Wireless Sensor Networks in
Healthcare
Potential and Challenges
•
integrate available specialized medical tech.
with wireless networks (ex: wearable
accelerometers with integrated wireless cards
for patient monitoring)
• Benefits: save on medical expenses, time
(less face-to-face appointments required),
allows more participants in clinical trials
Requirements
• Interoperability between biomedical devices
required
• Event ordering, timestamps, synchronization,
quick response in emergencies required
• Reliability and robustness for making
accurate diagnoses and proper functioning in
uncontrolled environments
• Integration of many types of sensors
demands new node architecture
Requirements (cont.)
• Operation in buildings results in further
interference due to walls, etc. decreasing
reliability
• Multi-modal collaboration and energy
conservation
• Multi-tiered data management
• Privacy of records: ownership of information
not always clear
– Priority override must be carefully designed
– Data available during emergencies
– Realtime role-based access control
Acceptance of WSNs by
patients
• Especially important for elderly patients:
– Tendency to reject technology
– Must be intuitive and easy to operate
• A study in which elderly residents of Sydney
participated in an open-ended discussion found:
– Overall positive view of WSNs due to implications
for independence
– Ashamed of visible sensors (design as
unobtrusive as possible)
– Adherence issues due to forgetfulness
– Distrust of technology
– Privacy
Implementation
• Sensors: various types of wearable biomedical sensors with
integrated radio transceivers (ex: accelerometer in bracelet to
detect hand tremors)
– Ad hoc network using Zigbee protocol?
• Low power consumption of protocol makes it desirable for this
application
• Radio signal received by cell phone and transmitted to server
• Analysis of raw data performed via wavelet analysis
• Decision tree or artificial neural network used to decide
appropriate action (data is within normal range, outside normal
range and either does or does not require emergency action,
etc.)
• Data stored in server side database and report is generated to
send to healthcare professional
Monitoring and Data
Transmission
• Monitoring and transmission can occur continuously,
periodically or be alert-driven (case-dependent)
• Transmit differential data to decrease energy
consumption/traffic
• Priority-based transmission: path of transmission
determined by nature of data, with emergency signals
receiving highest priority
• Sensors (and potentially other wireless devices in the
area) form an ad hoc network
– If cell phone fails to transmit data, data can be
transmitted over multiple hops in ad hoc network
to travel within range
Data Transmission (cont.)
• ZigBee could be appropriate
specification for networking biomedical
devices
– Significantly lower wake up time than
Bluetooth (15 ms or less vs. 3 s) > low
power consumption, long battery life
– Inexpensive transceivers
– Capable of establishing self-forming, selfhealing mesh networks
Motion Detection: Wavelet
Analysis
• Continuous Wavelet Transform (CWT)similar to Fourier Transform, but with a variety
of probing functions
• b translates function across x(t) and a varies time scale
•(t), when b=0 and a=1, represents mother wavelet of a
family of wavelets
• problem with CWT - overly redundant and extremely difficult
to recover original signal
Discrete Wavelet Transform
• To limit redundancy, DWT restricts variations
in translation and scale (often to powers of
two)
• Recovery tranformation:
– Where a=2k, b = l * 2k, and d(k,l) is a sample of
W(a,b) at discrete points
• Scaling function:
– c(n) is a series of scalars defining specific
function
• Wavelet:
– d(n) is a series of scalars related to x(t)
Filter Banks
• Most basic filter bank: x(n) is divided
into two - ylp(n) and yhp(n), using a
digital lowpass filter H0 and highpass
filter H1 respectively
Filter Banks (cont.)
• Using this method, twice the points of original
function must be generated
• Compensate by downsampling
• Signal smoothed by series of low pass filters
• Original signal broken down into frequency
bands > useful information about signal can
be determined