman, E. Yavari, V. Lubecke and O.-B. Lubecke, Single-Channel Radar Fusion For Quadrature Life-Sign Doppler Radar, in IEEE ACES, Honolulu, HI, 2016

[9] A. Rahman, E. Yavari, X. Gao, J. Xu, V. Lubecke and O.-B. Lubecke, See-Through-Wall Life Sensing Using Mobile Doppler Radar, in IEEE ARFTG Conference, San Diego, CA, 2016

[10] A. Rahman, E. Yavari, V. Lubecke and O.-B. Lubecke, Signal Conditioning for UAV-Radar in Vital Sign Monitoring, in IEEE ACES, Honolulu, HI, 2016

[11] X. Gao, J. Xu, E. Yavari, A. Rahman, O.-B Lubecke Barcode Based Hand Gesture Classification using AC Coupled Quadrature Doppler Radar in IEEE IMS , San Francisco, CA, 2016

[12] E. Yavari, A. Rahman, D. Mandic, O.-B. Lubecke, Synchrosqueezing an Effective Method for Analyzing Doppler Radar Physiological Signals in IEEE EMBC, Orlando, FL, 2016Ashikur RahmanStart: Fall - 20132

MOTION ARTIFACT CANCELLATION AND UNIQUE IDENTIFICATION BASED ON RESPIRATORY SIGNATURES USING DOPPLER RADAR SYSTEMS

Committee Members Dr. Olga Boric-LubeckeDr. David GarmireDr. Aaron OhtaDr. Jan Prins-Dr. Victor Lubecke (Chair)

3Comprehensive Examination of Ashikur Rahman

Good afternoon all, thanks for coming in supporting me.I would really like to thank my committee for volunteering specially Dr. Jan prins, Dr. David and Ohta. Also I am very grateful to Dr. Victor for being a great adviser, also support from Dr. Olga is always unprecedented not just in academic matters but other parentally supports. 3

OUTLINE4123Vital-sign-radar background (short distance radars)Artifacts in motion sensing (remove undesired sig. )Bio-signal recognitionSuppression of extraneous motion -Theory, experiments and resultsPattern recognition - Theory, experiments and resultsSummaryDissertation proposalConclusion

To accurately classify signals I needed to determine salient information, information that matters, but that information needs to standout from the other. That is the signal to noise ratio challenge. So the first part is determining what signal is of value and the second part is classifying what is noise free or important. 4

RADAR (RADIO DETECTION AND RANGING) TECHNOLOGY

5

How it worksReflected by a target , travel time of received signal gives some information (distance, shape, altitude)Doppler RadarFrequency shifts because of moving target

Radar principleRadar principle

Bounce and time of travel

The radar signals that are reflected back towards the transmitter are the desirable ones that make radar work. If the object is moving either toward or away from the transmitter, there is a slight equivalent change in the frequency of the radio waves, caused by the Doppler effect.

Reading list History of radar, math equation of radar, how frequency shifts , correlation equations, Doppler radar equations5

SHORT DISTANCE VITAL-SIGN RADARShort distance radar (Challenges)Special and small motion can be detected from phase variationReal-time application, time and space sensitiveApplication Non-invasive monitoring, Cardiac output , SVHeart rate , respiration rate, Occupancy sensing, Fall protection, SIDs precaution monitoring Must be robust to real world applicationImportance (Sleep studies)40M Americans suffer from sleep disorders12M have obstructive sleep apnea (OSA)Annual OSA cost $16BSudden Infant Death Syndrome (SIDS) is one form of apnea Up to 40% of returning soldiers suffer from Posttraumatic Stress Disorder (PTSD) Most PTSD cases have persistent sleep disturbance

6Real time processingTravel time - very smallSensitive - mm range

Short range radar definition promising in measuring cardio pulmonary motion, specialty of radarSAR take data for years and signal process later

Real time signal processingTime difference is smallProcess more or less real-time

Resolving small displacement, time pulsing cannot be used, attracted to do continuous wave with phase comparison

Application breathing on somebody more or less who is not movingThere are situation for example fall detection, occupancy sensing we cannot control the situations very well, figure out how to deal with extraneous noise

ImportanceLots of illness is related to vital signs like breathing, and heart ratesApplications may involve sleep studies, sleep apnea is a major problem , millions suffer from it, cost of treating 16BIn health care alone big businessLots of dollar and people involved --------------------------------------------------------------------------------------------------------Reading list

