Wearable nodes in context-aware
wireless sensor networks
CMC Workshop on wearable devices for
bio-medical applications
Marcin Marzencki
Bozena Kaminska
CiBER Laboratory, Simon Fraser University
Ottawa, February 5th 2010
Outline
• Introduction
• Context aware sensor network
• CiBER wearable node
• Mesh network interface
• Powering of wearable nodes
• Conclusion
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CiBER Innovation
Mission statement:
Enable real time access to
physiological, personal and
environmental data with small,
unobtrusive and accessible
wireless devices.
Wireless
Tiny
Reliable
Comfortable
Easy to use
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CiBER Motivation
Safety of people and assets in difficult environments
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Sensors
Communication
ECG
BCG/SCG
Heart rate
Respiration
Body position
Activity level
Localization
Intrusion
Environmental
• Body area network
• Mesh sensor network
(ZigBee)
• Bluetooth
• WiFi
• GSM/UMTS
• Internet
Data processing
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Thresholds
Anomaly
Events
Context
Tracking
Localization
Context sensitive operation
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Wireless Multi-Sensor System
Control and
supervision
Internet
Remote
databases
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Health and activity
Localization data
Environmental data
Dynamic, flexible and reliable
mesh network communication
– Transfer of power between devices
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Research challenges
• Reliability and value of data
– Data needs to be reliable to avoid unnecessary alarms
– Context sensitive operation for correct situation assesment
– Easy to use, integrated system
• Miniaturization
– Devices have to be small to be comfortable
– Cost reduction possible
• Powering
– Size of device is linked with the size of energy source
– Long lifetime increases comfort of use
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Multi-sensor Data Fusion
Localization
Health
Activity
Environment
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Multi-sensor Data Fusion
Personal
identification
Employee
monitoring
Personell
tracking and
protection
Context
awareness
Walk in the
park
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Perimeter
protection
Fitness
Rest at
home
Work
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Context Awareness
Acquire
Process
Combine & Interpret
Take actions
• ECG
• Heart rate
• Activity type
• Send alarm message
• Acceleration
• Body position
• Abnormal data
• Modify behavior
• Location
• Activity level
• Unauthorized entry
• Emit sound
• Identification
• Location
• Localization
• Warn and advise
• Heading
• Restricted zones
• Environment
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Miniaturization
• Motivation
– Devices have to be small to be comfortable
– Cost reduction possible
• Solution
– Integrated components: system-on-chip, system in a package
– Dense integration: multi-layer
CiBER flexible multi-sensor node
Developed in cooperation with CMC Microsystems
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Miniaturization
• Miniature flexible sensor platform
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Developed in collaboration with CMC
Multi-layer assembly
Flexible substrate
Reconfigurable filtering
Based on SoC with uC and radio
Synchronized multi-sensor data
Optimized algorithms
Multi-sensor platform
Security
TAG
device
Example multisensor system architecture.
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Health and
activity
monitoring for
fitness
Physiological
monitoring
and diagnosis
device
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Integrated environment: Ease of use
• Standard mobile phone is used with
the network of sensing devices
• Graphical user interface
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Sensor detection and control
Data transfer and display
Heart rate calculation
Threshold detection
Real time ECG streaming
ECG anomaly detection
Acceleration data transmission
Position detection
Level of activity detection
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Heart
rate
Real time
ECG
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Integrated environment: Ease of use
• Fully functional interface with two modes of operation
Connection control
List of detected nodes
with available
services.
Separate menu for
each detected node.
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HR monitoring
(data display from
multiple nodes possible
at the same time)
Real time
ECG streaming
(multiple nodes
can be reporting
HR, only one
streaming ECG)
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ECG data processing
• Innovative ECG processing algorithms
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New low footprint algorithm developed specially for low power applications
Use of register shifting instead of hardware multiplication
Low memory load
Simplified averaging and filtering adapted for ECG morphology
Result: Low power microcontroller can at the same time acquire multisensor data, perform HR calculation and stream full data outside.
