toward scalable activity recognition for sensor networks
TRANSCRIPT
MITSHUBISHI ELECTRIC RESEARCH LABORATORIESCambridge, Massachusetts
Toward Scalable Activity Recognition for Sensor Networks
Christopher R. WrenMERL Research
Emmanuel Munguia TapiaMIT House_n
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A sensor network for building context.
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Second Implementation
Emmanuel’s MERL internship Summer 2005
Wireless Motion DetectorWires for Power
155 installed and operational
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Why Smarter Buildings?
• Safety & Security– Unusual Activity Detection– Economical Context Sensing– Tuned Escape Routes
• Eldercare – Activity Monitoring– Awareness
• Energy Efficient Buildings– Lighting– HVAC– Integrated building systems
• Socially–Aware Buildings– Presence– Availability– Awareness
• Entertainment• Elevator Demand Prediction• Retail
– Predict densities– monitor reaction to displays
• Industrial Process– Ergonomics– Safety– Efficiency
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How do you build a sensor network?
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Ka
Photo courtesy of Kathy Ryall
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LoCA27 nodes1 receiver
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today155 nodes7 receivers
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How do you use a sensor network?
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SARSEN: Scalable Activity Recognition for Sensor Networks
•Locality of processing–Abstract information early–Reduce communication–Small data means small CPU
•Locality of Context–Build form small components–Small areas are more portable–Independent of context–A hallway is a hallway
•Build understanding–Tracking is not possible–Find consistent explanations–Abstractions reduce space
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Behavior Hierarchy
Action
Activity
Movement
Motion no context, no temporal information
limited local context, no temporal information
more context,significant temporal structure
activities in global context
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Behavior Hierarchy
Action
Activity
Movement
Motion
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Hardware
• 155 sensors, public spaces of a 2000m2 office building floor.• 7 weeks of data and counting• 3x106 observed motion events• ~90-150 people• Collaborative environment• Many kinds of spaces:
– laboratories – cubicles – offices – meeting rooms
• frequent visitors
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Behavior Hierarchy
Action
Activity
Movement
Motiontopology
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Automatically Recovered Topology
TR2005-003: “Similarity-based Analysis for Large Networks of Ultra-Low Resolution Sensors.” To Appear Pattern Recognition Special Issue on Similarity-Based Pattern Recognition
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Behavior Hierarchy
Action
Activity
Movement
Motion
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Movement
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Turn Movement
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Walk Movement
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Still Movement
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Behavior Hierarchy
Action
Activity
Movement
Motion
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Activities
• Temporal Structure: Markov Chains• Trained from hand-written examples
– Visit: stop, stand around, start– Meet: exit, exit, exit.
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Meet Activity
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Visit Activity
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Behavior Hierarchy
Action
Activity
Movement
Motion
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Next Generation
>3 years on AA!802.15.4 radio
Easy to manufacture
Jonathan WesthuesDarren Leigh
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Summary
•Buildings as experts in themselves!
•Cheap to install and operate•Potentially cheap…
…also in terms of privacy
•Aware buildings can be safer, more efficient, richer.
•Hierarchical Behavior Elements…not Tracks
SARSEN
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Thank You!
Christopher R. [email protected]
Emmanuel Munguia [email protected]
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Why Motion Detectors?
They’re cheaper, require less bandwidth, less computation, less memory, but also….
Cameras provide a greater potential for abuse when you vary the intentionality of the person in control of the technology.
Dimensional Metaethics, Carson ReynoldsAnalytic framework for ethical comparison
TR2006-005: Worse is Better for Ambient Sensing, Pervasive 2006 Workshop on Privacy, Trust and
Identity Issues for Ambient Intelligence