Examples of non invasive monitoring what people have done so farCardiac output systemWhat is the heart rate, blood pressure, what is respiration rate, what is occupancy sensing, draw a fall protection system

-----------------------------------------------------------------------------------------------------

6

DOPPLER PHYSIOLOGICAL RADARCardio-respiratory motion and rates can be extracted from phase variationQuadrature radar (stereo vision)

7

DAQ and Signal Processing

Radar TransceiverSubjectReflected waveTransmitted wavePhase comparisonIQ(t)x(t)

SedentarySedentaryB.K Park, et al. (2007) Arctangent Demodulation With DC Offset Compensation in Quadrature Doppler Radar Receiver Systems

Here is a short distance radar circuit, it could be more simpler than this, with just one power divider, one mixerSingle channel radars have some limitation, there are certain points where the radar output does not give any useful information about the targets motion.Hence a quadrature radar is used, What quad radar is two channel, more information, stereo vision mono vision

While some processing can be done in the front end to suppress noise, most of the processing has to be done after data acquisition and digitization and use signal processing techniques.

Displacement, x(t), determined by demodulating phase.Moving targets vary the phase over time (ac) while stationary targets do not (dc).Cardio-respiratory motion and rates can be extracted from phase variationDifficult to separate desired motion signal from noise (extraneous motion)Activity of subject will affect measured cardio-respiratory rates.

7

8

SubjectReflected waveTransmitted wave(t)x(t)RadarTXRXDoppler phase shift Short distance

Frequency (GHz)Wavelength (mm)Phase for (2mm) in degree2.41255.71030242412.557.6

SHORT DISTANCE RADAR VITAL SIGN DETECTION I-ChannelQ-Channel

I usually show this slide for general audience for them to have an idea of how Doppler phase shift helps determining vital sign.Depending on frequency the phase modulation will vary.For example a 2mm displacement would cause 5.7 degree for a 2.4 GHz radar. I point to one important thing here. I told about a quadrature radar in previous slide. Now if you make a plot one channel vs. the other, for a periodic signal you see an arc.

With proper demodulation the phase will be 4pi(x)/lambda

IS IT IQ- OR ARC LENGTH8

9

RESEARCH CHALLENGE

I want the displacement due to breathing or cardiopulmonary activity as shown around the heart sign for persons.If I measure that , say I get plots like this , this and this. For all of those we understand what the breathing rate is, it is right there.It gets little trickier, what about the depth, can we tell how deeply someone is breathing? Yes, you have a good signal there, and you have a model where displacement is proportional to radar output, so you can do that.You can do that for each personNow what if I wanted to tell the difference between this one , this one and this one then it is a matter of how good are these signals are,

One of these are like little bended, some has variation in up and down slope, they are all giving me rate and depth, but there are other differences which can be more clear depending on the quality of the signal.

Now if I only focus on the differences and measure the chest displacement I do not want to see and fidgeting, hand movements, or the radar shake due to hand movement.

So again the key here if I we want to tell the difference between these patterns , I have start with a system that gives a true and quality representation of distance, rate, slope, depth all of these has to be accurate.

So my talk will first look cleaning up those factors , so we have good signal, a low distortion system in other words.Then what's left can we do pattern recognition?

Fidgeting motion , there is actually a lot on that. There are techniques that you can do to reduce that, I am not going to go too much details into that.

But what is the remedy of handshake? You can use a tripod, but there are cases where we cannot use a tripod.

People do fidget, but in between fidgeting person stay still, so if you can recognize the difference between fidgeting and staying still you can find the vital signs.

We are going to assume that we are able to recognize the difference and able to record data when there is low fidgeting, but we cannot ignore the fact that radar it self may be in motion, shake, again since these short distance radars are very sensitive to any sort of displacement we will have to account for that seriously.

The platform might not be just handheld, it could be in a hospital situation, we use these radars in search and rescue operation, so they can be in any mobile platform.You can solve the problem by not shaking the hand, however there might be other situations.

9

DOPPLER PHYSIOLOGICAL RADAR

10

DAQ and Signal Processing

Radar TransceiverSubjectReflected waveTransmitted wavePhase comparisonIQ(t)x(t)

SedentarySedentary

What quad radar is two channel, more information, stereo vision mono vision

Displacement, x(t), determined by demodulating phase.Moving targets vary the phase over time (ac) while stationary targets do not (dc).Cardio-respiratory motion and rates can be extracted from phase variationDifficult to separate desired motion signal from noise (extraneous motion)Activity of subject will affect measured cardio-respiratory rates.

