Wireless Body Area Sensor Networks

CMPE 58C – Wireless Sensor Networks

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Contents

• Why WBASNs (or BAN or BASN)?

• Application Areas

• Challenges/Requirements

• Architecture

• Communication Issues – Requirements

– Candidate Technologies

– Standardization

References

• M. Patel and J. Wang, “Applications, Challenges and Prospective in Emerging Body Area Networking Technologies,” IEEE Wireless Communications, Feb. 2010

• Min Chen, Sergio Gonzalez, Athanasios Vasilakos, Huasong Cao, Victor C M Leung, “Body Area Networks: A Survey”, Mobile Networks and Applications, 2010.

• Hanson, M. A., H. C. Powell Jr, A. T. Barth, K. Ringgenberg, B. H. Calhoun, J. H. Aylor, and J. Lach, “Body Area Sensor Networks: Challenges and Opportunities”, Computer, 2009.

• Benoît Latré, Bart Braem, Ingrid Moerman, Chris Blondia, Piet Demeester, “A survey on wireless body area networks”, Wireless Networks, 2011.

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Motivation

• So far explored the networking issues of traditional WSNs

• Focus on emerging application areas of pervasive sensing – WBANs

- Ambient Sensing

– Sensing with SmartPhones

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Definition

In order to fully exploit the benefits of wireless technologies in telemedicine and mHealth, a new type of wireless network emerges: a wireless on-body network or a Wireless Body Area Network (WBAN).

Consists of small, intelligent devices attached on or implanted in or around the body which communicate with each other and external networks with wireless communication.

Similar to WSNs:

Small intelligent devices,

Wireless Communication

Advantages: •Flexibility •Effectiveness and Efficiency •Cost effective

Need

• Ageing population; sedentary lifestyle

* WHO stats:

# Diabetics-360 million by 2030

# >2.3 bn. people obese by 2015

# rise in neuro-degenerative diseases

• Fragile healthcare system, rising medical costs

• Shortage of trained health staff in third world

• Shortage of caregivers (long working hours, etc.)

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Realisation

• Strategically placed wearable or implanted sensor nodes

• Job: sample, process and transmit vital signs • What signs?

• Heart rate, blood pressure, temperature, pH, respiration etc.

• Where to? • To a hospital, clinic or a central repository of medical data

• How? • A gateway device (e.g., a cell phone or a PDA) is used as a

gateway device to connect to infrastructure networks like WLAN, WPAN etc.

General Picture of a WBAN

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Source: Mark A. Hanson et al., “Body Area Sensor Networks: Challenges and Opportunities,”

Computer, Jan. 2009

WBANs vs WSNs

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Applications

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Source: M. Patel and J. Wang, “Applications, Challenges, And Prospective In Emerging Body Area Networking Technologies,” IEEE Wireless

Communications, Feb. 2010

Challenges (Requirements for Widespread Adoption)

• Ease of Use: small, unobtrusive, ergonomic, easy to put on, few in number, and even stylish

• Usability by non-ICT experts (e.g.medical staff)

• Safety

• Privacy

• Compatibility/Standardization

• Value to the User

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Technical Requirements

Characteristic Requirement Desired Range

Operating space In, on or around the body Typically 0–3 m and extendable up to 5 m

Network size Modest < 64 Devices per BAN

Data rate Scalable From sub kb/s up to 10 Mb/s

Target Lifetime Ultra-long for implants Long for wearable

Up to 5 year for implants Up to 1 week for wearable

MAC Scalable, reliable, versatile, self-forming

Low power listening, wake up, turn-around and synchronization

Topology Star, Mesh or Tree Self-forming, distributed with multi-hop support

Device duty cycle Adaptive, Scalable From 0.001% up to 100%

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Technical Challenges

1. Frequency Band Selection: 1. Most BAN devices need global operability

2. Facility for low-power usage (less crowded)

3. Less stringent rules for flexible usage and adaptability

4. Solutions proposed: Opening up the 2360-2400 MHz spectrum near ISM for medical BANs and allocating up to 24 MHz in the 413-457 MHz range for medical micropower network

