Smart Embedded Solutions for

Connected Health

Prof. Abbes Amira

The 2015 International Conference on Electrical and Electronic Engineering (IC3E 2015)

© 2015 Abbes Amira

• Connected health is the convergence of medical devices,

security devices, and communication technologies

• It enables patients to be monitored and treated remotely

from their home or primary care facility rather than

attend outpatient clinics or be admitted to hospital

Connected Health?

© 2015 Abbes Amira

Healthcare Lifestyle

Technology

Sweet spot

Context

© 2015 Abbes Amira

Wireless Body Area Network

• Acquisition and transmission

• Physiological data

• Fall detection, ECG, Gait analysis

Some Challenges

• Extreme energy efficiency

• Data fusion, analysis and security

• Data classification and transmission

Compressive sensing

• Reduce acquired data

• Reduce power consumption

• Increase performance

• Complexity in the reconstruction side

Context

© 2015 Abbes Amira

Research: Focus Areas

• Video, image and signal

processing

• Reconfigurable computing and

HPC

• Connected health systems

• Medical imaging

• Data visualisation, analysis and

processing

• Biometrics and security

• Cloud computing

© 2015 Abbes Amira

• Research focuses on pioneering future directions and

innovation software tools and intelligent embedded

systems with applications in video and image

processing, connected health and biomedical signal

processing

Research: Focus Areas

© 2015 Abbes Amira

• The research aims at the development of software tools

and hardware accelerators for multidimensional data

acquisition, visualisation and analysis using advanced

compression standards, pattern recognition and

classification algorithms

Research: Focus Areas

© 2015 Abbes Amira

Funding and Collaborations

© 2015 Abbes Amira

Funding and Collaborations

© 2015 Abbes Amira

Outline

• Concepts and Challenges

• Emerging Technologies

• Smart Connected Health Solutions

• Fall Detection

• ECG Monitoring

• Medical Imaging

• Other Applications

• The Future?

© 2015 Abbes Amira

Concepts and Challenges

© 2015 Abbes Amira

Instrumented

Measure, sense and see

the exact condition

Interconnected

Communicate and interact

with each other

Intelligent

Respond to change,

predict and optimize for

future events

Embedded Sensors,

automatic capturing of

data, embedded computing

Real-time communications

Data analysis, pattern

recognition, use rules and

logic, intelligent reporting

Through greater levels of instrumentation, interconnectivity and intelligence,

smarter health monitoring solutions are possible

Connected Health: Concepts

© 2015 Abbes Amira

• Connected health is a new model for health management which

puts the correct information in the correct hands at the correct

time

• Patients and clinicians can make better decisions that can save

lives, save money and ensure a better quality of life during and

after treatment

• Not just about technologies but also about connecting people

and information within a system

• Includes terms such as eHealth, Digital Health, mHealth,

Telehealth, Telecare, remote care, and assisted living

Connected Health: Concepts

© 2015 Abbes Amira

Connected Health: Concepts

© 2015 Abbes Amira

Usage shifting toward prevention

and management

Connected Health: Concepts

© 2015 Abbes Amira

• Big Data

• Wearable devices and Internet of Things

• Data Analytics and Security

Connected Health: Challenges

© 2015 Abbes Amira

Connected Health: Challenges

• Data is growing and moving faster than

healthcare organizations can consume it

• 80% of medical data (EMRs, images,

physicians notes…etc) is unstructured and

is clinically relevant

• Healthcare organizations are leveraging big

data technology to capture all of the

information about a patient

• Successfully harnessing big data unleashes

the potential to achieve the critical

objectives for healthcare transformation

© 2015 Abbes Amira

Connected Health: Challenges

IoT, Big data key to overcoming

healthcare bottlenecks by 2025

Source: Survey, Firstpost. Friday, July 31, 2015

© 2015 Abbes Amira

• Big Data Analytics can Prevent Healthcare Fraud

• Scenario: If doctors and pharmacies can access controlled

substance history information quickly and at the point of care, it can

help them make better prescribing decisions and identify potential

prescription drug abuse. With the ability to combine multiple data

sources, analyze data and quickly deliver insights, pharmacies,

doctor offices and hospitals can track abnormal activity to mitigate

prescription drug abuse. Rather than just delivering raw data to

healthcare professionals, sophisticated databases will give

healthcare professionals a larger picture that allows them to address

why, where, when and how these issues are arising

Connected Health: Challenges

© 2015 Abbes Amira

• Applications of Emerging Technologies such as Social

Media, Cloud and Mobile for Healthcare

• Electronic Health Records

• Knowledge Generation and Analytics in Healthcare

Settings (data mining, knowledge management, decision

support systems, data visualisation)

• Health Information Systems for Chronic Disease

Management

• Mobile Apps, Healthcare and Analytics

Connected Health: Challenges

© 2015 Abbes Amira

• Evaluation and Assessment of Health Information Systems

and Technologies

• Confidentiality, Privacy and Data Security in Health

Informatics

• Remote or Resource Poor (international and local use of

Health Informatics in remote and resource poor locations)

• Acceptance of Information Technologies for Healthcare

Systems and Applications

• Open-Source Software for Healthcare

Connected Health: Challenges

© 2015 Abbes Amira

• Assistive and Adaptive ubiquitous Computing

• Technologies for Healthcare

• Computer Games and Augmented reality for Healthcare

• Telemedicine

• E-Learning for Healthcare, Health Education

• Informatics and Quality of Care

• Applications of informatics and analytics tools in nutrition

Connected Health: Challenges

© 2015 Abbes Amira

• Not practical for daily use

• Slow alerting system lacking intelligence

• Expensive to implement

• Complexity of the system

• Flexibility

• Privacy

Connected Health: Challenges

Limitations of Existing Solutions

© 2015 Abbes Amira

Emerging Technologies:

Platforms and Tools

© 2015 Abbes Amira

“Spartan-6, Virtex-6, and 7 series FPGAs offer the performance of

dedicated ASICs and DSPs, with the added benefits of low NRE

cost, substantially reduced time to market, easy design portability,

and high I/O count with simplified PCB layout. In addition, Xilinx's

40 nm and upcoming 28 nm FPGA custom low power process,

coupled with leading edge power optimization tools offer

significantly lower power consumption than competing solutions.