10

RadarNON-SEDENTARY CONDITIONPatient might be moving11

The platform might not be just handheld, it could be in a hospital situation, we use these radars in search and rescue operation, so they can be in any mobile platform.You can solve the problem by not shaking the hand, however there might be other situations.11

PLATFORM NON SEDENTARY CONDITION12

Surveillance , search and rescue operation12

13NOISE REDUCTION: ANALYZING THE I-Q SIGNALS

QIWhen motion is periodic about a fixed point, radius is maintained

Sedentary respiration can be inferred from a steady radius

Periods with fluctuating radius indicate extraneous activity

Extracting a rate during random activity is not useful

When motion is periodic about a fixed point, radius is maintainedSedentary respiration can be inferred from a steady radiusPeriods with fluctuating radius indicate extraneous activityExtracting a rate during random activity is not useful

13

MOTION ARTIFACT/NOISE14Motion artifact / Noise

Human subject fidgeting is not a problem / since human is not always moving. There has been some experiments and it is shown that those sweet spots are identifiable. So I will be mainly focusing on the platform motion.

I want to mention when I say target, chest motion, vital sign motion they apply all the samePlatform motion- means the measuring device which is the radar is mounted on something , a tripod stationary case, or a mobile platform.I have picked this special sub category for study.14

CLASSIFICATION OF ISOLATED BIO SIGNALS

Even there is some variation in the arc, distinguish between a normal one from a abnormal one Assumption that , noise has been suppressed what we could do the isolated bio-signals.15

RESEARCH OBJECTIVEI. Isolating bio motion from noise motion (assumption bio motion are similar to each other and different from noise) II. Classification of Isolated bio signals (Within the definition of bio-signal how they differ for illness or person to person) Emphasized focus 16

Determine how they same so that we can separate them noise, now once we have done that within the definition of bio signals how they are different from sick person to normal person or person to person.

The basic algorithm of the linear demodulation includes the followingsteps: 1) subtract the mean from each of the data dimension, 2) calculate the covariancematrix, 3) calculate the eigenvectors and eigenvalues of the covariance matrix, and 4)choose the components to form a feature vector. For the quadrature receiver, first, anydc offset is removed from the data, and the covariance matrix between the I and Qchannels is found. The I-Q arc is rotated such that it will be parallel to the Q-axis. Toachieve this, the received signal is multiplied by the transpose of the covariance matrixeseigenvector. After the rotation, the Q-component is always in an optimum point andthe I-component is always in a null point. Thus, this Q-component can be used as thedemodulation signal.

Another advantage of the linearmodulation is that detection of sudden changes in eigenvector and eigen value can beused to identify the motion artifacts,16

OUTLINE17123Vital Sign Radar Background (short distance radars)Artifacts in Motion Sensing (remove undesired sig. )Bio-Signal recognitionSuppression of extraneous motion -Experiments and resultsPattern recognition - Experiments and resultsSummaryDissertation ProposalConclusion

To accurately classify signals I needed to determine salient information, information that matters, but that information needs to standout from the other. That is the signal to noise ratio challenge. So the first part is determining what signal is of value and the second part is classifying what is noise free or important. 17

MOTION ARTIFACT CANCELLATION IN LITERATUREC. Gu et al. (2013) A Hybrid Radar-Camera Sensing System With Phase Compensation for Random Body Movement Cancellation in Doppler Vital Sign DetectionI. Mostafanezhad et al. (2007) Sensor Nodes for Doppler Radar Measurements of Life SignsC. Li et al. (2008) Random Body Movement Cancellation in Doppler Radar Vital Sign Detection 18

Body MovementRadar Platform ShakeSensor Node Antenna Shake CancelEmpirical Mode Decomposition

In literature there are some work reported for motion artifact compensation, both in category of body shake cancellation and platform shake cancellation. For instance, Hand shake cancellation for short duration of time, but not quite from a continuously moving mobile platformBi-static radars, separate receivers, they are not wired together, separating the transmitter from the receiver gives some advantage, it can ignore transmitter motion, some drawbacks, you do not have two channel high accuracy precision. Compromise. It helps but does not help on everything One approach is multiple radars from different angle to cancel fidgeting motion, however in this case the radars have to be sedentary which does not apply for mobile radar application.