Extensive ECG
data processing
capabilities
ECG
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R-Wave
Detection
Mobile Phone
Heart Rate
Variability
Atrial Flutter
Heart Rate
Cardiac Arrest
R-Wave
Amplitude
Premature
Ventricular
Contraction (PVC)
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Accelerometer data processing
• Acceleration data
– Data filtering and noise reduction on the sensor
– Simple data processing on the sensor
– Extended data processing on the phone
Sensor
Sensor or controller
XYZ
Accelerations
Body
Position
SW Filtering
SW Processing
HW Filtering
SW Threshold
detection
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Activity
Level
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Multi-sensor Data Fusion Example
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Y.Chuo et al., Mechanically Flexible Wireless Multisensor Platfrom
for Human Physical Activity and Vitals Monitoring, Submitted to IEEE TITB
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Mesh network interface
• Mesh network of sensor nodes
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ZigBee standard
Reliable data communication in difficult environments
Environment specific data bearers
Precise localization indoors and outdoors – proprietary algorithms
Simple data processing on the sensor
A1
A2
A3
C1
A4
A5
A6
A7
A8
A9
B1
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C2
C3
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Mesh network interface
• Mesh network of sensor nodes
–
–
–
–
–
–
ZigBee standard
Reliable data communication in difficult environments
Environment specific data bearers
Precise localization indoors and outdoors
Simple data processing on the sensor
Unauthorized entry detection with a unique ID on each device
Outdoors
Indoors
Running
In park
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Work – Sitting,
walking, interacting
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Mesh network interface
• Mesh network of sensor nodes
–
–
–
–
–
–
–
ZigBee standard
Reliable data communication in difficult environments
Environment specific data bearers
Precise localization indoors and outdoors
Simple data processing on the sensor
Unauthorized entry detection with a unique ID on each device
Environmental sensors (i.e. gas, temperature)
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Mesh network interface
• Testing
– Outdoors setup with CiBER mesh sensor
network (Okanagan Valley, BC)
– Indoors setup at SFU with CiBER routers
PEGs
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Powering of Wearable Devices
Biggest challenge in wearable electronics: POWER SUPPLY
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Powering of Wearable Devices
Biggest challenge in wearable electronics: POWER SUPPLY
Battery:
• limited capacity
• big and heavy
Energy
• creates waste
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Only battery
Time
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Powering of Wearable Devices
Biggest challenge in wearable electronics: POWER SUPPLY
Mechanical energy
harvesting:
Energy
• body movement and
deformation is a great
source of power
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Battery &
harvesting
Time
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• local energy storage is
recharged continuously
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Powering of Wearable Devices
Biggest challenge in wearable electronics: POWER SUPPLY
Wireless energy transfer:
• Energy transfer from other
devices
• Transparent for the user
Energy
Wireless
energy transfer
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Time
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Powering of Wearable Devices
Biggest challenge in wearable electronics: POWER SUPPLY
Wireless energy transfer:
Energy
Wireless
energy transfer
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• Energy transfer from other
devices
• Transparent for the user
Device operating time:
UNLIMITED
Time
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Mechanical Energy Harvesting
Body deformation is a great source of power for wearable
electronics:
– joint movement: knees, elbows (wireless pulse oximeter)
– respiration: deformation of chest and stomach (ECG monitor)
– inertia: body movement during intensive activities
Piezoelectric
patches for
energy
generation
Wireless
ECG
Wireless
pulse oximeter
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Mechanical Energy Harvesting
Experimental results
Target values
• Generated power around 1mW
• Device to be light and unobtrusive
• No discomfort for the user
• Polymer Fiber Composite patches
• Rectified voltage charging a capacitor
• Generated power per stroke
• 15V to 25V on 1μF → 0.2mJ
• 6V to 7V on 47μF → 0.3mJ
• For 1 breath per second and two devices:
~ 0.6mW
Charging of 1μF
Charging of 47μF
Strokes
Strokes
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Wireless Energy Transfer
Energy sharing
– Energy-rich device shares its resources Target values
with more constrained one: i.e. mobile
• Arm length transmission distance: 30cm
phone and a wearable node
• Transferred power around 10-100mW
– Limits amount of devices that need to
be recharged
• Energy transfer efficiency > 50%
– Recharging station can be integrated in
various objects: bed, chair etc.
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Wireless Energy Transfer
Energy sharing
– Energy-rich device shares its resources Target values
with more constrained one: i.e. mobile
• Arm length transmission distance: 30cm
phone and a wearable node
• Transferred power around 10-100mW
– Limits amount of devices that need to
be recharged
• Energy transfer efficiency > 50%
– Recharging station can be integrated in
various objects: bed, chair etc.
Experimental results
• Distance: 1m
• Coil diameter: 6cm
• Efficiency of power transfer:
up to 40% after matching
• Operating frequency: 42MHz
1m
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Wireless Energy Transfer
Energy sharing
– Energy-rich device shares its resources Target values
with more constrained one: i.e. mobile
• Arm length transmission distance: 30cm
phone and a wearable node
• Transferred power around 10-100mW
– Limits amount of devices that need to
be recharged
• Energy transfer efficiency > 50%
– Recharging station can be integrated in
various objects: bed, chair etc.
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Hybrid Powering
• Composition of the system
antenna
handheld
device
antenna
– Two methods of supplying energy charging one micro-battery
• Inductive energy transfer from a handheld device (mobile phone,
PDA etc.) to a wireless node
• Mechanical energy harvesting from the environment where the
node operates
Data
processing
transducers
Power management
Local energy
storage
wireless node
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Hybrid Powering
• Composition of the system
Local energy
storage
antenna
remote
device
antenna
– Two methods of supplying energy charging one micro-battery
• Inductive energy transfer from a handheld device (mobile phone,
PDA etc.) to a wireless node
• Mechanical energy harvesting from the environment where the
node operates
Data
processing
transducers
Inductive
harvested
Power
power
power
management
interface
interface
Local energy
storage
hybrid powering module
Ambient
energy
harvesting
wireless node
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Key Issues
What I see as key issues in Wearable Wireless
Devices research
– Comfort of use and acessibility
• Size and reliability
• Powering
• Complexity of operation, interpretation, interfaces
– Validity of data to avoid unnecessary alarms
• Multi-sensor inputs – data fusion, reliability
• Context awareness
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Infrastructure
Needs related to design, prototyping and testing of
Wearable Wireless Devices
– Advanced integration (ex. bare dies, multi-layer, tools,
modeling, test methodology, test environment)
– SoP tools and prototyping facilities
– Polymer carriers and reliable interconnects for partially
flexible miniature devices/microsystems
– Quick packaging solutions and test capabilities
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Conclusion
• Devices need to be comfortable and cost effective to
be widely used.
• Full context-awareness required for reliable data
analysis and false alarms avoidance.
• Innovative power sources and reduction of energy
consumption are keys to miniaturization and comfort of use.
• Multiple sensor inputs required for truly context aware
operation reliable situation assesment.
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The CiBER Team
www.ciber.ca
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Support and Collaborations
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