ECE 494 Capstone Design Final Design Presentation Smartphone Based Human Behavioral Analysis

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ECE 494 Capstone Design Final Design Presentation Smartphone Based Human Behavioral Analysis. Andrew Jackson Michael Armstrong - PowerPoint PPT Presentation

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Slide 1ECE 494 Capstone DesignFinal Design PresentationSmartphone Based Human Behavioral Analysis Andrew Jackson Michael Armstrong Robbie Rosati Andy McWilliams Aaron StewartApril 18, 2014 Advisor: Dr. Fei HuOutline4/18/2014Smartphone Based Human Behavioral Analysis2Project Recap & GoalSystem DiagramSubsystem Breakdown & Team RolesSensor Data Extraction & AnalysisApp Development & Activity RecognitionDTW AlgorithmEnvironment SensorsNN AlgorithmRaw Memory Extraction & Analysis HMM AlgorithmAdministrationProject Recap & Goal4/18/2014Smartphone Based Human Behavioral Analysis3Create a system for tracking and detecting a users behavioral patterns though the phones sensors and internal memory logs.Can be used in multiple areas:HealthcareActivity MonitoringHomeland SecuritySystem Diagram4/18/20144Smartphone Based Human Behavioral AnalysisSubsystem Breakdown & Team Roles 4/18/20145Smartphone Based Human Behavioral Analysis5Sensor Data Extraction & AnalysisAaron StewartAndroid, iOS, or Windows PhoneWe chose to go with Android.No development license feesMajority of marketMany sensors availableOpen file systemPrevious Android programming experienceCheaper phone prices4/18/20147Smartphone Based Human Behavioral AnalysisSamsung Galaxy S4 Mini4/18/2014Smartphone Based Human Behavioral Analysis8New phone with many sensorsFairly inexpensive for an unlocked phoneSmaller size great for testing in pockets4/18/2014Smartphone Based Human Behavioral Analysis9SensorsSensor Types 4/18/2014Smartphone Based Human Behavioral Analysis10Sensor Types 4/18/2014Smartphone Based Human Behavioral Analysis11 Behavior Analysis App Version 1.04/18/2014Smartphone Based Human Behavioral Analysis12Shows values from each sensor in real-time as the sensor updates Can save the data to a file on phone Move file to computer to analyze with MATLABGraph the dataValidate that it makes senseTesting with App3/10/2014Smartphone Based Human Behavioral Analysis13Most sensor data comes in a set of 3 points Few have single values Tests related to typical smartphone user behaviorsUsed MATLAB for data graphing and filteringLight Sensor Test (Different Exposure) 4/18/2014Smartphone Based Human Behavioral Analysis14Accelerometer Test (Answer Call)4/18/2014Smartphone Based Human Behavioral Analysis15XYZApp Development and Activity RecognitionAndrew Jackson4/18/2014Smartphone Based Human Behavioral Analysis17 Behavior Analysis App Version 2.04/18/2014Smartphone Based Human Behavioral Analysis18Integrates DTW machine learning algorithm Allows training of the algorithm from the phone no computer requiredOnce trained, will recognize where the phone is with almost 100% accuracyVisual UpgradesAll features from previous app rolled overFurther App Improvements4/18/2014Smartphone Based Human Behavioral Analysis19Increased accuracy of activity recognition by Increasing sensor sensitivityPolling each sensor more times a secondTweaking DTW algorithm codeUsing multiple sensors at the same time Continued adding new activitiesAdded speech recognitionBy demo day:Add in GPS coordinatesFurther explore light, sound, and proximity sensor possibilitiesAccelerometer, Light, and Proximity4/18/2014Smartphone Based Human Behavioral Analysis20Chose to work with accelerometer first because it can give some of the most useful data about the phoneApplying DTW on accelerometer dataNext, applied light and proximity sensors to get better resultsRecognized ActivitiesLast PresentationNow4/18/2014Smartphone Based Human Behavioral Analysis21Walking (with phone in hand)Talking on phoneSitting on tableHolding in handWalking (with phone in pocket)RunningStairs SittingDriving4/18/2014Smartphone Based Human Behavioral Analysis22App DemonstrationDynamic Time WarpingAndrew JacksonDynamic Time WarpingMeasures similarity between two sequences which may vary in time or speedCalculates an optimal match between the two given sequences or time seriesA distance-like quantity is measured between the two seriesSmartphone Based Human Behavioral Analysis4/18/201424DTW Time Series4/18/2014Smartphone Based Human Behavioral Analysis25Time series can be accelerated/decelerated as much as necessary to give an optimal match.Cost: 3.3084e+05Cost: 2.7239e+06Applying DTW to the Project4/18/2014Smartphone Based Human Behavioral Analysis26Allows training for different activities with disregard to time Supports three-dimensional dataProvides extremely accurate resultsRuns in O(n2) time FastDTW4/18/2014Smartphone Based Human Behavioral Analysis27Open-source algorithm based on original DTWRuns in O(n) timeSlightly less accurateEasy to port to AndroidEnvironmental SensorsRobbie RosatiProximity Sensor4/18/2014Smartphone Based Human Behavioral Analysis29Gives proximity in