All of these benefits enable portable ultrasound system

developers to improve patient care by rapidly deploying

systems that deliver the latest technology within budget and

power consumption constraints.”

www.xilinx.com

Xilinx FPGAs in Portable

Ultrasound Systems

Emerging Technologies

© 2015 Abbes Amira

Excuse me for a

minute while I

reconfigure my

computer

Emerging Technologies

Reconfigurable Computing

© 2015 Abbes Amira

** ASIC’s, CPU’s, and DSP’s

• Time- to- market

• Performance

• Power

• Size/ weight

• Flexibility

• Life cycle cost

Using reconfigurable electronics to build connected health

systems with advantages over conventional technology **

in any of the areas of:

Emerging Technologies

© 2015 Abbes Amira

• Logic ports are connected to realize the right logic function

• Low delay (ns) input assertion and output generation

• High flexibility

• Customizable hardware

• X and Y are estimated at the same time

• Two different locations and hardware resources inside FPGA are used to generate X and Y

• Hardware structure is pre-defined

• An instruction per clock cycle

• Hardware is shared for generating out_1 and out_2

• High delay for output estimation

Memory

ALU I/O …

FPGA Microprocessors

Emerging Technologies

© 2015 Abbes Amira

FPGA Architectures

Emerging Technologies

© 2015 Abbes Amira

All Xilinx FPGAs contain the same basic resources

Logic Resources

• Slices (grouped into CLBs)

• Contain combinatorial logic and register resources

• Memory

• Multipliers

Interconnect Resources

• IOBs

Interface between the FPGA and the outside world

Other resources

• Global clock buffers, RISC ARM processors…etc

Emerging Technologies

© 2015 Abbes Amira

© 2015 Abbes Amira

Zynq platform architecture

Emerging Technologies

© 2015 Abbes Amira

RC10 XUPV5-LX110T

Virtex-7 FPGA VC707

Emerging Technologies

© 2015 Abbes Amira

Zynq SoC prototyping board

Emerging Technologies

Zynq board for video processing

© 2015 Abbes Amira

Xilinx FPGAs

1

2

3

4

Software tools 4

3

1

2

Zynq

Emerging Technologies

© 2015 Abbes Amira

1

2

3

4

Software tools4

3

1

2

Xilinx FPGAs

Emerging Technologies

© 2015 Abbes Amira

• HDL (VHDL, Verilog)

• High Level Languages

• (Handel-C, SystemC, CatapultC, JHDL…etc)

• Schematic

• MATLAB-Simulink- Xilinx System Generator (XSG)

• AccelDSP (MATLAB)

• VIVADO HLS (AutoESL)

Emerging Technologies

© 2015 Abbes Amira

Compile

....00100110....

configure

Place & Route

Generate

+

Schematic

or HDL

Hardware Compilation

Handel-C Simulate

EDIF1

EDIF2

Emerging Technologies

© 2015 Abbes Amira

Emerging Technologies

New approach using Vivado HLS

© 2015 Abbes Amira

RCMAT: A Coprocessor for matrix

algorithms

Emerging Technologies

© 2015 Abbes Amira

Array Blocks Routines

Platform

Matrix multiplication

Matrix inversion

DSP, IP

Platform

Matrix transforms:

DCT, DWT, FFT

AP

Platform

Matrix decomposition:

SVD, QR, LU

Emerging Technologies

© 2015 Abbes Amira

DriverRoutines

Singular Value Decomposition

Finding EigenvectorsSolving Linear Equation

Matrix Invertion

Computational

Routines

LU, QR Factorisation

Bidiagonalisation, Jacobi Rotation

Array Block

RoutinesMatrix Multiplication

Matrix Addition

Design

Schematic, HDL, FSM

Systolic “Conv Arith”

Distributed-

Arithmetic

Implementation

CompilerTemplates

Hardware

FPGA, VLSI

Software

Libraries

ProgrammingLanguage

Level Examples

Real

World

Applications

Image processing

Kalman Filtering

ECG Monitoring

Fall Detection

VIVADO

HLS

JHDL

Handle C

Emerging Technologies

© 2015 Abbes Amira

Source: University of Denver Source: Ecole Polytechnique, Montreal

Emerging Technologies

Reconfigurable Computing

© 2015 Abbes Amira

Smartphone Point of care device

Source: University of Ulster, United Kingdom

Emerging Technologies

© 2015 Abbes Amira

Shimmer is a small wireless sensor platform that can

record and transmit physiological and kinematic data

in real-time. Designed as a wearable sensor, Shimmer

incorporates wireless ECG (Electrocardiogram), EMG

(Electromyography), GSR (Galvanic Skin Response-

skin conductance), Accelerometer,…

Source: http://www.shimmer-research.com/

Shimmer Platform

Emerging Technologies

© 2015 Abbes Amira

CPU

• Low-power TI MSP430F1611 microprocessor which

controls the operation of the device

• It configures and controls various integrated peripherals

through I/O pins, some of which are available on the

internal/external-expansion connectors

Emerging Technologies

© 2015 Abbes Amira

Computing Platform

• Run appropriate software

• Analyze the recorded data

• Analyze a person’s activities

• Intelligent alerting system

Emerging Technologies

© 2015 Abbes Amira

Emerging Technologies

© 2015 Abbes Amira

Accelerometer Walk Data

0.00E+00

5.00E+00

1.00E+01

1.50E+01

2.00E+01

2.50E+01

3.00E+01

3.50E+01

1 153 305 457 609 761 913 1065 1217 1369 1521 1673 1825 1977 2129 2281 2433 2585 2737 2889 3041 3193 3345 3497