- Sensor node has some disadvantages as well as empirical mode decomposition requires computational processingSome experiments only for sudden shake cancellation not from a mobile platform with larger amplitude of motionsTwo radars from two sides, only limited to some suitable scenario

Some works have been published addressing motion artifact due to hand shake for Doppler radar sensors. One way of tackling this problem is using a sensor node [3]. Empirical mode decomposition techniques were discussed for removing fidgeting interference in Doppler radar life signs monitoring devices [4]. Our work further analyzes the noise compensation not only for transmit antenna, but also the whole radar module for continuous motion of larger amplitude in order to use the system in vehicle mounted devices. Sensor node technique is not always feasible because it requires additional daq device nearby a subject. In EMD method intrinsic mode functions (IMFs) should be selected manually which is not always possible and it is more computationally complex18

AC/DC COUPLING IN CW DOPPLER RADARAC coupling - works as high pass filterLow frequency distortionHigher dynamic range before it reach saturation

DC-coupling-No nonlinear distortion @ low frequencyDynamic range is lesser than AC coupled systems

19

Okay so far I have talked about noise or artifacts introduced by motion for the target or the platform itself. Now there is another kind of distortion which is introduced in circuit level and it is AC coupling distortion. Since the demodulated RF is down converted to baseband and amplified for processing, the amps can be set in either DC coupling or AC coupling mode19

AC/DC COUPLING IN CW DOPPLER RADAR20

Displacement is proportional to the output, this experiment was done moving the platform moving forward, then rest and go backward. The same experiment was done for and dc coupling. Note the blue curve at resting try to go back to 0, hence you see a distortion. AC coupling shows distortion20

X1+X2

HOW DOES MOTION ARTIFACT MODULATE?

Displacement is proportional to voltage output (modulation) within limitX2X121

In simple cartoonish visualization-When there is only targets movement related to vital sign, since respiration movement is periodic , you see somewhat like x1, Now if the platform is moving in periodic movement the output signals are bounced of from stationary clutter and you see an output like this. Now combination looks like this.21

HYPOTHESISVital Sign (such as respiration, heart rate ) can be measured if successful noise cancellation/motion compensation is performed by acquiring enough information about the motionVital sign / Motion of interest = Received signal Radar platform motion22

Now I can state my hypothesis Proving concept , take a complex model state another hypothesis , extend it to 3 dimensional motion.22

PLATFORM MOTION OPTICAL TRACKING

Resolution of 50 micrometer

23

EXPERIMENT SETUP (CW RADAR)

CameraCameraRadarCamera tracks the motion

Output is proportional to displacement

24

Here is the experiment setup in cartoon. The camera marker is24

EXPERIMENT

Tracking CameraRadar TRXRespiration Chest BeltCamera Marker

25

2.4 GHz Radar CW , 12.5 cm wave length, 10dBm power from LO , SHOW the IR markers25

IIR FILTERS Analyze the radar motionDetermine the frequency components Apply filter

IIR filters Simple experiments (Radar motion components were 2Hz, 1.2 Hz)2nd/4th Order Butterworth filter was used26

However, vital sign detection from mobile radar system is challenging due to possible aliasing, phase distortion and occurrence of null point. These problems occur due to variable traveling distance from radar antennas and target. 26

PLATFORM 2 HZ SINE 8 mm

Platform 2 Hz Sine 8 mm

Proportional to Displacementx = k*v 27

The red curve is respiration belt reference the black is the filtered output which is in good agreement.27

Platform 1.2 Hz Sine 8 mm

PLATFORM 1.2 HZ SINE 8 MMProportional to Displacementx = k*v 28

ADAPTIVE NOISE CANCELLATION (0.2 Hz)

Radar output signalCorrelated signal / Radar MotionDesired signal (Respiration)

Strong aliasingIIR filters werent useful

29

Now considering a situation where there is a possibility of having components closer to respiration, there will be aliasing, also if the platform motion is changing which would be true for real case, we need some sort of adaptation. So instead of using fixed filters I used adaptive filters. Well in case of severe aliasing adaptive filters will suffer as well. But it is more practical in motion compensating problems

A least mean square adaptive filter was used for platform motion cancellation. Adaptive filter parameters , why do you need adaptive filter, why IIR does not work but adaptive filter works29