cmMost phones only return binary values near and farAbility to check whether phone is pressed against ear, in pocket, etc.neural networksLight Sensor4/18/2014Smartphone Based Human Behavioral Analysis30Detects ambient luminosity in luxUseful for indoor/outdoor detectionCould be included in gestures, used with DTWGPS4/18/2014Smartphone Based Human Behavioral Analysis31Can get users latitude and longitudeCould improve detection for if user is drivingWorst sensor with battery lifeNeed to only use it occasionallyMicrophone4/18/2014Smartphone Based Human Behavioral Analysis32Speaker recognition via NN algorithmPassive or active detectionCould use to detect loudness of roomsAlso see things in sound waveform like snoringNeural NetworksRobbie RosatiTransition from Support Vector Machine to Neural Networks4/18/2014Smartphone Based Human Behavioral Analysis34Implemented SVM into our Behavior Analysis appRan too inefficiently for phone hardwareDifficult to train with the sound sensorTherefore, decided to use an alternative algorithm that would fit our needs.Decided on Neural Networks, a popular machine learning algorithm for speech recognitionNeural Networks4/18/2014Smartphone Based Human Behavioral Analysis35Algorithm used for machine learning and pattern recognitionInspired by the way the brain recognizes objects and soundPresented as systems of interconnected neurons that can compute valuesDifficult to train in a short time so used an API from Google to offload processing from the phone Integrates speech recognition into the appGoogle Speech API4/18/2014Smartphone Based Human Behavioral Analysis36Open source API for speech recognition Could be coded into our Behavior Analysis appUses NN to interpret speech Can be presented in text with further codingHowever, the app requires an internet connection for this function since it streams audio to remote servers4/18/2014Smartphone Based Human Behavioral Analysis37App DemonstrationRaw Memory Extraction & AnalysisMichael Armstrong4/18/2014Smartphone Based Human Behavioral Analysis39Overview4/18/2014Smartphone Based Human Behavioral Analysis40Retrieving the physical image of a device is our goal.Immense variety of phones with an array of OS and applications.Current solutions are time consuming and/or very expensive.Access to deleted dataA logical image is easier to obtain, but it omits deleted data, and logical extraction interfaces usually enforce access rules and may modify data upon access.Samsung Galaxy S3 Mini4/18/2014Smartphone Based Human Behavioral Analysis41Relatively inexpensiveCompatible with teams SIM cardsSD card slotHas some sensors in case we need it as back up for sensor testingTesting Options4/18/2014Smartphone Based Human Behavioral Analysis42Flashing is interpreted as a dump of the phones memory into a format that is either hexadecimal or binary.Backup SoftwareFlashing boxLinux Forensics Softwaredc3dd dc3dd 4/18/2014Smartphone Based Human Behavioral Analysis43Terminal based utility for Linux Parses a partition bit-by-bit and creates binary imageAdvantages: Exactly what we need, easy to useDisadvantage: Parses through empty space and fills it with zeroes, creating a very large image file full of nothing however, there is a workaround for this issueSteps for Extraction4/18/2014Smartphone Based Human Behavioral Analysis44Gain root access to Android using simple utilityExtract a copy of the database fileShrink the partition of a USB flash drive to the smallest possible size and copy the database file to the partitionUse dc3dd to parse the partition and create the binary image for the database fileViewing the Image File4/18/2014Smartphone Based Human Behavioral Analysis45Any hex viewer application should able to view the data inside the imageChose GHex because it is easy to obtain with Ubuntu and has search functions for finding the data we are looking for Finding Desired Data4/18/2014Smartphone Based Human Behavioral Analysis46Messages and numbers are encoded as ASCIILocate corresponding hex data using the search function in GhexVerify by comparing the located hex data to ASCII valuesProcess should work for any type of file because the ASCII data will there regardless of file typeExample: Text Message4/18/2014Smartphone Based Human Behavioral Analysis47Strings and Grep Commands4/18/2014Smartphone Based Human Behavioral Analysis48Strings - Captures groups of letters separated by spaces in a fileGrep - Searches for an expression matching the users input expressionHidden Markov ModelAndy McWilliamsHidden Markov Model Smartphone Based Human Behavioral Analysis4/18/201450Way of predicting the next possible output in a sequence of events Equivalent to recognizing or interpreting that particular sequenceSlightly complicated because there can be multiple ways to produce the same observed outputHidden Markov Model4/18/2014Smartphone Based Human Behavioral Analysis51Accomplished through MATLABThere will be different matrices such as the transition matrix and emission matrixTransition matrix probability of transitioning from one state to the nextEmission matrix