CareNet: An Integrated Wireless Sensor Networking Environment for Remote Healthcare

Introduction

The cost of health care for our nation's aging population has become a national concern. According to the U.S. Census Bureau, the number of people over the age of 65 is expected to hit 70 million by 2030, having doubled since 2000. Health care expenditures in the United States are projected to rise to 15.9% of the GDP ($2.6 trillion) by 2010.

Recent advances in wireless sensor networks have made it possible to deploy wearable sensors on the bodies of patients in a residential setting, allowing continuous monitoring of physiological signals (such as ECG, blood oxygen levels) and other health related information (such as physical activity levels). The application of wireless sensor networks in a medical care environment provides a unique opportunity to shift health care outside a traditional clinical setting to a patient/home-centered setting, and to reduce healthcare expenses through more efficient use of clinical resources and earlier detection of medical conditions.

There remains a significant gap between the availability of the sensing technology and our ability to bring it into general use for home medical sensing. A medical sensing system must provide reliable and privacy-preserving information transmission between patients' homes and the care giver. CareNet is an integrated wireless sensor environment for remote healthcare that addresses these critical needs.

This work was supported in part by TRUST (The Team for Research in Ubiquitous Secure Technology), which receives support from the National Science Foundation (NSF award number CCF-0424422) and the following organizations: AFOSR (\#FA9550-06-1-0244), Cisco, British Telecom, ESCHER, HP, IBM, iCAST, Intel, Microsoft, ORNL, Pirelli, Qualcomm, Sun, Symantec, Telecom Italia and United Technologies.

System Design

CareNet is built upon a heterogeneous networking infrastructure which involves the patient data collection, transmission, and access phases, as shown in Figure 1. Here we focus our discussion on the networking and system design of the data collection phase.

Figure 1: System Architecture

As illustrated in the figure, a two-tier wireless network is used to provide data sensing, collection, transmission and processing functions. At the lower tier, a body sensor network consisting of lightweight wearable sensors provides data sensing and transmission functions. These sensors communicate with each other and the base-station sensors (which are attached to the backbone wireless network) directly using IEEE 802.15.4 wireless standard. We use Telos motes as the hardware devices. For movement sensing and fall detection, these motes are equipped with accelerometers and gyroscopes as shown in Figure 2. At this tier, sensor devices are lightweight, wearable and mobile, which also means they have low computation, communication power and small amount of memory. So in our design, only necessary computational and communication tasks are implemented at these devices.

At the upper tier of the network is a multi-hop IEEE 802.11-based wireless network which provides a high-performance backbone structure for packet routing. We use Stargate single board computers as the hardware devices. A picture of the backbone router is shown in Figure 2. The backbone routers are connected to the base-station motes which communicate with the mobile wearable sensors directly. The Stargate board can also be connected with a web camera and serves as a video sensor. Equipped with IEEE 802.11 wireless adaptors, the backbone routers communicate with each other and relay the movement sensing data as well as video streams to the home healthcare gateway. Using IEEE 802.11 wireless communication standard, this stationary backbone structure provides a high-performance and high-reliability packet routing service. Since IEEE 802.11 has a larger communication range than IEEE 802.15.4, our design also scales much better in terms of local area communication coverage. Finally, the home healthcare gateway serves as an interface between the patient's home and the care giver's medical system, which processes all the sensing data and transmits them to the remote medical care system.

Figure 2: Sensor and Router Devices

CareNet is also built upon a multi-layered software infrastructure based on the features and functions at each of the network tiers. The overall software architecture are shown in Figure 3.

Figure 3: Software Architecture

We use TinyOS operating system and NesC programming language to implement the movement data sensing at the wearable sensors and the data transmission between the mobile wearable sensors and the base-stations. The major functions of the wearable sensors are to sample, synchronize, and transmit the movement data. Beacon messages are used for hand-offs between the base-stations and the mobile sensors when the mobile sensors are moving into or out of the communication range of the base-station sensors. A customer-designed and -implemented protocol based on TinySec is used for mobile sensor authentication and secure communication between mobile sensors and the base-stations.

We use Embedded Linux operating system and the ACE programming environment to implement the network communication among the backbone network routers and between the backbone routers and the home healthcare gateway. ACE is open-source software based on C++, which encapsulates OS concurrency and network programming APIs. We take advantage of ACE's strong communication and concurrency capabilities in our implementation. There are two major functions implemented at this layer: as backbone routing structure, software components are built to route and forward the video and sensor data to the home healthcare gateway; as video sensors, video data sampling and compression functions are also implemented. We use the Linux operating system and the ACE programming environment to develop the application software for the home healthcare gateway, whose function is to collect, aggregate, analyze, and forward the video and movement sensor data.

People

Vanderbilt University University of California, Berkeley Cornell University In collaboration with: Vanderbilt Homecare Services

Faculty

  • Prof. Ruzena Bajcsy, University of California, Berkeley
  • Prof. Shankar Sastry, University of California, Berkeley
  • Prof. Stephen Wicker, Cornell University
  • Prof. Yuan Xue, Vanderbilt University

Collaborators

  • Laura Brown, Vanderbilt Homecare Services
  • Janie Parmley, Vanderbilt Homecare Services

Students

  • Shanshan Jiang, PhD student, Vanderbilt University
  • Yann Cao, PhD student, Vanderbilt University
  • Sameer Iyengar, PhD student, University of California, Berkeley
  • Philip Kuryloski, PhD student, Cornell University

Alumni

  • Prof. Roozbeh Jafari, University of Texas at Dallas

Publications and Presentations

  • Monitoring Elderly, Ruzena Bajcsy, TRUST Autumn 2007 Conference.

Video Demonstration

Funding Agency

NSF TRUST

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