Sample Number @ 100sps

mg

X-Axis

Y-Axis

Z-Axis

Resultant waveforms from a walking motion for

the 3-axis accelerometer

Walking

Emerging Technologies

© 2015 Abbes Amira

Running

Accelerometer Run Data

0.00E+00

5.00E+00

1.00E+01

1.50E+01

2.00E+01

2.50E+01

3.00E+01

3.50E+01

4.00E+01

1 85 169 253 337 421 505 589 673 757 841 925 1009 1093 1177 1261 1345 1429 1513 1597 1681 1765 1849 1933

Sample Number @ 100 sps

mg

X-Axis

Y-Axis

Z-Axis

Resultant waveforms from a running motion for the 3-axis accelerometer

Emerging Technologies

© 2015 Abbes Amira

Jumping

Accelerometer Jump Data

0.00E+00

1.00E+01

2.00E+01

3.00E+01

4.00E+01

5.00E+01

6.00E+01

7.00E+01

1 777 1553 2329 3105 3881 4657 5433 6209 6985 7761 8537 9313 10089 10865 11641 12417 13193 13969 14745

Sample Number @ 100sps

mg

X-Axis

Y-Axis

Z-Axis

Resultant waveforms from a jumping motion

Emerging Technologies

© 2015 Abbes Amira

Vitalsens Device

ecg

respiration rate

temperature

SpO2

accelerometer

Vital signs measured can include:

• Simple heartbeat to full ECG

• Blood Oxygen

• Respiration

• Skin surface temperature

• Motion Detection

Intelligent, wearable, non invasive, wireless vital signs monitor

Source: http://www.intelesens.com/

Emerging Technologies

© 2015 Abbes Amira

• A reusable person-locating device

which is worn on the wrist, identifies

the wearer, is capable of locating the

wearer through GPS and

communicates their position and

status using GPRS communication

• Bio-sensing device which measures

human iso-potential signals and

derives a unique bio-measurement

from those electrical signals. This

has the potential to uniquely identify

the user of any piece of equipment

• Uses for this type of device include monitoring the

whereabouts of a person and for the tracking of patient in

hospitals

Emerging Technologies

© 2015 Abbes Amira

Emerging Technologies

Computing Platform

© 2015 Abbes Amira

Smart Connected Health Solutions

•Fall detection

•ECG monitoring

•Medical Imaging

•Other applications

© 2015 Abbes Amira

Falling and Importance of Monitoring

• Individuals at risk:

• Elderly

• Medical conditions

• Injuries caused:

• Broken bones

• Head trauma

• Individuals often unable to call for help

• Fall detection device can quickly raise alarm

Fall Detection

© 2015 Abbes Amira

Fall Detection Technology

• Visual camera-based

– High accuracy and high cost

• Wearable accelerometer-based

– Low accuracy and low cost

• We aim to improve upon wearable systems robustness

and accuracy for fall detection

Wearable Sensor Visual Camera

Fall Detection

© 2015 Abbes Amira

Proposed System

• Shimmer accelerometer device utilised

• Signal analysed with time, wavelet and classifier techniques

• Fall occurrence, strength and direction detected

Fall Detection

© 2015 Abbes Amira

Fall Data and Methodology

• 36 subjects

• 2 groups; wavelet and Principle Component

Analysis (PCA)

• Shimmer worn on centre chest position

• Activities of Daily Life (ADL) recorded

• Hard and soft directional falls recorded

Strong Front Fall Soft Front Fall

Fall Detection

© 2015 Abbes Amira

Fall Data and Methodology

• Subjects were split into two groups for independent analysis

and comparison of multiresolution and PCA fall detection and

diagnostics

• 25 subjects and their associated fall and ADL occurrences over

3 repetitions were combined to form group 1 for multiresolution,

threshold-based, analysis

• Group 2 consists of 11 subjects,139 falls and 84 ADL samples

which were used for PCA + decision tree classifier

• The fall data obtained from the Shimmer device was wirelessly

recorded and analyzed in LabView and Matlab

Fall Detection

© 2015 Abbes Amira

Multiresolution Wavelet Analysis

Group 1

• Biorthogonal (5-5) L2 wavelet applied

• Thresholds applied to categorise signal data:

– Time domain

• Fall occurrence; optimum threshold = maximum ADL

acceleration

– Wavelet domain

• Fall occurrence; optimum threshold = minimum average fall

acceleration

• Fall strength; threshold = maximum soft fall acceleration

– Wavelet and time signals compared for fall detection

• Logical AND or OR operators

Fall Detection

© 2015 Abbes Amira

PCA Classifier

Group 2

• All wavelet acceleration data evaluated by PCA

• Fall detection classifier tree obtained from PCA

• Wavelet acceleration signals applied to Boolean

classifier

• Classifier determines fall occurrence, strength and

direction

– Classifier can be calibrated with ADL data

Fall Detection

© 2015 Abbes Amira

Analysis Metrics• Recall (φ)

