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Location and Context Awareness

Location and Context Awareness. Dan Siewiorek June 2012. Outline. Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges. Outline. Distraction Context Aware Computing Location Activity Recognition Applications Research Challenges.

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Location and Context Awareness

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  1. Location and Context Awareness Dan Siewiorek June 2012

  2. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  3. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  4. Moore’s Law Reigns Supreme 7 PENT . PRO 10 PENT . 80486 80860 68040 6 Number of devices 10 80386 SLOPE = 10X INCREASE IN 7 YEARS 68030 80286 68020 5 10 8086 6801 4 4004 8080 10 6802 6800 (Source: Walt Davis, Motorola) 3 10 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 Year Samples Introduced Transistors perProcessor a

  5. Moore’s Law Reigns Supreme Disk Capacity

  6. Moore’s Law Reigns Supreme Cost per Megabyte

  7. Glaring Exception Human Attention Adam & Eve 2000 AD

  8. Distraction Matrix

  9. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  10. Context Aware Computing • Applications that use context to provide task-relevant information and/or services • Context is any information that can be used to characterize the situation of an entity (person, place, or physical or computational object) • Contextual sensing, adaptation, resource discovery, and augmentation

  11. Context Aware Service Examples • Primary Services • Location • Biological measures (heart rate, breathing) • Position of limbs • Derived Services • Difficulty in performing activity • Amount of activity for elderly • “Is Bob coming to the meeting” • Match Making (location, activity, skill level)

  12. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  13. Location Sensing Parameters • Physical (x,y,z) versus Symbolic (room) • Absolute (shared reference grid) vs Relative • Localized Location Computation - where calc • Object Recognition • Accuracy, Precision • Distance, Distribution • Scale • Number of objects per unit infrastructure per time interval • Cost

  14. Fluctuating signals for a single-location over time in the Wean Linux Cluster

  15. Combined data-points from Linux and Windows Cluster in Wean Hall

  16. Location Sensing Approaches • Triangulation • Lateration – multiple distance measurements between known points • Angulation – angle or bearing relative to points with know separation • Proximity • Nearness to known set of points • Scene Analysis • Identify relationship to know points

  17. Triangulation Technologies

  18. Location Service Architecture Comparison • Four main types of location architectures (Centralized Push, Centralized Pull, Distributed Push, Distributed Pull) • Location aware messaging as a representative application • Quantified data flow requirements and messages for location based application • Centralized pull model performs better than distributed for location aware messaging

  19. Location Service Architecture Alternatives

  20. Location Service Architecture Data Rates Comparison of Architectures using Instant Messaging Application Polling/Push Frequency = 1 min ~ 3,000 wireless clients 48 Buddies per User

  21. Systems Issues (Bats) • Aesthetics • Distribution of sensors by space usage • Physical/symbolic boundaries • Overlap • False negatives • Not wearing • Quiet Zone • Not tracked • Cycle: user participation decreases  application degrades  reduced incentive to participate 

  22. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  23. Activity Recognition Through Machine Learning .

  24. Model Generation Variables • Window Sizes • 4, 6 Seconds • Features Extracted • Model • k nearest neighbors • clustering • Support Vector Machine (SVM) • ‘Leave-one-out’ cross validation for optimization and testing

  25. Feature Space After Linear Discriminant Analysis (LDA) Transformation

  26. Feature Subsets and Classification for Body Positions

  27. Activity Recognition Accuracy at Body Locations

  28. Accuracy Classification Recorded Over 100 Minutes

  29. Audio and Light Sensor Clustering

  30. New Sensor Types Increase Range of Activity Recognition Accuracy Physiological Sensors

  31. Cell Phone Activity RecognitionDeployment

  32. Data Quality • In a real-time wireless environment data centric systems are plagued with lost packets and corrupt data. • A dynamic sensor architecture can mitigate these problems.

  33. Centralized Architecture Low Bandwidth Architecture Dynamic Architecture Handling Multiple Sensors Decision Master Device Master Device Master Device Classifier Feature Extraction Sensor Device Sensor Device Raw Sensor Data Sensor Device Aggregation of N sensors takes place between the two devices.

