Mobile Body Sensor Networks for Health Applications

1. Mobile Body Sensor Networks for Health Applications Yuan Xue, Vanderbilt Posu Yan, UC Berkeley A collaborative work of Vanderbilt (Sztipanovits, Xue, Werner, Mathe, Jiang) Berkeley (Bajcsy, Sastry’s group) Cornell (Wicker group)

2. Topics • Introduction • Monitoring congestive heart failure (CHF) patients • System overview • Security support • Experiments • WAVE and Berkeley Fit

3. Introduction • The cost of health care has become a national concern. • Medicare was 35 million for 2003 and 35.4 million for 2004 • Health care expenditures in the United States will project to rise to 15.9% of the GDP ($2.6 trillion) by 2010. • Impact of Information Technology • Electronic Patient Records • Remote Patient Monitoring • Integration of wireless communication, networking and information technology • large amount of medical information can be collected to help determine the most effective strategies for treating chronic illness, reducing disability and secondary conditions • improving health outcomes, and reducing the healthcare expenses by more efficient use of clinical resources.

4. Remote Patient Monitoring • Needs to be part of the overall chronic disease management process. • Requires fully integration of • IT Technologies • wireless communication, sensor platform, networking, and database • Clinical enterprise practice • Explicitly incorporates security and privacy policies to protect the end-to-end communication and access of sensitive medical information.

5. System Overview Clinical Foundation Decision Support Patient management Remote Patient Management Protocol models Workflow models Monitor models Execution Engines BPEL Engine EMR Services Monitor Services Monitor Services Execution Engines EMR Homecare System Clinical Information System Service Oriented Architecture Sensor network Computing and Network Infrastructure Technology Foundation End-to-end Security models

6. Monitoring CHF Patients • Provide unobtrusive and persistent monitoring • Weight • Blood pressure • Heart rate • Energy expenditure • Data analysis and feedback • Automated - based on thresholds (i.e. cannot allow rapid weight fluctuation, etc.) • Doctor intervention

7. System Architecture 802.15.4 feedback 802.11/internet Bluetooth Automated Evaluation Medical Database Doctor Evaluation

8. System Components • Hardware • Nokia N810 Internet Tablet • External 802.15.4 basestation • Motion sensor (802.15.4) • Weight scale (Bluetooth) • Blood pressure monitor (Bluetooth) • Software • SPINE (Signal Processing In Node Environment) • Bluetooth daemon • Apache Axis2 WSDL client Nokia N810 Motion sensor Weight scale Blood pressure monitor

9. Remote Monitoring Software Architecture Data sampling Data analysis Data analysis Data analysis Service Layer Sensor control Sensor control Data aggregation Data aggregation Secure Communication Secure Comm. Web service Web service Sensor Authentication Sensor Auth. Buffer Management Comm Layer Media Access Control Media Access Ctr OS/hardware platform TinyOS TinyOS TinyOS Maemo Linux Telos Mote Telos Mote Nokia N10 Workstation USB Sensor Healthcare Gateway Clinical System SPINE

10. Integration With Clinical Information System

11. SPINE • Open-source framework for managing wireless sensor networks • Discovery • 1 motion sensor node • Configuration • Energy expenditure feature @ 1 Hz • Data processing • Calculate kilocalories per minute • SPINEController • Main application which runs a SPINE server, communicates with Bluetooth daemon, runs networking thread (WSDL Client)

12. Bluetooth Daemon • Communicates with weight scale and blood pressure monitor • SDP (Service Discovery Protocol) and SPP (Serial Port Profile) protocols • Hardware configured to send last measurement automatically after measurement is taken • Communicates with SPINEController through text files

13. Apache Axis2 WSDL Client • Runs in thread in SPINEController • Queues data • Sends data in queue to medical database • Automatically retries to send data if unsuccessful (no wireless connectivity) • Data log files • All data • Queued data

14. Security and Privacy Overview • Security Requirements • Data confidentiality • Data integrity • Device authentication • User authentication and access control • Service availability

15. Vertical View Across Different Network Layers • Network security • involves the security issues from link to transport layer security. • provides communication platform security service, including data confidentiality, integrity, source authentication, service availability (e.g., resilience to DoS/jamming attacks) • independent of application semantics • Application security • Web security/ Web service security.(e.g., resilience to SQL injection, cross-site scripting) • User authentication and access control • Data access policy • Ensures the consistency between the privacy policy and workflow

16. Security Mechanisms • Existing security mechanisms and solutions to leverage • Web security solutions • SSL • TinySec • New security service to implement • Device authentication • Sensor-to-gateway secure communication • Resilience to jamming attack -- channel reallocation • Privacy policy enforcement • All above security mechanisms need to be integrated in the system • Challenge: How to ensure the end-to-end system security

17. Network Security Architecture Data sampling Data analysis Data analysis Data analysis Service Layer Sensor control Sensor control Data aggregation Data aggregation Secure Communication Secure Comm. Web service Web service Sensor Authentication Sensor Auth. SSL Comm Layer Channel reallocation Channel reallocation OS/hardware platform TinyOS TinyOS TinyOS Maemo Linux Telos Mote Telos Mote Nokia N10 Workstation USB Sensor Healthcare Gateway Clinical System

18. Horizontal -- along the message communication path • Stage 1: between sensors and mobile gateway • IEEE 802.15.4 communication standard • Pre-key distribution • Sensor device authentication • Encryption and MAC generation based on SkipJack in TinySec • Computation: 5.3 ms • Verification 1.3~1.4ms • Bluetooth • Stage 2: between sensor fusion center and the Vanderbilt web server. • SSL • Client device (or user) authentication • Data encryption and integration protection • Stage 3: Within Vanderbilt Clinical Information System • Integration of user authentication and access control policy with workflow model

19. Application-Layer Security Architecture Web Service Layer Sensor collection Alert Processing Workflow Data archive workflow Detail Alert Policy Layer Policy Enforcement Policy Enforcement Alert Validating Screen Monitoring Screen

20. Experiment on CHF Patient • 5 hour experiment • Nokia N810 battery life approximately 4 hours – required battery change • Energy expenditure every minute • Weight, blood pressure, heart rate measurement at beginning and end of experiment • Hardware malfunction at end of experiment • Failed CRC checks on incoming serial packets

21. Experimental Results raw data Energy Expenditure (kCal / min) moving avg. Time (min)

22. Experimental Results raw data Energy Expenditure (kCal / min) car moving avg. Time (min)

23. WAVE and Berkeley Fit • Social networking in mobile BSNs for health applications • WAVE – API for Android OS • Sensor setup through SPINE framework • Data processing • Action recognition • Energy expenditure estimation • GPS functions • Berkeley Fit • Showcase application for WAVE • Encourages exercise through social interaction

24. Social Interaction • Compete to see who expends the most energy each day • Users will see leaderboard with rankings • Exercise teams • Users exposed to both encouragement and competition • Other features • 1 mile, 5 mile, etc. competition runs for time

25. Planned Experiments • Study of 30 college students • Monitor energy expenditure • Phase 1 • Control group with no social feedback • Phase 2 • Add social feedback • Change in energy expenditure with social feedback enabled?

26. Summary and Future Work • Our system is consistent with the existing clinical enterprise practice, and thus have the capability to scale and become part of the overall patient management process. • Future Work • Full migration to Android • Current Android release has no support for Bluetooth – no external sensors • Android 2.0 will have Bluetooth API • Distributed action recognition • Experiments on obese children • Extension of security models to sensor networking system and integration with application-level security models