– Measure of falls detected from fall data

– Based on number of true falls detected (𝑇𝑝) and falls not detected

(𝐹𝑛)

φ=𝑇𝑝

𝑇𝑝 + 𝐹𝑛

• Precision (ψ)

– Measure of true falls detected, not ADL

– Based on number of true falls detected (𝑇𝑝) and ADLs detected as

falls (𝐹𝑝)

ψ=𝑇𝑝

𝑇𝑝 + 𝐹𝑝

• F-value (𝐹)

– Accuracy measure based on harmonic mean of recall and precision

𝐹=2φψ

φ+ψ63

Fall Detection

© 2015 Abbes Amira

Fall Detection Results

• AND comparator

• OR comparator

Fall Analysis Recall (%) Precision (%) F-Value (%)

Fall Detection

(AND Comparator)

83 94 88

Fall Detection

(OR Comparator)

98 88 93

Fall Strength 76 73 75

• Multiresolution fall detection

• Strict operator • High precision and low recall

• Flexible operator • High recall and low precision

Fall Detection

© 2015 Abbes Amira

• Additional ADL data used to calibrate classifier

• Calibration can improve precision

Fall Analysis Recall (%) Precision (%) F-Value (%)

Fall Detection

(Uncalibrated)

91 90 88

Fall Detection

(Calibrated)

87 92 87

Fall Strength 85 77 80

Fall Direction 81 83 87

• PCA classifier fall detection

Fall Detection

Fall Detection Results

© 2015 Abbes Amira

• Multiresolution Analysis

– Fall detection

• Can obtain high precision or high recall results only

• Comparator operators produce strict or flexible fall

detection

– Poor fall strength categorisation

• PCA classification tree

– Fall detection

• Good precision and good recall

• Can be calibrated to improve response with good data

selection

– Good fall strength categorisation

– Can obtain fall direction information

Fall Detection

© 2015 Abbes Amira

Automatic fall detection and Hardware Acceleration

Fall Detection

© 2015 Abbes Amira

y

x

z

Signal

reconstruction

using OMP

Orientation

and

acceleration

estimation

module

Fall

detection

decision

module

Fall detection

response

Sparse 3D

Accelerometer

Signal

Orientation

Acceleration

Compressive Sensing

Fall Detection

© 2015 Abbes Amira

Compressive sensing

• Activity of Daily Living (ADL) training data

• Detects fall occurrence, strength and direction

Fall Detection

© 2015 Abbes Amira

Compressive sensing

• Allows signal recovery from sparse data samples

– Less acceleration data sampled

– Less data transmitted

– Less power requirements

• Wavelet acceleration signal

– Thresholded; 45 largest nonzero coefficients

– Pre-known permutation index

– Gaussian sensing matrix

Fall Detection

© 2015 Abbes Amira

Compressive sensing• Sparse signal recovery methods

– Matching Pursuit (MP) – 1993

» High iterations, low complexity

– Orthogonal MP (OMP) – 2007

» Low iterations, high complexity

– Regularised OMP (ROMP) – 2009

» Multiple magnitude-based column processing

– Stagewise OMP (StOMP) – 2012

» Multiple column, fixed iterations, high sparsity suited

Fall Detection

© 2015 Abbes Amira

Fall Data and Methodology

• 11 subjects

• 110 ADL samples recorded

• 140 hard and soft directional falls recorded

Strong Front Fall Soft Front Fall

Fall Detection

© 2015 Abbes Amira

Classifier Fall Detection Results

• Good rate of falls detected

• Accurately distinguish falls from ADL

• Additional data can be used to further calibrate

classifier

• Calibration can improve results

Fall Analysis Recall (%) Precision (%) F-Value (%)

Fall Detection 96 99 97

Fall Type 91 99 95

Fall Direction 87 98 93

Fall Detection

© 2015 Abbes Amira

Compressive Sensing Sparsity Effect

• Sparsity effect on sparse signal reconstruction

• Fall detection:

Recall Precision F-value

• 45 nonzero coefficients

• Sparsity decrease = response decrease

Fall Detection

© 2015 Abbes Amira

H. Rabah, A. Amira, B.K. Mhanti, S. Almadeed, P.K. Meher “FPGA implementation of orthogonal matching

pursuit algorithm for compressive sensing reconstruction”, IEEE Transactions on VLSI, 2015 (available

online)

• Reconstruction failed with non-sparse signals (0.5 sparsity)

• StOMP and OMP obtained better responses

• Precision response maintained longest

Fall Detection

Compressive Sensing Sparsity Effect

R.M.Gibson, A. Amira, P. Casaseca, N. Ramzan and Z. Pervez “An Efficient User-Customisable

Multiresolution Classifier Fall Detection and Diagnostic System” The 26th International Conference on

Microelectronics (ICM), 14-17 December 2014, Doha, Qatar.

H.Rabah, A. Amira, and A.Ahmad “Design and Implementation of a Fall Detection System using

Compressive Sensing and Shimmer Technology” the 24th IEEE Conference on Microelectronics ICM2012,

Algiers, 17-20 December 2012.

A. Amira, N. Ramzan, C. Grecos, Q. Wang, P. Casaseca, Z. Pervez, X. Wang and C. Luo “A Reconfigurable

Supporting Connected Health Environment for People with Chronic diseases” book chapter (Chapter 17-

page 332) in Healthcare Informatics and Analytics: Emerging Issues and Trends" IGI Global 2014.

