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