probability that one state emits the next stateHidden Markov Model4/18/2014Smartphone Based Human Behavioral Analysis52Model Evaluation Problem What is the probability of the observation? Given an observed sequence and an HMM, how probable is that sequence?Forward algorithmPath Decoding ProblemWhat is the best hidden state sequence for the observation?Given an observed sequence and an HMM, what is the most likely state sequence that generated it?Viterbi algorithmModel Training ProblemHow to estimate the model parameters? Given an observation, can we learn an HMM for it?Example with Binary4/18/2014Smartphone Based Human Behavioral Analysis53HMM MATLAB4/18/2014Smartphone Based Human Behavioral Analysis54Answers received contain new transition and emission matrices with probabilitiesThere are three files in which we work with to execute the codeMultiple Assumptions must be made in order to create the three files Three Files of MATLAB4/18/2014Smartphone Based Human Behavioral Analysis551. Markovsource is a data file that contains multiple fields of phone numbers, dates, etc.2. The transfer file contains matlab code to convert the data into the necessary values 3. The markovTEmatrix file contains the transition and emission matrices These three files are used to receive the state probabilities for each matrixAssumptions4/18/2014Smartphone Based Human Behavioral Analysis56There are three kinds of fields within the data file such as phone numbers with 10 digits, dates, and other non-numerical alphabetic fieldsWithin the matrices, the first ten rows represent the date fields probabilitiesThe next ten rows represent the phone number fieldsThere is an extra 1 or 2 rows that are used for the non-numerical alphabetic fieldsTransition and Emission Matrices4/18/2014Smartphone Based Human Behavioral Analysis57Multiple MATLAB Functions4/18/2014Smartphone Based Human Behavioral Analysis58Hmmdecode - used to calculate the state probabilities of a sequence of emissionsHmmviterbi used to calculate the most probable state path for a hidden Markov modelHmmtrain used to calculate likelihood estimates for the transition and emission matrices from a certain sequenceHmmestimate can be used if the sequence is already known, but it has something missingState Probabilities4/18/2014Smartphone Based Human Behavioral Analysis59AdministrationAndy McWilliamsValidation Plan4/18/2014Smartphone Based Human Behavioral Analysis61Sensor Data Extraction & AnalysisRetrieve real-time data from all chosen sensorsGraph and analyze each set of data by performing testsRaw Memory Extraction & AnalysisTest each raw memory extraction optionRetrieve binary image of databasesMachine Learning AlgorithmsUse algorithms to recognize patterns in users behaviorUse HMM algorithm to retrieve deleted dataBudget4/18/201462Smartphone Based Human Behavioral AnalysisAlso received $400 from Dr. Hus budget to purchase Samsung Galaxy S4 MiniScheduleSmartphone Based Human Behavioral Analysis4/18/201463ImpactsHealthcare, Activity Monitoring, Forensics National Science FoundationDr. Hu will use project findings to help teach CPS at rural schools in a virtual environment4/18/201464Smartphone Based Human Behavioral AnalysisThank you! Questions?Chart20.0010.0010.0010.0010.0150.0150.0160.0140.0310.0310.0310.0160.0620.1250.0470.0010.1090.1870.0630.0010.1410.2660.0780.0150.2030.360.110.0150.2820.4540.1250.0010.360.5160.1560.0010.50.6090.1710.0160.9220.6720.2030.0152.251.5620.4220.0465.0312.3750.640.0628.9223.250.8750.09415.5154.1561.0630.12520.7824.9681.3280.17229.5475.891.5940.23437.1416.7971.6590.26647.5947.6562.0310.32857.4538.5312.2970.39123824.865.2181.25515.45337.5158.6412.735957.45358.87514.23561508.15778.54721.53112.1872182.719103.96931.3619.53010.859130.42244.78228.9223935.531154.87568.09439.9846238.625184.57883.93852.9377969.594232.64106.2566.8299361.422245.75130.12583.67211592.922280.765152.218102.73516667.359326.687176.171120.17219326.719390.11204.172141.43722144.313447.531240.375163.48525386.641464.469265.063188.016170000509.875298.671217.765180000561.547330.797244.266190000190000190000267.297200000200000200000301.547DTWFastDTW (radius=100)FastDTW (radius=20)FastDTW (radius=0)Length of Time SeriesTime (seconds)Execution Time of FastDTW on Large Time SeriesSheet1Results on TEK dataRandom DataTrace DataGunx DataSearch RadiusError (FastDTW)Std Dev (FastDTW)Time (FastDTW)Error (Abstraction)StdDev (Abstraction)Time (Abstraction)Error (Band)Std Dev (Band)Time (Band)Error (FastDTW)Std Dev (FastDTW)Time (FastDTW)Error (Abstraction)StdDev (Abstraction)Time (Abstraction)Error (Band)Std Dev (Band)Time (Band)Error (FastDTW)Std Dev (FastDTW)Time (FastDTW)Error (Abstraction)StdDev (Abstraction)Time (Abstraction)Error (Band)Std Dev (Band)Time (Band)Error (FastDTW)Std Dev (FastDTW)Time (FastDTW)Error (Abstraction)StdDev (Abstraction)Time (Abstraction)Error (Band)Std Dev (Band)Time 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(radius=0)Length of Time SeriesTime (seconds)Execution Time of FastDTW on Large Time Series

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