© 2015 Abbes Amira

A Wireless Reconfigurable

System for Falls Detection

© 2015 Abbes Amira

• Developing an efficient hardware architecture for 3D

accelerometer based fall detection approach

• Developing a novel fall detection algorithm with the best

effectiveness and complexity trade-off

• Using FPGAs as a low cost accelerator for falls detection

• Developing a real-time monitoring system for fall

detection using 3 axial accelerometer data

A Wireless Reconfigurable System for Falls Detection

Fall Detection

© 2015 Abbes Amira

A Wireless Reconfigurable System for Falls Detection

Fall Detection

© 2015 Abbes Amira

System block diagram Architecture

A Wireless Reconfigurable System for Falls Detection

Fall Detection

© 2015 Abbes Amira

Task Software FPGA Speed

up

Orientation estimation 2052ns 12.2ns 168

Acceleration estimation 2388ns 12.2ns 195

Orientation & Acceleration estimation 3466ns 12.2ns 284

Task execution times and speedup

A Wireless Reconfigurable System for Falls Detection

Fall Detection

M. Neggazi, A.Amira and L. Hammami “Efficient Compressive Sensing on the Shimmer Platform for Fall

Detection”, the IEEE International Symposium on Circuits and Systems (ISCAS2014), 1-5 June 2014,

Melbourne, Australia.

M. Neggazi, A. Amira, L. Hammami “A Wireless Reconfigurable System for Falls Detection” the 11th

International Conference on Information Sciences, Signal Processing and their applications (ISSPA2012),

2-5 July 2012, Montreal, Canada.

© 2015 Abbes Amira

A. Ait Si Ali, M. Siupik, A. Amira, F. Bensaali and P. Casaseca, “HLS Based Hardware Acceleration on the

Zynq SoC: a Case Study for Fall Detection System,” The 11th ACM/IEEE International Conference on

Computer Systems and Applications, 10-13 November 2014, Doha, Qatar.

Maximum running Frequency: 300 MHz

PCA and Decision Tree Classifier

Fall Detection

© 2015 Abbes Amira

ECG Encryption using

VitalSens Technology

© 2015 Abbes Amira

To develop a concept demonstrator

for both wireless health-care monitoring and

individual identification using ECG,

with the aspects of

data storage and data security

ECG Encryption using Advanced Encryption

Standard (AES)

ECG Encryption

© 2015 Abbes Amira

VitalSens VS100

+LM058 Bluetooth-Serial

adapter

Bluetooth

Flash memory holds data

VGA

VGA Screen

Flash memory holds AES

encrypted data

RC10 Board

ECG Encryption

© 2015 Abbes Amira

Expandability Scalability

Reliability

ECG Encryption

© 2015 Abbes Amira

Data from / toBluetooth-to-Serial Adapter

Output Datato Screen

Data Distribution Module

AES Encoder

Bluetooth Communication & Acquistion Module

Serial Interface Module

VitalSensProtocol Stack

LM058 Library

VGA Screen Displaying

People Recognition

Finite State Machinefor Communication

Store Data in Memory

ECG Encryption

© 2015 Abbes Amira

RC10 Board with LM058 Bluetooth-Serial adapter

VitalSens VS100

FPGA stores cipher-text with ECG data in

flash memory

Link option 1

Bluetooth Communication

& Acquistion

Data Distribution

ModuleAES Cipher

1

ECG Encryption

© 2015 Abbes Amira

RC10 board

VGA link

VGA screen

FPGA restores ECG data from

ciphertext

AES Decipher

VGA Displaying

Module2

ECG Encryption

A.Amira, M.Saghir, N.Ramzan, C. Grecos and F. Scherb “A Reconfigurable Wireless Environment for ECG

Monitoring and Encryption”, International Journal of Embedded and Real-time Communication systems,

Special issue on: Networked Embedded Systems- Design for Scalability and Heterogeneity. Volume 4,

Issue 3, 2013.

© 2015 Abbes Amira

ECG Encryption using AES Demo

ECG Encryption

© 2015 Abbes Amira

ECG Recognition using

VitalSens Technology

© 2015 Abbes Amira

• To develop a real time embedded system for people

identification using ECG signal

• To perform and evaluate a Matlab Simulation for the

identification process using Principle Component

Analysis (PCA) and different ECG sources

• To perform the ECG identification process on the

FPGA using Handel-C and Matlab simulation results

ECG Recognition

© 2015 Abbes Amira

Pre-processing

Feautre Vector

in Database

Classification

Verification

Access Denied

NO

Access Granted

YES

EnrolmentIdentification

ECG Sensor ECG Sensor

Feature

Extraction

Pre-processing

Feature

Extraction

ECG Recognition

© 2015 Abbes Amira

RC10 Board with bluetooth adaptorECG Sensor on human

chest

+

Database on

Flashmemory

Person ID on

7 Segment

Display

Identification

Algorithm on

FPGA

ECG Encryption

© 2015 Abbes Amira

FilteringDWT

CompletePCA

Create FlashData

User interface

CompletePCA

Load MIT-BIH ECG Data

Load VS ECG Data

MIT-BIH onlineECG Database

Recognition

ECG Recognition

© 2015 Abbes Amira

RAM

ECG

sampling

Module

Computing

PCA Values

Module

Load

Matlab Data

from Flash

Module

Euclidian

Distance

Module

Display ID

Module

Bluetooth

and Data

Distribution

Module

VS100 + LM058

16 MB Flash Memory

Seven Segment Display

FPGA

ECG Recognition

P. Zicari, A. Amira, G.Fischer, J. Mclaughlin “An Embedded System for on Field Testing of Human

Identification Using ECG Biometric” the 11th International Conference on Information Sciences, Signal

Processing and their applications (ISSPA2012), 2-5 July 2012, Montreal, Canada.

