1. Wireless Sensor Networks in Healthcare
Presented by Mohammad Hossein Homaei President of Wireless Sensor Networks Laboratory of Iran
WSNLAB Courses, 2015
Feb 2015

2. 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
Feb 2015

3. 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
Feb 2015

4. 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
Feb 2015

5. 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
Feb 2015

6. 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
Feb 2015

7. 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
Feb 2015

8. 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, self-healing mesh networks
Feb 2015

9. 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
Feb 2015

10. Discrete Wavelet Transform
To limit redundancy, DWT restricts variations in translation and scale (often to powers of two)
Recovery transformation:
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)
Feb 2015

11. 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
Feb 2015

12. Filter Banks (cont.)
Using this method, twice the points of original function must be generated
Compensate by down sampling
Signal smoothed by series of low pass filters
Original signal broken down into frequency bands > useful information about signal can be determined
Feb 2015