WearMeSoC Multi Functional Wearable Wireless Medical Monitoring Based on a Multi Channel Data Acquisition and Communication Management System on a Chip
Schekeb Fateh, Integrated Systems Laboratory, ETH Z端rich Nano-Tera Annual Plenary Meeting, May 4th, 2015 04/06/15
Introduction
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Introduction
Challenges
Opportunities
Escalating Medical Costs
§ Wireless Revolution § Advent of M2M and IoT § Smartphones and Tablets § Nano Electronics Integration [Source: Swiss Federal Statistical Office]
§ Aging Population § Growing need of the elderly § Silent Suffering
§ Potentially Benefiting Healthcare
WearMeSoC Objective: Develop technologies essential to miniaturize wireless monitoring, and demonstrate its effectiveness in real medical use cases 04/06/15
Introduction
Wearable Devices § Clinic & point-of-care devices: § Uncompromised biosignals acquisition and processing quality required § High cost and power consumption § Not portable
§ Wearable devices: § Need to trade off medical-grade signal quality with: § Minimal dimensions § Ultra-low power consumption & low cost § Multi-sensor support with limited onboard signal processing resources
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Introduction
Current Wearable Device with Wireless Link § Biomedical data acquisition ASIC supporting: § ECG/ EMG/ EEG/ EOG … § 3.2kHz signal bandwidth § DC signal tracking
§ Device based on this ASIC § Portable § Medium size § Wireless connection to monitoring display
[P. Schonle et al., Modular multi-sensor platform for portable and wireless medical instrumentation, BioCAS, 2014]
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Introduction
Modular Biomedical Data Acquisition Platform
§ Development platform: § Modular § Flexible § Programmable
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VivoSoc: Intelligent Sensor Front-end with Wireless Link 04/06/15
VivoSoc
VivoSoC: Biomedical Data Acquisition System-on-Chip Area:
3.8 mm x 2.8 mm
Technology:
130 nm CMOS (tape-out: April 2015)
Architecture:
Dual core RISC with DMA, 2 kByte Cache, 32 kByte L2, 16 kByte TCDM
Front-end:
8 analog channels with 108 dB CMRR, 3.2 kHz bandwidth
Peripherals:
• • • •
2 quad SPI masters 1 SPI slave 8 GPIO, 1 JTAG 1 UART
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VivoSoc
Vision of VivoSoC § Potential Application: Build intelligent biomedical platform with only 3 ASICs including RF capability
Flash Mem
Electrodes Connector
VivoSoC
RF Modem
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Field of Applications
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Electrocardiography (ECG) and Pulse Oximetry USZ Pulmonary Department 04/06/15
Pulse Oximetry
Pulse Oximetry Hardware
Oximetry module for the WearMeSoC development platform based on discrete components
walking and arm swinging
§ First experiments based on discrete hardware § Test of algorithms for motion artifact and ambient light suppression § Crucial intermediate step towards an integrated solution
arm swinging
§ Specification
§ First Oximetry ASIC:
still vertical movement
horizontal movement
Oscillating finger
Raw data of a test recording showing heavy motion artifacts for different movements
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Pulse Oximetry
Ambulatory Respiratory Monitoring with Lung Disease § 40 patients with chronic obstructive pulmonary disease performed several 6MWT in Zurich (490m), Davos Clavadel (1650m) and Davos Jakobshorn (2590m) § Hypothesis: SpotSpO2 differs from values derived at the end of 6MWT by analysis of SpO2 trends (TrendSpO2) downloaded from pulse oximetry memory after 6MWT § Altitude had no influence on agreement § Visual spot readings and graphical analysis of SpO2 recordings at end of 6MWT agree well on average
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Electrooculography (EOG) Fitness-to-Drive Test for the Elderly USZ Neurology Department 04/06/15
EOG
Mobile Eye Tracking Scene camera with synchronization LED
Vertical EOG channel
Horizontal EOG channel Live view of channels
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EOG
Achievements § Sophisticated synchronization method § Optimal-polynomial 9point calibration procedure § System linearity over +/-15deg § Robust event detection (blinks, fast eye movements, fixations)
Target Eye Mean
2°
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Electroencephalography (EEG) USZ Sleep Analysis Lab 04/06/15
EEG
Automatic Artifact Detection in Long-Term EEG Recordings [A. Wierzbicka et al.: Sleep Disorders Center, Institute of Psychiatry and Neurology Warsaw, Poland]
§ Implemented algorithms for automatic artifact detection § Identify artifact free segments for further analysis § Best to combine several algorithms 04/06/15
EEG
Identification of Microsleep Episodes
§ Microsleep episodes are characterized by a change in oscillatory activity § Microsleep episodes mostly associated with a lack of eye movements, and often occurred after brief episodes of alpha activity (~10 Hz) § Future work: Develop a method for (semi-)automatic detection of microsleep episodes 04/06/15
Electromyography (EMG) Universita di Bologna Micrel Lab 04/06/15
EMG
Platform for Evaluation of EMG Signals and Hand Gesture Recognition using Cerebro JTAG/SWD
SD CARD BLUETOOTH MODULE BLUETOOTH REGULATOR
BATTERY
ANALOG
USART PRESSURE SENSOR I2C
SENSORS REGULATOR
JTAG
SPI
IMU
USB
DIGITAL
ARM Cortex M4 MCU
MCU REGULATOR
SPI
CEREBRO
ELECTRODES CONNECTOR
CEREBRO DIGITAL CEREBRO ANALOG REGULATOR REGULATOR
POWER MANAGEMENT
• Cerebro interfaced with the ARM Cortex M4 to process EMG signals • Data is transferred using Bluetooth to a mobile device http://www.touchbionics.com/ 04/06/15
EMG
Hand Gesture Recognition [S. Benatti et al. EMG-Based Hand Gesture Recognition with Flexible Analog Front End, BioCAS 2014]
92% accuracy in estimation of 7 gestures
Digitally-controlled AFE
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Conclusions
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Conclusions
Conclusions § Successful tests of portable device through medical research partners § System-on-Chip for biomedical platform called VivoSoC realized § ASIC implementation of Pulse Oximetry Hardware § Ultra low power cellular modem implemented by industry partner § Additional promising applications gained: § EMG based gesture recognition § Ultra-low power implantable devices
§ First implantable system with size of 0.8 cm3 04/06/15