© 2015 Abbes Amira

ECG Recognition Demo

ECG Recognition

© 2015 Abbes Amira

• Developing intelligent segmentation, filtering and

compression systems for 2D and 3D medical imaging

• Tumour definition for radiation therapy planning and

cancer diagnosis

• Efficient low power architectures for 3D medical

imaging

Medical image segmentation

© 2015 Abbes Amira

A. Amira, S. Chandrasekaran, D. Montgomery and I.S.Uzun “A Segmentation Concept for Positron

Emission Tomography Imaging Using Multiresolution Analysis” Neurocomputing, Special Issue on

Vision Research, Volume 71, Issues 10-12, pp 1954-1965, June 2008

D.Montgomery, A. Amira and H.Zaidi “Oncological PET Volume Segmentation Using a Combined

Multiscale and Statistical Model” Medical Physics (The American Association). 34 (2), February 2007.

Medical image segmentation

© 2015 Abbes Amira

Medical image segmentation

© 2015 Abbes Amira

• Input: is decomposed into a • Low-pass subband

• High-pass subband

• Where are low-pass and high-pass filter coefficient

• Haar filter:

• 9/7 Biorthogonal spline filter:

},...,,{ 110 Nxxxx

},...,,{ 12/10 Naaaa

ii gh ,

k

kknn xha 2

)5.0,5.0(

)5.0,5.0(

g

h

},...,,{ 12/10 Ncccc

k

kknn xgc 2

),,,,,,(

),,,,,,,,(

4321012

432101234

gggggggg

hhhhhhhhhh

Algorithms

Medical image segmentation

© 2015 Abbes Amira

Haar Wavelet Transform

A = [6, 4, 5, 9, 7, 5, 9, 3]

D1 = [5, 7, 6, 6, 1, -2, 1, 3]averages differences

D2 = [6, 6, -1, 0, 1, -2, 1, 3]averages

differences

Standard 2D Decomposition

Decompose all Rows

Decompose all Columns

Non-Standard 2D Decomposition

Decompose on a Row-Column,

Row-Column cycle

Medical image segmentation

© 2015 Abbes Amira

DataProcessed

Data

Software Hardware

GUI- Host Application

Flash

MemoryFPGA

FPGA Board

Functions

Read ();

Haar ();

Write ();

Confirm ();

Configure ();

Host-FPGA Communication

Medical image segmentation

© 2015 Abbes Amira

Haar

Coefficients

Inputs

HWTF (N=8) based on distributed principles

ROM

+

-

+/-

+/-

x1x2

x5x6

x3x4

x7x8

+/-

SR2-1

Y

Medical image segmentation

© 2015 Abbes Amira

• PINLab chest/heart phantom

Four acquisitions with the inclusion of varying tumour inserts

Real PET data

Data set dimensionalities 128 x 128 x up to 117

Medical image segmentation

© 2015 Abbes Amira

(a) Original Phantom Image, (b) Thresholded Image T= 9000, (c) MRA Level 1,

(d) Reconstructed Image

Phantom Data

Medical image segmentation

© 2015 Abbes Amira

• The HWT hardware implementation based on pseudo code

using Handel-C requires 259 FPGA slices and operates

at a maximum frequency of about 67 MHz

• Parametrisable, scalable 16 bit 8x8 2-D has been used for

medical volume segmentation

• The hardware implementation of HWT clearly outperforms

the software implementation by a speedup factor of 5.5

times

Medical image segmentation

A.N. Sazish, Mhd S.Sharif and A. Amira “Hardware Implementation and Power Analysis of HWT for Medical

Imaging” The 16th IEEE International Conference on Electronics, Circuits and Systems (ICECS 2009),

Hammamet, Tunisia, 13-16 December, 2009.

Mhd Sharif and A. Amira “An Intelligent System for PET Tumour Detection and Quantification” The IEEE

Conference on Image Processing (ICIP 2009), Cairo, 07-11 Nov 2009.

Mhd.Sharif, M. Abbod, A. Amira and H. Zaidi “Artificial Neural Network Based System for PET Volume

Segmentation” International Journal on Biomedical Imaging, Volume 2010, Article ID 105610, 2010.

© 2015 Abbes Amira

• Two architectures for 1-D DBWT

• Fully pipelined DBWT architecture,

• Hybrid DBWT architecture

• Two architectures for 2-D DBWT

• Separable 2-D DBWT architecture

• Non-separable 2-D DBWT architecture

• FPGA-based HDTV image/video compression

applications

Discrete Biorthogonal Wavelet Transform

Medical image segmentation

I.S.Uzun and A.Amira “A Framework for FPGA based Discrete Biorthogonal Wavelet Transforms

Implementation” Vision, Image and Signal Processing, IEE Proceedings -Special Issue on Rapid

Prototyping Systems- Volume 153, Issue 6, Page(s):721 - 734, Dec. 2006.

© 2015 Abbes Amira

1-D DBWT Pipelined Arch

PE1

PE2

PE3

PEK

x(n)

N0

N0/2

a1(n)

N0/4

a2(n)

N0/2K-1

aK-1(n)

d1(n)

d2(n)

N0/8

a3(n)

d3(n)

dK(n)

aK(n)

KK2

LM

2

LM1

4

LM 2

8

LM 3

• Arch-I consists of K PEs, each PE is devoted to compute

decomposition level k where 1≤k≤K.

• Because of decimation by two in DWT, each PE basically

has floor(L/2k) MACs. L is the filter length.