2. Antenna Design: 1. Restrictions on size, material and shape of antenna

2. Hostile RF environment due to changes in wearer’s age, weight changes and posture changes

3. During implants only non-corrosive and bio-compatible material can be used: Platinum or Titanium (both poor) against the usual copper

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Source: M. Patel and J. Wang, “Applications, Challenges, And Prospective In Emerging Body Area Networking Technologies,” IEEE Wireless

Communications, Feb. 2010

Technical Challenges 3. PHY Protocol Design:

1. Minimize power consumption

2. Solution: Quick turn-around from transmit to receive and fast wake-up from sleep mode

3. Seamless connectivity in dynamic environments

4. Energy-Efficient Hardware: 1. Today’s wireless technologies draw relatively high peak

current

2. Also rely on duty cycling between sleep and active

3. Solution: Operation on low peak pulse-discharge current from thin-film (paper) batteries, low-power listening, developing a crystal-less radio*

5. Technical Requirements: 1. Wide variation in data rate, BER, delay tolerance, duty cycle

and lifetime

2. Diverse application environments

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Source: M. Patel and J. Wang, “Applications, Challenges, And Prospective In Emerging Body Area Networking Technologies,” IEEE Wireless

Communications, Feb. 2010

Architecture

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Source: Min Chen, Sergio Gonzalez, Athanasios Vasilakos, Huasong Cao, Victor C M Leung, “Body Area Networks: A Survey”, Mobile Networks and Applications, 2010.

Sensors

• Physiological Sensors – Blood glucose

– Blood pressure

– ECG (electrocardiography)

– EEG (electroencephalography)

– EMG (electromyography)

– Pulse oximetry

– Body temperature

• Biokinetic/Inertial Sensors: • Accelerometer

• Gyroscope

• Ambient Sensors – Humidity, temperature, light, sound …

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Source: Mark A. Hanson et al., “Body Area Sensor Networks: Challenges and Opportunities,”

Computer, Jan. 2009

Signal Processing

• to extract valuable information from captured data – such as falls,

– as well as from trends, such as the onset of fever, daily routine of a user and drifts from the routine

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Average power consumption of wireless transceivers (orange) and microprocessors (blue)

Process or transmit data?

Communication

• Challenged by the dramatic attenuation of transmitted signals resulting from body shadowing – the body’s line-of-sight absorption of RF

energy,

– Movement also causes significant and highly variable path loss.

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Protocol Layer Requirements

• Already talked about the challenges/requirements at the physical layer

• At the MAC Layer – Energy efficiency

– Quality of service: Receiving right data at the right time (real-time, delay, delay variance)

• At the Routing Layer – Mostly a single-hop topology assumed

– Energy efficiency

– Reliability

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Candidate WirelessTechnologies

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Source: M. Patel and J. Wang, “Applications, Challenges, And Prospective In Emerging Body Area Networking Technologies,” IEEE Wireless Communications, Feb. 2010

. The key advantages of BTLE are the strength of the Bluetooth brand and the

promise of interoperability with Bluetooth radios in mobile phones.

Bluetooth SIG has developed the Bluetooth Health Device Profile (HDP) that defines the

requirements for qualified Bluetooth healthcare and fitness device implementations,

Standardization

• The IEEE 802.15.6 Task Group is developing the first industrial standard for the Physical and MAC layers for BAN (done, Feb. 2011)

• Major competition: ZigBee and Bluetooth.

• Holistic standardization needed for plug-and-play interoperability

• ISO/IEEE 11073 Personal Health Data Working Group: Standardization of data exchange between peripheral area network devices and gateway devices

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Source: M. Patel and J. Wang, “Applications, Challenges, And Prospective In Emerging Body Area

Networking Technologies,” IEEE Wireless Communications, Feb. 2010

Interoperability

• Continua Health Alliance (http://www.continuaalliance.org/index.html)

– developing interoperability guideline, testing, and certification program for the emerging personal telehealth ecosystem

– Bluetooth is a candidate technology for lower-layer connectivity,

– endorsed ZigBee healthcare as Continua’s low-power LAN standard.

