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Smart Environments for Occupancy Sensing and Services

This paper explores innovative methods for occupancy sensing and services in smart environments, covering various challenges and technologies such as infrared, ultrasound, RF, vision location detection, and location estimation algorithms. It also discusses Bayesian filtering algorithms, routine learning, and platforms like EasyLiving and Aware Home. The authors conclude that while there is no perfect system, advancements can be made in predicting users' actions proactively.

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Smart Environments for Occupancy Sensing and Services

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  1. Smart Environments for Occupancy Sensing and Services Paper by Pirttikangas, Tobe, and Thepvilojanapong Presented by Alan Kelly December 7, 2011

  2. Smart Environments and Location • Smart spaces provide location-based services • Challenges • Assigning place names and a naming ontology • Identifying observed people and objects • Computing accurate location of observations

  3. Location and Smart Environments • Sensors observe physical phenomena • Challenges • Fusing observations from multiple sensors • Removing noise and interference • Compensating for environmental variations

  4. Infrared Location Detection • Each person wears transmitter badge • Fixed receivers report to central server • Limitations • Very short range • Line-of-sight needed • Fluorescent lighting and direct sunlight interfere

  5. Ultrasound Location Detection • Active Bat • Bat (transmitter) on person/object sends pulse • Fixed receivers report to central server • Uses time-of-flight trilateration • Cricket • Object is the receiver and does the calculations • Uses TDOA between ultrasound and RF • DOLPHIN - distributed positioning algorithm

  6. RF Location Detection • Frequency Modulation (FM) • Signal strength between FM radio stations • Wi-Fi • Signal strength between access points • Accuracy depends on AP density and mapping • Ultra-wideband (UWB) • Very precise measurement of UWB radio pulses • Lower sensor density necessary

  7. Vision Location Detection • Cameras track persons or objects • Motion • Body parts (by color) • Face detection or recognition

  8. Pressure Location Detection

  9. Location Estimation Algorithms • Occupancy sensing provides abstract information about a user’s place • Movement, and/or • Static position, and/or • Relative distance to other objects • Bayes filtering • Noise indicates most probable state • Algorithm estimates angle and distance

  10. Bayes Filtering Algorithms • Kalman filter • Used for tracking moving objects • 3 extended Kalman models • Position • Position-Velocity • Position-Velocity-Acceleration

  11. Bayes Filtering Algorithms • Particle Filter • Estimates location at given time • Builds a particle cloud — a distribution cloud of a finite number of (position, probability) pairs

  12. Routine Learning • Days/weeks/months of observations • Identification of critical places • Naming or geo-coding of these places • From data, algorithm can predict path • Then, smart services can be provided • Location-based reminders • Advice based on next step of learned routine

  13. Platform: EasyLiving • Microsoft Research • Tracks person and their interaction with system • Computer session can follow user to a new device • Local lights, speakers, etc. turned on and off • Sensors • 3D stereo cameras • Pressure mats • Thumbprint reader • Keyboard login

  14. Platform: Aware Home • Georgia Institute of Technology • Research focused on evaluating user experiences in the home domain • Sensors • Ceiling cameras • RFID floor mat system • Door lock fingerprint readers • Voice recognition

  15. Conclusions • Authors: there is no one ‘perfect’ occupancy sensing system • Accuracy • Privacy • User preference • Cost • Authors: Next steps are to accurately predict users’ actions ahead of time

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