Medical image segmentation

© 2015 Abbes Amira

1-D DBWT Hybrid Arch

PE1 PE

2

RPA-

Scheduling

x(n)

N0

N0/2

a1(n)

N0/2K-1

aK(n)

d1(n)

d2(n),d3(n),..,dK(n)

2

LM1

L2M 2

The proposed architecture consists of 2 PEs

• PE1 computes the first level of decomposition

• PE2 computes the higher levels (k) of the

decomposition, where 2≤k≤K

Medical image segmentation

© 2015 Abbes Amira

2-D DBWT Separable Arch

• The separable 2-D DBWT architecture consists of a delay

line, a filter bank and a memory unit of J register blocks

(Rj) in order to store intermediate outputs.

Medical image segmentation

© 2015 Abbes Amira

2-D DBWT Non-Separable Arch

R1ODD

NP

0N

N N

N/2 N/2

N/2 N/2

N/4 N/4

N/4 N/4

S0

DEMUXEVEN

0

1

2

0 1 2

Input Image

(Even Rows)

[I] 2m

([I]2m

+[I]2m-8

)

([I]2m-2

+[I]2m-6

)

[I]2m-4

([LL1]2m

+[LL1]2m-8

)

( [LL2]2m

+[LL2]2m-8

)

([LL1]2m-2

+[LL1]2m-6

)

([LL2]2m-2

+[LL2]2m-6

)

[LL1]2m-4

[LL2]2m-4

[LL3]2m

LL{0,8}

/ HL{0,8}

LH{0,8}

/ HH{0,8}

LL{2,6}

/ HL{2,6}

LH{2,6}

/ HH{2,6}

LL4 / HL

4

VSER1EVEN VSER2

EVENVSER3

EVEN

Delay Line and

Symmetrical

Extention Router

(SER) Network

P2

S0

0

1

2

LH4

/ HH4

P4

S0

0

1

2

NP

1N

N

N/2 N/2

N/2

N/4 N/4

N/4

S0

DEMUXEVEN

0

1

2

0 1 2

Input Image

(Odd Rows)

([I]2m

+[I]2m-8

)

([I]2m-2

+[I]2m-6

)

([LL1]2m

+[LL1]2m-8

)

( [LL2]2m

+[LL2]2m-8

)

([LL1]2m-2

+[LL1]2m-6

)

([LL2]2m-2

+[LL2]2m-6

)

[LL3]2m+1

LL{1,7}

/ HL{1,7}

LH{1,7}

/ HH{1,7}

LL{3,5}

/ HL{3,5}

LH{3,5}

/ HH{3,5}

VSER1ODD VSER2

ODDVSER3

ODD

Delay Line and

Symmetrical

Extention Router

(SER) Network

P3

S0

0

1

2

+

S1

S2

LH1,2,3 /HH1,2,3

HL1,2,3

LL/HL

0

1

1

0

Row

Adder

LH{0,8}

/ HH{0,8}

LH{2,6}

/ HH{2,6}

LH4 / HH

4

LH{1,7}

/ HH{1,7}

LH{3,5}

/ HH{3,5}

+

Row

Adder

LL{0,8}

/ HL{0,8}

LL{2,6}

/ HL{2,6}

LL4

/ HL4

LL{1,7}

/ HL{1,7}

LL{3,5}

/ HL{3,5}

Qr[]

Qr[]

[I] 2m+1

R1EVEN

R2EVEN

R3EVEN

R2ODD

R3ODD

VSERjODD

: Vertical Symmetric Extension Router for ODD-

numbered of input data at jth decomposition level.

VSERjEVEN

: Vertical Symmetric Extension Router for EVEN-

numbered of input data at jth decomposition level.

RjODD/EVEN

: Row-delay circuit for jth decompsition level.

N : Row delay elements composed of N delay units.

Qr[] : Quantisation applied to subband outputs.

Medical image segmentation

© 2015 Abbes Amira

DBWT Results

Medical image segmentation

• Computation time for 1-D DBWT architectures is N0/2,

therefore it is at least twice faster than existing

architectures

• Very high data-throughput rates up to 320

MegaSamples/sec, with efficient hardware utilization

• The separable architecture requires less number of

multipliers compared to the non-separable architecture;

• The routing complexity of non-separable architecture is

less than separable architecture, it achieves better

maximum operating frequency; and

• The non-separable architecture requires less number of

ccs to compute the DBWT (2N2/3 versus 2N2)) and

achieves higher fmax, therefore it outperforms the

separable architecture in terms of computation time

© 2015 Abbes Amira

Medical image segmentation

© 2015 Abbes Amira

FPGA-based Compression

A single level of 9,7 filter

bank implementation

requires 462 slices (less

than %3 of slices

available on the FPGA

device) for a input data

throughput of 320

MegaSamples/sec

I.S.Uzun and A.Amira “Real-Time 2-D Wavelet Transform Implementation for HDTV Compression” Real-

Time Imaging, Special Issue on Spectral Imaging II, Volume 11, Issue 2, Pages 151-165, April 2005.

© 2015 Abbes Amira

3D Medical image compression

A. Ahmad, B. Krill, A. Amira and H. Rabah, “Efficient Architectures for 3-D HWT using Dynamic Partial

Reconfiguration”, Journal of Systems Architecture - Special Issue on Hardware/Software Co-Design,

Issue 8, Volume 56, pp 305-316, August 2010.

