Smart Environments for Smart Environments for Occupancy Sensing and Occupancy Sensing and

ServicesServices

Paper by Pirttikangas, Tobe, and Paper by Pirttikangas, Tobe, and ThepvilojanapongThepvilojanapong

Presented by Alan KellyPresented by Alan KellyDecember 7, 2011December 7, 2011

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Smart Environments and Smart Environments and LocationLocation

►Smart spaces provide location-based Smart spaces provide location-based servicesservices ChallengesChallenges

►Assigning place names and a naming ontologyAssigning place names and a naming ontology►Identifying observed people and objectsIdentifying observed people and objects►Computing accurate location of observationsComputing accurate location of observations

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Location and Smart Location and Smart EnvironmentsEnvironments

►Sensors observe physical phenomenaSensors observe physical phenomena ChallengesChallenges

►Fusing observations from multiple sensorsFusing observations from multiple sensors►Removing noise and interferenceRemoving noise and interference►Compensating for environmental variationsCompensating for environmental variations

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Infrared Location DetectionInfrared Location Detection

►Each person wears transmitter badgeEach person wears transmitter badge►Fixed receivers report to central serverFixed receivers report to central server►LimitationsLimitations

Very short rangeVery short range Line-of-sight neededLine-of-sight needed Fluorescent lighting and direct sunlight Fluorescent lighting and direct sunlight

interfereinterfere

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Ultrasound Location Ultrasound Location DetectionDetection

►Active BatActive Bat Bat (transmitter) on person/object sends pulseBat (transmitter) on person/object sends pulse Fixed receivers report to central serverFixed receivers report to central server Uses time-of-flight trilaterationUses time-of-flight trilateration

►CricketCricket Object is the receiver and does the calculationsObject is the receiver and does the calculations Uses TDOA between ultrasound and RFUses TDOA between ultrasound and RF

►DOLPHIN - distributed positioning algorithmDOLPHIN - distributed positioning algorithm

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RF Location DetectionRF Location Detection

►Frequency Modulation (FM)Frequency Modulation (FM) Signal strength between FM radio stationsSignal strength between FM radio stations

►Wi-FiWi-Fi Signal strength between access pointsSignal strength between access points Accuracy depends on AP density and mappingAccuracy depends on AP density and mapping

►Ultra-wideband (UWB)Ultra-wideband (UWB) Very precise measurement of UWB radio Very precise measurement of UWB radio

pulsespulses Lower sensor density necessaryLower sensor density necessary

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Vision Location DetectionVision Location Detection

►Cameras track persons or objectsCameras track persons or objects MotionMotion Body parts (by color)Body parts (by color) Face detection or recognitionFace detection or recognition

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Pressure Location DetectionPressure Location Detection

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Location Estimation Location Estimation AlgorithmsAlgorithms

►Occupancy sensing provides abstract Occupancy sensing provides abstract information about a user’s placeinformation about a user’s place Movement, and/orMovement, and/or Static position, and/orStatic position, and/or Relative distance to other objectsRelative distance to other objects

►Bayes filteringBayes filtering Noise indicates most probable stateNoise indicates most probable state Algorithm estimates angle and distanceAlgorithm estimates angle and distance

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Bayes Filtering AlgorithmsBayes Filtering Algorithms

►Kalman filter Kalman filter Used for tracking moving objectsUsed for tracking moving objects 3 extended Kalman models3 extended Kalman models

►PositionPosition►Position-VelocityPosition-Velocity►Position-Velocity-AccelerationPosition-Velocity-Acceleration

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Bayes Filtering AlgorithmsBayes Filtering Algorithms

►Particle FilterParticle Filter Estimates location at given timeEstimates location at given time Builds a particle cloud — a distribution Builds a particle cloud — a distribution

cloud of a finite number of (position, cloud of a finite number of (position, probability) pairsprobability) pairs

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Routine LearningRoutine Learning

►Days/weeks/months of observationsDays/weeks/months of observations► Identification of critical placesIdentification of critical places►Naming or geo-coding of these placesNaming or geo-coding of these places►From data, algorithm can predict pathFrom data, algorithm can predict path►Then, smart services can be providedThen, smart services can be provided

Location-based remindersLocation-based reminders Advice based on next step of learned Advice based on next step of learned

routineroutine

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Platform: EasyLivingPlatform: EasyLiving

►Microsoft ResearchMicrosoft Research► Tracks person and their interaction with Tracks person and their interaction with

systemsystem Computer session can follow user to a new Computer session can follow user to a new

devicedevice Local lights, speakers, etc. turned on and off Local lights, speakers, etc. turned on and off

► SensorsSensors 3D stereo cameras 3D stereo cameras Pressure matsPressure mats Thumbprint readerThumbprint reader Keyboard loginKeyboard login

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Platform: Aware HomePlatform: Aware Home

►Georgia Institute of TechnologyGeorgia Institute of Technology► Research focused on evaluating user Research focused on evaluating user

experiences in the home domainexperiences in the home domain► SensorsSensors

Ceiling camerasCeiling cameras RFID floor mat systemRFID floor mat system Door lock fingerprint readersDoor lock fingerprint readers Voice recognitionVoice recognition

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ConclusionsConclusions

►Authors: there is no one ‘perfect’ Authors: there is no one ‘perfect’ occupancy sensing systemoccupancy sensing system Accuracy Accuracy PrivacyPrivacy User preferenceUser preference CostCost

►Authors: Next steps are to accurately Authors: Next steps are to accurately predict users’ actions ahead of timepredict users’ actions ahead of time