RESULTS ADAPTIVE NOISE CANCELLATION

Proportional to Displacementx = k*v Least Mean Squared Convergence30

Least mean square algorithm is employed for ANC due to its simplicity and real-time capability with 0.008 constant step size and an order of 8 30

MOBILE RADAR AS FIRST RESPONDER

DESIREDUNDESIRED31

However, vital sign detection from mobile radar system is challenging due to possible aliasing, phase distortion and occurrence of null point. These problems occur due to variable traveling distance from radar antennas and target. Null point distortionDemodulation techniques breaks downVariable distance from target to antennaAntenna focus abrupt

31

Flight Simulation32

SHORT DISTANCE RADAR MOUNTED ON UAVON GOING EXPERIMENTS - CHALLENGES

32

PLATFORM MOTION ASSESMENT

33

RF Tag

Target

Radar System on moving platformTag (passive)Piece of electronics that would alter the received frequency during reflection

StationaryTarget - Breathing

RF Tag

I will talk about the tag moreThe tag is stationary, it could be dropped on the ground, or the tag could be on the helmet or anywhere it doesn't measure the vital signStationary tag in the same field of view allows us to effectively measure the motion of platform.33

LOW IF TAG APPLICATION

34

One point to clear here. I was almost out in the field , flying quad copters over human target.I had to go back to lab again, designed some experiments and get back to lab34

TAG RELATED WORKLow IF-Tag35Harmonic Tag

A. Singh et al., Adaptive Noise Cancellation Using Two Frequency Radar Using Frequency Doubling Passive RF Tags

Harmonic tag vs low IF tag. A single broad band receiver can the IF signals to be received. Multiple IF offset will gives the option of many tag. Harmonic tags only can only go up to 2nd 3rd and again there has to be multiple receivers. Same broadband receiver , f1, f2 , f3 , f4 etc.35

MOTION ARTIFACT DETECTION USING TAG In all RF radar

36

Special technology required to take in field

Drift is problemNeeds direct processingNeeds to be translated to work with radar

RF tag

LOW-IF TAG

37

STATIONARY TAG & MOBILE PLATFORM

One dimensional simplified case

X1 Human chest motion

X2- Platform Motion

Down Conversion38

STATIONARY TAG & MOBILE PLATFORM39

LOW IF EXPLAINED MORE

40

AC/DC COUPLING (NO TARGET)

Base band

Low IFTag

41

AC/DC COUPLING (MECHANICAL TARGET)

ADAPTIVE FILTERS FOR SUBTRACTING THE MOTION ARTIFACT

Programmed Reference Motion42

HUMAN TESTING WITH LOW-IF TAG MOTION COMPENSATION

Slide # 17

BASEBAND = PLATFORM MOTION + BREATHINGIF = PLATFORM MOTION

Reference43

PLATFORM NON SEDENTARY CONDITIONYet to apply the noise cancel techniques- Data acquisition and processing and better radio for data record-10 GHz Tags needs to be fabricated (2.4 GHz were tested)44

Dramatic field condition like thisAssuming we have removed the platform noise issue, what can we do?44


CLASSIFICATION OF ISOLATED BIO SIGNALS 46

Even there is some variation in the arc, distinguish between a normal one from a abnormal one 46

UNIQUE IDENTIFICATION STATE OF THE ARTWill these method can give me health information?

Do you really want yourself to be exposed always?Do I always have to touch that machine? Is it safe?

47

In various ways you can identify a person, fingerprint , eyes and such. Sometimes you want to be unobtrusive, sometimes you want to do it through the wall. Is there a non contact through the wall unique identification.

Respiration signals look like enough so you can tell respiration rate, but they still look different and examining how accurately we can tell difference from one person to another.

May be can be used with other situation like cameras, depending on application. 47

MOTIVATION UNIQUE IDENTIFICATION

48

Fix radar photos48

?