  34. Centralized Architecture Decision • Classifier is static. • Requires knowledge of sensors devices available (N). • Bandwidth utilization will be higher (~500 B/s per sensor) sending all raw data. • Raw Data Aggregator must deal with memory intensive data sets (~1-10 MB) ~4 B/window Classifier Master Device ~1 KB/window Feature Extraction ~1-10 MB/window Raw Data Aggregator ~500 B/s ~500 B/s Raw Sensor Data Raw Sensor Data Indicates wireless link Sensor Device 1 Sensor Device N …

  35. Coping with Data loss in the Centralized Architecture Decision • Process 1: • Buffer data on master device with the Raw Data aggregator. • Forward incomplete windows at least 2/3 full to Feature Extraction. • Ex: Forward a set of 40 data points when 60 are expected. ~4 B/window Classifier Master Device ~ 1KB/window Feature Extraction ~1-10 MB/window Raw Data Aggregator ~500 B/s ~500 B/s Raw Sensor Data Raw Sensor Data Sensor Device 1 Sensor Device N Indicates wireless link …

  36. Ex: With subject 20 a maximum of 604 windows could be classified on. Process 2: Classify only on windows in which all sensors have an output. Coping with Data loss in the Traditional Architecture

  37. Low Bandwidth Architecture Decision ~4 B/window • Lower wireless bandwidth compared to centralized architecture Classifier Master Device ~1KB/window Feature Aggregator ~200 B/window ~200 B/window Feature Extraction Feature Extraction Sensor Device 1 Sensor Device N ~500 B/sec ~500 B/sec Indicates wireless link Raw Sensor Data Raw Sensor Data …

  38. Fuse locally made classifications from multiple sensors. N is dynamic. Confidence information, the probability of each context, is transmitted to Fuser. (~80 B/window) Dynamic Architecture Decision Master Device ~4 B/window Sensor Fuser ~80 B/window ~80 B/window Classifier Classifier ~200 B/window ~200 B/window Feature Extraction Feature Extraction Sensor Device N Sensor Device 1 ~500 B/sec ~500 B/sec Raw Sensor Data Raw Sensor Data Indicates wireless link …

  39. Comparison to Centralized Architecture • Utilizes dynamic sensor set • Increased Accuracy • No static classifier in a central location. • Utilizes heterogeneous algorithms • Best techniques can be used on a per device basis to address: • Power constraints • Computation constraints

  40. ErgoBuddy – Experimental Setup • 11 Subjects • Approximately 22 hours of data total. • 7 Sensor Body Locations • Ankle, Arm, Back, Handheld, Holster, Lanyard, Wrist • 10 Activities • Sitting, Standing, Lifting, Walking, Running, Carrying, Sweeping/Mopping, Stairs, Laddering, Carting

  41. Experimental Issues • 7 Wearable Sensors for activity recognition communicating over Bluetooth. • Approximately 10% packet loss per sensor with current implementation. • Low Bandwidth Architecture Reliability ~48% • 1 packet lost = missed classification • Probability one of seven sensors is down @ 90% reliability: • 0.9 ^ 7 = 0.48 • Dynamic Architecture Reliability ~99.99% • 1 packet lost = continue with N-1 other sensors. • Probability all seven sensors are down @ 90% reliability: • 1-(.1 ^ 7) = .999999

  42. Model Generation • 2 Final Window Sizes • 4, 6 Seconds • 7 Models • 1 model for each body location • 5 Fusion techniques • 0%-90% simulated packet loss environments • ‘Leave-one-out’ cross validation for optimization and testing

  43. Fuser Technique – Lossless Environment Decision Master Device Sensor Fuser Classifier Classifier Feature Extraction Feature Extraction Sensor Device N Sensor Device 1 Raw Sensor Data Raw Sensor Data …

  44. Performance Results • With same number of sensors in a lossless environment fusion yields results 2% worse than a model with access to all sensor’s raw data.

  45. Conclusions • In all lossy environments, 10%+, there was better performance using fusion. • 35% accuracy increase in an environment with 50% packet loss. • The number of sensors can be reduced in low loss environments for power and bandwidth savings. • For our experiment 3 sensors was ideal. • This technique can also be applied to systems of heterogeneous sensors.

  46. Outline • Distraction • Context Aware Computing • Location • Activity Recognition • Applications • Research Challenges

  47. Context Sensing • Basic context • Location • Orientation • Audio samples from the user’s environment • Static data • History of user context • Multiple sensors can be used to infer user’s intent • Wireless Network Card, Digital Compass, Thermometer, Camera

  48. Example Applications • Notification • Alert a user if they are passing within a certain distance of a task on their to do list. • SenSay Context Aware Cell Phone • Meeting Reminder • Alerts a user if they are in danger of missing a meeting. • Activity Recommendation • Recommends possible activities/meetings that a user might like to attend based on their interests. • Proactive Assistant • Answering questions about user’s intent • Proactively preparing user’s workspace based on usage patterns and behavior • Matchmaking • Locating an entity based upon expertise, skills, proximity and/or availability

  49. Distraction Matrix for Portable Help Desk

  50. Technology Location Service Use multiple sources to calculate location (e.g. wireless access point triangulation, ceiling photo match) Give applications simple form Transcoders Translate data to form useful on device Manage Network Disconnects Persistent Proxies for Devices, Users Allow policies to be set Remove burden from individual applications

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