Issues for Large Scale WSN for Medical Care John A. Stankovic Department of Computer Science University of Virginia

What’s Wrong With Wires Tethered to Bed Restricted to Location of Devices

Themes Scaling in many dimensions Heterogeneity Openness Activity Recognition Accuracy too low

Scaling Multi-function BSN Many people in close proximity Rich sensor environments (homes) Multiple homes, CCRC, AL Multiple facilities

With Harvard

With MARC UVA Medical School

SATIRE - Activities * With the Univ. of Illinois – paper appeared in Mobisys

SATIRE Activity classification –Five two-axis accelerometer network for classifying activities with a HMM

SATIRE

Robust Fall Detection Accelerometers only –Difficult to avoid false positives Sitting quickly Jumping Dropping (get) into bed …

Robust Fall Detection 2 TEMPO nodes 3D Accelerometers 3D Gyroscopes Real-time Low false alarms Robust to more situations

Algorithm – 3 Phases Is activity static –Use both accel. and gyros Is static posture, lying down –Use angle between trunk and thigh and gravity Is transition before lying unintentional –Use accel and gyros Q. Li, J. Stankovic, M. Hanson, A. Barth. J. Lach, G. Zhou, Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information, Proc. Body Networks, 2009.

Performance

Scale - Multiple People Interference QoS Architecture

2-BSN Interference 3 TelosB motes in each BSN: –1 source mote on the chest (position 1) –1 source mote on the thigh (position 2) –1 aggregator on the waist (position A) The data rate of the source motes is 50 pkt/s.

Factors Examined Four factors: –Distance between two BSNs –Power Level –Postures –Relative Orientation

Distance Measurement: the power level of the interference BSN is 10. Result: the interference very important up to 2m.

Orientation Two persons are standing. Bars with different colors show represent different relative orientation of the two BSNs including face to face, side to face, back to face, and back to back. Result: the interference is larger when two persons are fact to face, and it’s smaller when the two are back to back.

A Multi-Function BSN Heart rate & blood oxygen saturation Two-Lead EKG Limb motion & muscle activity Sweat Temp.

BodyQoS Goals  Priority-based  Wireless resource scheduling  Providing effective bandwidth Design Constraints  Heterogeneous resources  Heterogeneous radio platforms EKG Light Sweat Data Control QoS for Body Sensor Networks

25 Architecture BodyQoS (1)Schedule wireless resources (2)Calculate effective bandwidth (3)Put radio to sleep (1)Abstract wireless resource for QoS scheduling (2)Implemented by calling real MAC’s functions (1)Decide which streams to serve and which not to serve

Smart Living Space

Large Scale Deployments

Scaling Issues Highly flexible (radio shack model) New sensor types; radios Energy - Some plugged in some not Data association Privacy

Privacy - Many Stakeholders Patients Patients family and friends Doctor – what advantages for them in treating patients Nurse Technician Orderly Admin Social Worker

Privacy - Many Data Types Personal medical data Personal activity data Environmental data Contextual data Longitudinal data System Performance data

Authorization Framework

Fingerprint And Timing-based Snoop attack Front Door Living Room Kitchen Bathroom Bedroom #1 Bedroom #2 Adversary Fingerprint and Timestamp Snooping Device T1 T2 T3 … … TimestampsFingerprints Locations and Sensor Types ? ? ? … V. Srinivasan, J. Stankovic, K. Whitehouse, Protecting Your Daily In-Home Activity Information fron a Wireless Snooping Attack, Ubicomp, 2007.

ADL ADLs inferred: –Sleeping, Home Occupancy –Bathroom and Kitchen Visits –Bathroom Activities: Showering, Toileting, Washing –Kitchen Activities: Cooking hot and cold food High level medical information inference possible HIPAA requires healthcare providers to protect this information Adversary Fingerprint and Timestamp Snooping Device T1 T2 T3 … … TimestampsFingerprints Locations and Sensor Types ???…???…

Performance 8 homes (X10 sensors) –Each home had 12 to 22 sensors 1 week deployments 1, 2, 3 person homes Violate Privacy - Techniques Created –80-95% accuracy of AR via 4 Tier Inference FATS solutions –Reduces accuracy of AR to 0-15%

PDA Real-Time Queries AlarmGate SW on stargate DB

SenQ Interactive, Embedded Query System –Peer to peer Virtual sensors – discover and share Streams – define, discover and share Devices added/deleted Optional Modules Location Transparency UI - Developers, Domain Experts, Users Privacy and Security A.Wood, L. Selavo, J. Stankovic, SenQ: An Embedded Query System For Streaming Data in Heterogeneous Interactive Wireless Sensor Networks, DCOSS, 2008.

SenQ Layers Loosely coupled layers

Sensor Data Sampling & Processing

Virtual Sensors –users fuse streams to make new sensors –sensor drivers can recursively invoke SenQ

Scalable AR Systems More and more activities Finer level of detail –Cooking versus how well one is cooking, or is it deviating from previous pattern

front floor fridg e micr owa ve pant ry cook top sinkflus h entr anc e sinksho wer moti on weig ht light pres sure bed roo m kitch en bath roo m bed roo m kitch en bath roo m bed roo m kitch en bath roo m bed roo m Personal location tracking Kitchen visits bedroom visits bathroom visits eatingtoiletingshoweringsleeping Eating Level Toileting Level Sleeping Level Movement Level Light Level Weight Level DiabetesDepression Light Weight

Summary - Open Problems Scaling Sensor form factor and accuracy AR not robust enough Privacy and Security Interoperability – one-on solutions Cost Deployment time Runtime Assurances Certification Lifetime Data Association

Power Level Result: comparing the PRR at the same distance, we can see that the higher the interference power level, the more interference results.

Posture Result: comparing the PRR for different postures at the same distance, we can see the interference is smaller when two persons are standing.