MOTIVATION UNIQUE IDENTIFICATION (RADAR)Each individual appears to select one particular reproducible pattern among the innite number of possible combination of ventilatory variables and airow prole. Extraction of variable features can lead to unique identification.Noncontact Doppler Radar System

Heath diagnostic + Unique Identification 49

Also there is a medical value of seeing the change in that, how overtime persons vital sign changes. Is the condition getting worse , is it getting better? For ID also, who is who? 49

WHY IT COULD WORK? Breathing frequency Inspiratory and expiratory time Tidal volume Airflow profile

ExhaleInhaleRestRestEach individual appears to select one particular reproducible pattern among the innite number of possible combination of ventilatory variables and airow prole. Extraction of variable features can lead to unique identification.50

MEASURING PARADOXICAL BREATHINGNormal respiration is tracked with no phase variation between thorax and abdomen

51

Resp. Disp.Resp. Rate

Paradox Index

Normal Ventilation

So far I have been describing breathing as a back and forth piston motion of a chest, even with that simplification, different people moving their chest in different ways. One fact that there is various degree of chest and abdomen moving out of sync. For illness it could be more prominent. Another case to mention, when person goes through apnea cycle, just after the apnea there is notable difference between the movement of chest and abdomen. 51

52MEASURING PARADOXICAL BREATHINGObstruction causes thorax and abdomen to move out of sync.

Resp. Disp.Resp. Rate

Paradox Index

Paradoxical Respiration

Paradoxical Respiration begins

FRACTAL CLASSIFICATION SYSTEM OVERVIEWRespirationDoppler radar sensingSignal proc.Fractal AnalysisPrint Results

Fractal Analysis53

Fractal analysis why Health condition, variation little bit of variation It varies over short time or in long term, it recognize long time short time variation correlates. Some speculation has been there, more fractal is good. 53

HIGUCHI FRACTAL DIMENSION (HFD)54

Why higuchi fractal dimension

Box countingKatzLater work54

FRACTAL ANALYSIS HUMAN SIMULATION

55

1.61.4

NormalPartial ObstructionFull ObstructionDeep breathsNormal

Tell the experiment 55

FRACTAL ANALYSIS- BREATHING PATTERN SUB # 1

56

While distinguishing sick person from normal person I thought normal people might show some pattern, so why not perform some experiment. At this point it is really hard to tell, but it seems there might be some unique visible patterns from person to person. But we will need to lot more data and a way to find some mathematical algorithm 56

FRACTAL ANALYSIS- BREATHING PATTERN SUB # 2

57

NEURAL NET - SYSTEM OVERVIEWRespirationDoppler radar sensingSignal proc.Feature extractionNeural network training

RespirationDoppler radar sensingNN Implementation

Training Implementation 58

LEARNING PROCESS

ShapeSizeColorSmellAnd More Memory 59

45 Second

PeaksFEATURE EXTRACTION AND TRAINING60Long Time Radar Data

FEATURE EXTRACTION AND TRAINING61

TRAINING ALGORITHM AND PERFORMANCE

LevenbergMarquardt Algorithm

Input layer 3Output layer 1Hidden layer 2

62

RESULTS63Long Time Radar Data


65

SUMMARY

Real world scenario can be even more complicated than that, I would like to examine what it will take to do this too. More complexity , not only I have a shaking thing but also I am seeing myself when it bounce back from the wall, possibility of tagging myself and cancel that out. Which I consider a part of my goal. 65

THROUGH WALL SENSING66

Put the bar highPossibility of more experiments 66

Unique Identification Larger Dataset Heart Rate Variability (Addition to respiratory)Heart Rate Pattern Correlation factors of cardio pulmonary variables (Respiratory-Heart Rate relation)Motion Artifact CancellationComplex scenario of motion artifact (Horizontal, vertical movements)Signal processing, adaptive algorithms, real-time cancellationsUAV Radar testing with Low-IF motion compensation (10 GHz tags, miniature 2.4 GHz Radar)

67DISSERTATION PROPOSAL

67

68Qualification examComprehensive examDefenseFractal analysis for radar signal classification, sleep studiesMotion artifact compensation RF tags unique identification smaller data set10 PublicationsUnique identification on more challenging cases with larger dataset (6-10)

GRADUATION PATH

Thank You

EXTRA SLIDESMedical conditions, heart rate, respiration rateIQ Radars, signal distortion, demodulation techniquesNoise figureAntenna used, mixer usedTag circuitQuadcopter progressPower level (13 dBm)Harmonic tagFree space lossRadar cross sectionRadar typesFractal dimensionNeural networkCW radar (Sensitive to motion, insensitive to stationary object, low complexity)

70

TAG CIRCUIT

TAG RF THEORY

TAG RF THEORY

COMPONENTS -PD

COMPONENTS -MIXER

COMPONENTS -ANTENNA

RADAR TYPES80

Documents

Comprehensive_Slides_Ashikur_4_13