Title: Embedded systems in medical devices
1Embedded systems in medical devices
- Tomasz J.Petelenz, PhD
- Sarcos Research Corporation, Salt Lake City, UT
- S.C.Jacobsen, PHD
- Sarcos Research Corporation, Salt Lake City, UT
2BackgroundSarcos Products Projects
- Medicine
- Artificial Arms
- Micro Catheters LIS
- Programmable Drug Pumps
- Medical Monitoring
- Diabetic Catheters
- Artificial Kidneys
- Home IV Systems
- Micro Systems
- Sensors
- Strain (1,2,6 axes)
- Pressure Flow
- Vibration, Sound Accel.
- Multi Axis Fluid Shear (skin friction)
- Rotary Encoder
- Actuators
- Servo Valves - Micro Motors
- Entertainment
- Jurassic Park Robots - large
- Disney Robots
- Ballys and Primadonna Robots
- Ford Tele Entertainer
- Carnegie Science Museum
- Buffalo Bills - Zinger
- Bellagios Robot Fountains
- Robotics Tele-robotics
- Utah/MIT Dexterous Hand Master
- Dexterous Arm I GRLA Master
- NASA Space Suit Tester
- Sensor Suit - Motion Capture
- VR Mobility Ports - IPORT
- Wireless Communication
- Other Human-Interactive Systems
3Embedded processors in medical devices
- Already ubiquitous growing at the rate exceeding
that of desktop computing - Highly tasks/application-specific
- Generally not highly user-programmable
- Widely used in
- sensing and control
- real time operations
- Must be designed to work at low power in
autonomous, distributed, networked and highly
reliable manner. - Impart limited "intelligent" characteristics to
sensors - adaptability to the changing physical world,
- real time, user-unsupervised functioning.
- Minimize number of parts and cost as compared to
analog electronics - Problem higher power requirements
- At design level, embedded systems require
- highly interactive hardware - software co-design,
- requires new embedded software tools that and
testing systems that cannot be separated from
physical aspects of a device application. - (For example, embedded software controlling an
infusion pump must be co-developed and tested
with all other components - motors, actuators,
sensors, fluid conduits, etc.) - employ real time operating systems (RTOS) that
must be highly fault tolerant - In portable devices, are highly limited in
available power.
4Embedded processors in medical devicesExamples
of system developed or under development at Sarcos
5Drug delivery devices with embedded controllers
6Sensors and controllers 6 DOF Digital Load Cell
7Imaging and hyperspectral systems Sarcos Micro
Camera
8Strain, Multi-axis Strain and Rotary Sensors
Networks
RDTTM IC and Emitter
UASTTM IC Chip
Packaged Rotational Displacement Transducer
(RDTTM)
UniAxial Strain Transducer (UASTTM)
BiASTTM-based 6 DOF Force-Moment Sensor
BiASTTM IC Chip
BiAxial Strain Transducer (BiASTTM)
9Telemedicine / telemonitoring systems sensing,
signal and data processing, communication,
interfaces
10Medical embedded systemsFocus
- Functions
- Recovering and interpreting biological signals
- Optimal data sampling
- Communication major power drain
- Internal wired
- External wired or wireless networks
- Constraints
- Ultra-low power operation
11Technical and scientific issues in embedded,
battery-powered systemsRecovering and
interpreting biological signals
- Effectiveness of current data recovery and
interpretation algorithms in wearable,
autonomously-operating medical devices that rely
on sensor-body contact is limited by - interfacial motion caused by the transducer
motion (relative to skin and muscles), or passive
and active skin/muscle motion that results in
motion-related variations of the biological
signal - changes in the interface chemistry/electrochemistr
y resulting in generation of electrical noise,
and drift - changes in the interface geometry resulting from
tissue dimensional changes (e.g. from swelling or
dehydration) - changes in electrical characteristics of the
interface caused by metabolic and physical
factors (e.g. occlusion of the skin site by the
transducer, changes in hydration, blood flow) - frequently scarce "on-sensor" computational
resources - non-linear phenomena
- multidimensional data forming complex,
time-dependent patterns, such as neuronal
recordings in the CNS - limited available power, bandwidth and physical
space
12Technical and scientific issues in embedded
battery-powered systemsRecovering and
interpreting biological signals
- Future capabilities of smart embedded sensors in
battery-powered autonomous, wearable medical
systems - Optimal signal sampling to minimize power and
storage needs - Recovering (and interpreting) signals by
combining inputs from multiple data sources in
order to extract information. - Performing data reduction, conditioning,
interpretation and storage locally, adaptively,
and "on-demand". - enable distributed, adaptive sensor and control
networks. - Recognizing multidimensional patterns and
interpreting information. - Detecting, interpreting and minimizing motion
artifacts, - E.g. via sensor networks, (especially biomedical
sensor networks), that maximize reliability of
information derived from orthogonal sensors. - Assessing quality and reliability of data
- Assessing confidence in measured data, especially
in uncontrolled and noisy environment - Intelligent power management.
