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TRANSCRIPT
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Real-Time Operating Systems
for eHealth Wearable Devices
Mauro Marinoni, Gianluca Franchino
and Giorgio ButtazzoReTiS Lab, TeCIP Institute
Scuola superiore SantAnna - Pisa
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Outline
Embedded systems in e-health
Evolution
Issues
Real-time Operating systems
Erika kernel
Contributions
Case studies
Telemonitoring
Telerehabilitation
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Scenario
Recent technological advances have enabled the
development of low-cost, miniature medical devices,
they improve the communication among patients,physicians, and other health care centers.
This solution presents a lot of advantages like: reduced hospitalization;
improved efficiency;
speed up the diagnosis of diseases;
detect critical health conditions.
cut total costs;
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Embedded systems in e-health
Embedded systems for medical use have caused deepchanges in medical practice:
Automate tasks in the hospital
Remote logging (e.g., Holter monitor)
Automatic remote acquisition
Tele-rehabilitation
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Data Trust
In remote logging, data are analyzed by the physicianBEFORE being added to the health record;
In remote acquisition, data are added to the healthrecord and LATERvalidated by some physician;
Data included in the health record MUSTbe reliable
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Multi-sensor platforms
In new generations of medical devices:
Data are acquires from multiple sensors;
Sensors are sampled with different periods;
Some sensors sharethe same bus;
Several configuration of sensors and sample timescould be available.
THEN
Concurrency among tasks
Resource management
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Rehabilitation Tele-rehabilitation is characterized by an extra set of
constraints correlated with the feedback that must beprovided to both the remote supervisor and the patient.
To provide a responsive and natural experienceresponse times and jitters must be strictly bounded.
THEN
Temporal isolation reduce interference;
Real-time protocols reduce jitter;
Adaptive scheduling increases flexibility.
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E-health embedded devices
Summarizing the requirements:
Reliable chain of data from sensor to destination;
Multitasking and temporal isolation;
Resource management;
Adaptive scheduling;
Real-time communication.
Together with:
Maximize the lifetime.
Reduce development and certification costs;
Real-Time System
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Erika Enterprise
The proposed solution is based on the tiny real-timeoperating system Erika Enterprise:
Minimal kernel (few Kb footprint) for single and multicore
platforms.
Free, open-source and compliant with the OSEK/VDX APIwith an OSEK OIL compiler integrated into Eclipse.
Support for different architectures (8, 16 and 32 bits), hidinghardware complexity.
Interchangeable scheduling algorithms such as Fixed Prioritywith preemption thresholds, Stack Resource Policy (SRP),and Earliest Deadline First (EDF).
Support for automatic code generation from ScicosLab.
http://www.evidence.eu.com/ -
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ReTiS contributions
Real-time scheduling
aperiodic servers, resource reservation, shared resources
Resource management
overload handling, elastic tasks, adaptive allocation policies
Real-time communication
multi-hop communication, bandwidth allocation, task andpacket co-scheduling
Power management with timing constraintssupport for multiple algorithms for CPU and devices,integration of DVFS and DPM mechanisms
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Power management issues Power Management for Real-Time systems
Started focusing on CPU
Now technology allows more devices CPU is only partof the totalpower consumption
Embedded systems present strong interaction among each other and
with the environment
new constraints for energy-aware algorithms CMOS Power Dissipation due to:
Dynamic power consumption (switching activities)
Static power consumption (leakage current)
Energy Saving through:
Dynamic Voltage/Frequency Scaling (DVFS)
Dynamic Power Management (DPM)
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Power management issues (2)
In embedded real-time systems with energy
constraints, selecting the most appropriate energymanagement policy is not easy.
The result heavily depends on:
the platform characteristics (e.g., energy modesand profiles of the devices, frequency range andpower states of the CPU);
the application constraints (e.g., task deadlines,sensors acquisition delays, communicationbandwidth, etc.)
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Kernel support for power management
The Power Management is performed through a module in
the Real-Time OS The Scheduler selects
independently the tasks toexecute (FP or EDF)
The Power Manager choosesan appropriate runningconfiguration (i.e. speed andvoltage)
Although Applications and theScheduler can communicate
with Power Manager, they areindependent
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Kernel module architecture Power driver and Device driver abstract the device behavior using a
discrete set of states.
The Power Manager is divided in independent modules.
The API module implements the interfacedefined for the interaction with the kerneland Applications.
The CPU policy submodule implementsthe energy saving policies.
The CPU driver makes new configurationoperative.
Device policy implements the devicestrategies.
Device Interface offers a single accesspoint to the devices.
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Case Studies
Remote monitoring of physiological parameters
ASCOLTAproject
Kinematic tracking for telerehabilitation
Part of the WHITEJoint Open Lab
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ASCOLTA ProjectThis project has been funded by the Tuscany region to analyze
the possibility of remotely monitoring patientsrecovering from anheart failure.
The board allowsto acquire ECG,SPO2, breath rate,accelerometersand communicatestrough a WiFi link;
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ASCOLTA Project Information are stored in a server to be automatically
analyzed and accessed by physicians.
IMU d Ki ti
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IMU and Kinetics
Inertial Measurement Units (IMUs) are
becoming essential in monitoring the human body:
Games control
Wellness
Gait analysis
Patient monitoring
Rehabilitation
A. Hadjidja, M. Souila, A. Bouabdallaha, Y. Challala, H. Owenb,
Wireless sensor networks for rehabilitation applications: Challenges and
opportunities, Journal of Network and Computer Applications, 36(1), January 2013.
S G
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First case study: SisTAG
Wearable system for telemonitoring of the knee joint
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Current activity
Monitoring of joints using wireless inertial sensors.
Calculating angles
and measurementerrors
3D Avatar
Data filtering and
analysis
Recognition ofpostures andactions
N R l ti D
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Non Real-time Demo
Nodes Synchronization
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Nodes Synchronization
To compute node position and body postures it is
crucial that all data from nodes are acquired atthe same time instant;
Time information in embedded nodes could be
affected by jitters due to low quality clocksources;
Hence time synchronization
among nodes mustbe provided.
Integration with body kinematics
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Integration with body kinematics
Kinematic constraints of themonitored body can be used tocorrelate nodes positions;
Thus reducing local errorspresent in each single sensor(e.g., drift, random walk)
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Conclusions
E-health devices are more an more complex
everyday.
A real-time OS could provide:
multi-tasking support,
timing constraints guarantee,
HW abstraction to improve portability.
Two use cases have been shown to present theproposed approach.
Questions
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Questions
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thank you!
Mauro Marinoni - [email protected]
Giorgio Buttazzo - [email protected]
Gianluca Franchino - [email protected]