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WBANs @ CMPE

• WeCare: Wireless Enhanced Health Care

• Fall Detection with Wearable Sensors

• A Smart Couch Design for Improving the Quality of Life of the Patients with Cognitive Diseases

• Designing A Wireless Sensing System for Continuous Behavior and Health Monitoring

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WeCARE – WSN Enabled Home Care

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• Improved care • Improved treatment

compliance

• Better basic data for health related decision-making

• Faster detection of deteriorating health

• Easier to see effects of new/changed medication

• Patient can stay home for longer periods of time

• Cost Efficient

• Fewer/shorter hospital visits

• Fewer acute situations

- Purpose of WeCARE- - System Overview- • Body Area Network Subsystem

• Mobile sensor devices

• Personal Area Network Subsystem • Cell phones, PDA`s • Embedded sensors

• Gateway to the Wide Area Networks • Placed at patient’s home

• Computer with installed software

• Physiological sensor equipment

• Web portal

• For care givers and patient with family

• Accessible through internet browser

• Wide Area Networks for Healthcare • Internet/ADSL, GSM/3G

• End-user healthcare monitoring • Real-time monitoring • Alarm definitions

WeCARE – Architecture

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WeCARE intends to offer to elderly people an integrated homecare solution, enabled by wireless sensor networks.

WeCARE – Solutions

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RFID Tags

Accelaration Sensors

Component Functionality Application

Wireless IP cameras

Temperature, humidity,

sound, light sensors

RFID reader

Base station mote

Internet

Graphical User Interface

Presence (at home or not)

Location (in which room)

Fall and Motion Detection

Different sensing modalities

(ambient light, temperature, sound,

humidity and acceleration

Video broadcasting enhanced with

location tracking

No user functionality

Key component of data flow

Web based remote access

Flexible, personalized alarm

definition

Providing

presence and

location

information for

caregivers

Home

surveillance

Fall detection

Fire alarm

E-mail and SMS notifications in

emergency situations

Alarm and event logging and

archiving mechanism for later

evaluation and monitoring

CURRENT WORK

Fall Detection using Accelerometer Data

• Use a time-frequency analysis to locate the high frequencies

– Discrete Wavelet Transform (DWT)

– Differentiate falls from other activities: lying, sitting, sitting fastly, hopping

– Used a thresholding mechanism to identify falls

Fall Detector Application on a Smartphone

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Objective: Tracking of Patients with neurodegenerative diseases, such as epilepsy Method: Inertial sensors on the smartphone are used to detect abnormal human movement such as falls Application: Provides location information using the GPS module and sends a warning to interested users, such as the caregivers, via SMS, email and Twitter messages.

A Smart Couch Design for Improving the Quality of Life of the Patients with Cognitive Diseases

• Elderly spend most of their time in front a TV on their favourite couch

• The changes in their napping, lying and sitting patterns may be indicators for Alzheimer disease

• designed a smart couch that is equipped with accelerometer, vibration and force resistive sensors to monitor how much time people spend while sitting, lying or napping on the couch and to recognize the drifts from their daily routines

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Experiments

• 4 volunteer subjects, average age of 28

• Sampling the sensors at 20 Hz.

• 15 minutes of data collection from each subject

• KNN and Naïve Bayes classifiers are used

• 95% accuracy is achieved.

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Designing A Wireless Sensing System for Continuous Behavior and Health Monitoring

• A wireless sensing system for continuous monitoring of human behavior in a natural living environment.

• to learn the daily habits of people and detect abnormal behavior or emergency

• Potential users – elderly people who are more likely to have

cognitive problems such as Alzheimer or dementia.

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Deployment

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3 distance sensors, 3 force resistive sensors, 2 light sensors, 1 vibration sensor, and 1 contact sensor. Activities: studying, using internet, watching TV, reading a book, hav- ing a snack, sleeping, going outside.

Paper Discussion

• Next week:

Mercury: A Wearable Sensor Network Platform for High-fidelity Motion Analysis, Konrad Lorincz, Bor-rong Chen, Geoffrey Werner Challen, Atanu Roy Chowdhury, Shyamal Patel, Paolo Bonato, Matt Welsh, Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (SenSys'09), November, Berkeley, CA, 2009.

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