BSN Architectures

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Architecture Design Considerations

The design of Body Sensor Networks follow on from the general Wireless Sensor Network design criteria and involve topics such as power optimisation, reliability, privacy etc. However in the context of body-worn sensors some new requirements come in to play such as device size, how well the device integrates into environment and aspects of mobility. In general for Body Sensor Networks we can define several criteria that must be met for a successful solution. These include:

  • Selection of Processor and Node type - According to Lo et al [1] choice of processor depends on application. Traditional wireless sensor networks used power heavy ARM type processors for applications such as PDAs. For low powered applications, conventional low power microcontrollers are often used,. For instance, the Atmel ATmega 128L processor is used in MICAz, DSYS25, Ember and Fleck. Compared to the ARM processor, the ATmega processor is a relatively slow 8-bit processor; however, the ATmega processor requires much less power than the ARM processor and it can operate at only 2.7V.
  • Reliability: As the application is dealing with potentially life-threatening data, the reliability of measurements and dependable message delivery to healthcare professionals is essential.
  • Power Management: Power optimisation is essential. Ideally the nodes operate in the most power efficient mode as possible.
  • Time synchronization: Each sensor runs its own clock and has a different sample frequency (example one sensor sampling ECG, another sampling pulse oxygen...). Consequently time synchronization between sensors is needed.
  • Message delivery: The architecture should allow real-time delivery of emergency vital signs for both indoor and outdoor environments. Messages carrying emergency vital signs data require minimal delays.
  • Frequency of signal transmission and the amount of information: Important questions are, how often data has to be transmitted and what is the volume of data. Policies need to be put in place that dictate if the data is sent in real-time or if there is a store-and-forward system or maybe some combination of these exists i.e. emergency data is sent in real-time always, whereas routine data is stored and forwarded at a later stage
  • Scalability: The architecture should scale well in terms of the number of patients and the number of sensors on each patient. The system should not 'break' if further sensor nodes are added.

A 'typical' architecture for a Body Sensor Network System is shown here

Typical architecture for a Body Sensor Network system showing the major components

Example Architectures

As most of research has been in the area of wireless sensor networks there has been a shortage of hardware platform support for Body Sensor Networks. Imperial College London designed a system called the BSN Node to act as a hardware platform for the development of Body Sensor Networks. With its stackable design, different type of sensors can easily be integrated. By adopting the IEEE 802.15.4 standards, sufficient bandwidth is available for demanding continuous physiological and context sensing. With a size of 26mm, the BSN node is ideal for developing wireless biosensors.

Saadaoui and Wolf (2) describe a Body Sensor Network for monitoring of elderly people with cardiac arrhythmia. The network consists of a pulse oximeter sensor which monitors the blood oxygen saturation (SpO2) and heart rate (HR) and an Electrocardiogram sensor (ECG) for monitoring heart activity. The system is concerned with transmission of both routine and emergency vital signs in both indoor and outdoor environments.

Jovanov et al at the University of Alabama have developed a system called ActiS that they are targeting at… “post-stroke rehabilitation, orthopedic rehabilitation (e.g. hip/knee replacement rehabilitation), and supervised recovery of cardiac patients.

HealthGear consists of a set of non-invasive physiological sensors wirelessly connected via Bluetooth to a cell phone which stores, transmits and analyzes the physiological data, and presents it to the user in an intelligible way. The system has been targeted at detecting sleep apnea incidents.

System developed at Harvard includes ECG, Pulse Ox and EMG sensors. This system has been designed from the ground up including a full query and performance monitoring environment.

"Assisted-Living and Residential Monitoring Network for pervasive, adaptive healthcare"(6) system developed by researchers at the University of Virginia- The system is intended for large scale monitoring of people in residential care settings The system monitors both the Circadian Activities (i.e. the 24 hour behavioral patterns) of the people, plus it also monitors physiological data (ECG, Pulse Ox etc). The hardware is based on MicaZ motes on Tiny OS [1]. It integrates environmental and physiological sensors in a scalable, heterogeneous architecture. The system features context-aware power management, dynamic privacy policies, and data association. Communication is secured end-to-end to protect sensitive medical and operational information.

The miTAG platform is a collaboration between Harvard, Johns Hopkins University, Washington Hospital Centre and Aid Networks. It is an out-of-the box emergency response system that automatically tracks patients throughout each step of the disaster response process, from disaster scenes, to ambulances, to hospitals.

Research Challenges

The research challenges for Body Sensor Networks have been discussed in detail in the Design Aspects of Body Sensor Networks Wiki and also in the Roadmap document. However in summary, architectural challenges include;

References

  1. ↑ Benny Lo and Guang-Zhong Yang. "Architecture for Body Sensor Networks"
  2. ↑ Benny Lo and Guang-Zhong Yang. "Architecture for Body Sensor Networks"
  3. ↑ Sana Saadaoui and Lars Wolf. Architecture Concept of a Wireless Body Area Sensor Network for Health Monitoring of Elderly People. Consumer Communications and Networking Conference, 2007. CCNC 2007. 4th IEEE Volume , Issue , Jan. 2007 Page(s):722 - 726
  4. ↑ Emil Jovanov, Aleksandar Milenkovic, Chris Otto and Piet C de Groen.”A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation”. Journal of NeuroEngineering and Rehabilitation.March 2005.
  5. ↑ Nuria Oliver, Fernando Flores-Mangas. HealthGear: A Real-time Wearable System for`Monitoring and Analyzing Physiological Signals. Proceedings of the International Workshop on Wearable and Implantable Body Sensor Networks (BSN’06). 0-7695-2547-4/06
  6. ↑ Victor Shnayder, Borrong Chen, Konrad Lorincz, Thaddeus R. F. FulfordJones, and Matt Welsh. "Sensor Networks for Medical Care". http://www.eecs.harvard.edu/~mdw/papers/codeblue-techrept05.pdf
  7. ↑ http://www.cs.virginia.edu/wsn/medical/research.html
  8. ↑ Gao et al, Wireless Medical Sensor Networks in Emergency Response: Implementation and Pilot Results”http://www.eecs.harvard.edu/~mdw/papers/aidn-ieeehst08.pdf.

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