13Physiologic Data Acquisition and HR Recovery
Pulse rate recovery (lower trace) from ECG with
motion artifact-related noise (upper trace
Example of motion-induced interference
Pulse-pressure wave for cuff-free Blood Pressure
measurement ECG (ISU-based sensor, upper trace)
and pressure wave (finger-mounted sensor, lower
trace)
14Sensor output, ECG and Breathing
15Example Data interpretation algorithm
Extensive use of embedded processors
Injury assessment Normal Dead Injured Minor
trauma Major trauma Flags H/S PT/HT
Other Confidence measures
Severity of Injury Hemorrhagic Shock Other
conditions ALGORITHM
Trigger
on demand
HS hemorrhagic shock PT/HT pneumo/hemothorax
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17Technical and scientific issues in embedded
battery-powered, autonomous systemsSignal
Sampling
- Current methods
- Equidistant sampling
- Frequency-dependent sampling
- Problem
- Large number of data points
- Data storage requirements
- Available memory
- Speed
- Power consumption
- Data transmission
- Requires high bandwidth if sampling is not
optimized - Future Optimal, adaptive sampling algorithms
- Minimize power consumption, data storage and
communication bandwidth requirements - Enable high fidelity reconstruction of data in
host computer - Example
- ECG data acquisition, compression and
reconstruction - Image processing
- Neuronal recordings EMG, EEG.
18Non-equidistant sampling(work in
progressco-author Dr.K.Sikorski, University of
Utah)
Equidistant sampling 10k points
Optimal sampling sampling 200 points
co
19Technical and scientific issues in embedded
battery-powered systemsPower
- Currently
- Only few microprocessor families feature
ultralow power consumption - Requirement for battery powered, long lifetime,
and remotely-operating (unsupervised) systems
significantly limits - Functionality of current embedded systems
- Processor selection and system design.
- Issues
- Safety and reliability at power limits
potential for life-threatening situations - Brown-outs/blackout recovery
- Assessment available power
- Loss of data
- Volume and weight
- Batteries are large
- Next generation of smart embedded sensors
operating autonomously in wearable medical
systems must - Minimize power consumption of
- Sensors
- Data processing,
- Data communication
- Operate, with full-functionality, on scavenged,
or biologically-generated power. - Current power scavenging systems are inadequate
(mechanical, thermoelectric, photovoltaic)
20Example Power Consumption in devices with
wireless data transmission
- Integrated Sensor Unit
- ECG acq.
- waveform recovery
- Temp
- Body motion
- Body position
- Wireless comm
21Example Power Consumption Comparison
- Blood pressure measurements
- 1. Oscillometric Method
- a. Cuff based (wrist size) 2.4 mAh per reading
( commercial product specification) - Battery life approx. 400 readings
- b. Cuffless mode 0.2 mAh per reading
- Battery life approx. 4750 readings (approx. 2
yrs) - 2. Velocity measurement or pulse analysis (ToF)
methods - Less than 300 µA
- Battery life more 2 yrs expected
- SpO2 measurement Two-Wavelength Optical Method
- 1. Transmission mode 0.25 mAh per reading
- Battery life approx. 2 years
- 2. Reflectance mode 0.35 mAh per reading (1
min. reading) - Battery life approx. 1.5 years
- 3. Blood Pressure and SpO2 Combined Sensor
Wrist Module - Power consumption estimate 0.45 mAh per reading
( one minute) - Battery life 1 year
- NOTE 1. Coin battery 950 mAh 2. Monitoring
Modes - Emergency cycle Continuous monitoring 1/ minute
for 6 hours, 3 times repeat
22Summary
- Current practical limitations of embedded,
battery-powered medical systems - Power requirements
- Limited availability of devices capable of
ultra-low power operation - Lack of biologically-derived power sources.
- Data acquisition and processing
- Lack of optimal sampling methods
- Signal recovery and interpretation algorithms for
- Multidimensional, non-linear signals.
- Future. Embedded processing will form basis for
- Adaptability to environment / task - Intelligent
transducers - Total integration of analog transducer
functionality with signal conditioning, data
processing, communication and interpretation. - Multidimensional, real time pattern recognition
and interpretation - Learning.
- Plasticity of embedded processor networks
- Distributed, self-organizing, reconfigurable,
adaptive, self-healing systems. - Reliable and adaptive data collection, routing,
storage and control. - Systems capable of generating own adaptive,
autonomous software in response to environment
and/or task.
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24Bio
- Tomasz J.Petelenz, Ph.D., obtained his MS degree
in physics microelectronics from the Silesian
Technical University, Gliwice, Poland, and a
Ph.D. degree in bioengineering from the
University of Utah in Salt Lake City. He has
over twenty years experience in medical and drug
delivery device RD, working on projects ranging
from iontophoretic transdermal drug delivery,
kidney dialysis machines, infusion and injection
devices, to non-invasive physiologic sensors,
wireless data communication and prosthetics.
Prior to joining Sarcos, he conducted
iontophoretic research at the Center for
Engineering Design, University of Utah, and was a
Director of RD at Iomed, Inc. He is currently a
Vice President of Medical Projects at Sarcos
Research Corporation, managing biosensor and drug
delivery development projects, and an Adjunct
Associate Professor at the Department of
Bioengineering, University of Utah . Dr.Petelenz
is an inventor/co-inventor on 25 patents, and
authored/co-authored 37 publications and
presentations.