© 2015 Abbes Amira

A. Ahmad, B. Krill, A. Amira and H. Rabah, “Efficient Architectures for 3-D HWT using Dynamic Partial

Reconfiguration”, Journal of Systems Architecture - Special Issue on Hardware/Software Co-Design, Issue

8, Volume 56, pp 305-316, August 2010.

3D Medical image compression

© 2015 Abbes AmiraOriginal 3-D image Compressed 3-D image

1D HWT T2 1D HWT1D HWT T1

Subimages (S0 – S7)

3D HWT coefficients

A

A

A

A

D

D

D

D

A

A

D

D

A

D

i(0)

i(1)

i(2)

i(N-1) o(N-1)

2 2 1

2

i ii

a aH

20.. 1Ni

22 2 1N i ii

H a a

20.. 1Ni

A

D

o(0)

...

N in

pu

ts s

am

ple

, N

=8

o(1)

o(2)

...

(a)

(b)

(c)

(d)

(e)

1D HWT 1D HWT 1D HWTT1 T2

Data fetch unit

Block RAMs

2

3

1

4Send data operation for transposition operation

Read data operation from memory

FPGA – Static area

Write the results to different

memory location

Send data operation to be proposed in 1D HWT

1D HWT Transpose

Data fetch unit

Block RAMs

Read data operation from

memory

Send data operation

for transposition operation Send data

operation to be proposed in 1D HWT

Write the results to different

memory location

2

3

4

1

Static area

Reconfigurable area

Reconfigurable area

FPGA

Xilinx University Program XUPV5 LX110T Development System

3D Medical image compression

© 2015 Abbes Amira

3D Medical image compression

© 2015 Abbes Amira

A. Amira and S. Chandrasekaran “Power Modelling and Efficient FPGA Implementation of FHT for Signal

Processing” IEEE Transactions on Very Large Scale Integration TVLSI, Vol 15 Number 3, pp 286-295,

March 2007.

Fast Hadamard Transform

04

7,8 iH

4

5,8 iH

4

7,8

4

5,8 ii HH

4

3,8

4

1,8 ii HH

4

7,8

4

3,8

4

1,8 iii HHH

4

5,8

4

3,8

4

1,8 iii HHH

4

7,8

4

5,8

4

3,8

4

1,8 iiii HHHH

04

8,8 iH

4

6,8 iH

4

8,8

4

6,8 ii HH

4

4,8

4

2,8 ii HH

4

8,8

4

4,8

4

2,8 iii HHH

4

6,8

4

4,8

4

2,8 iii HHH

4

8,8

4

6,8

4

4,8

4

2,8 iiii HHHH

mXX )( 21

4x16

Dec

oder

mXX )( 87

mXX )( 65

mXX )( 43

ROM's content for

i = 1,3,5,7

ROM's content for

i = 2,4,6,8

FF

SR

CI

RESULT

SHIFTACC

INVERTERINVER

T

TH

XHH

XHY

N

NN

N

4

24

© 2015 Abbes Amira

Algorithm performance

Implementation performance

Feature Proposed

Structure

Structure

[Existing]

Structure

[Existing]

Computation

time

2n (2N-1)(n+log2N) (2nN)T

Design Features

(N=8, n=8)

Slices Speed

(MHz)

(Area/Speed) Ratio

Proposed

Architecture

124 90 1.37

Existing 136 45 3.02

Fast Hadamard Transform

© 2015 Abbes Amira

FPGA-based Compression

H. Rabah, A. Amira, B.K. Mohanti, S. Almadeed, P.K. Meher “FPGA implementation of orthogonal matching

pursuit algorithm for compressive sensing reconstruction”, IEEE Transaction on VLSI, 2015.

© 2015 Abbes Amira

F.Bensaali and A.Amira “Accelerating Color Space Conversion on Reconfigurable Hardware” Image and

Vision Computing, Vol 23, pp 935-942 (2005).

Color Space Conversation

© 2015 Abbes Amira

• Efficient architectures for

• the á trous wavelet transform

• Finite Radon transform

• Finite Ridgelet transform

• Building blocks of the Curvelet

transform and their FPGA

implementation

Spatial Domain Lena FRIT domain, p = 7

Spatial Domain Baboon FRIT domain, p = 31

S. Chandrasekaran, A. Amira A. Bermak and M. Shi “An Efficient VLSI Architecture and FPGA

Implementation of the Finite Ridglet Transform” Journal of Real-Time Image Processing, Vol 3, NO 3, pp

183-193, September 2008.

Curvelet Transform

© 2015 Abbes Amira

a’trous Wavelet Transform

Curvelet Transform

© 2015 Abbes Amira

Radon Transform

Curvelet Transform

© 2015 Abbes Amira

• Ridglet transform is precisely the application of a 1-

dimensional wavelet transform to the slices of the

Radon transform

Curvelet Transform

© 2015 Abbes Amira

Curvelet Transform

© 2015 Abbes Amira

• Implementations have been carried out fordifferent input image size where N=7,17 and 31for FRIT

• The Curvelet design has been implemented for atransform size of 289x289 and p = 17

• With 83MHz operating frequency and 3N2 + 3p3Nclock cycles computation time, the Curvelettransform with 3 resolution levels takesapproximately 57ms

• That gives a speed increase factor of 20compared to existing work

Curvelet Transform

© 2015 Abbes Amira

J.S.S.Kutty, F.Boussaid, A.Amira “A High Speed Configurable FPGA Architecture For Bilateral Filtering”

The International Conference on Image Processing (ICIP), 27-30 October 2014, Paris, France.

Image filtering

© 2015 Abbes Amira

Image filtering using FFT

I.S.Uzun and A.Amira “An FPGA-based Parametrisable System for High-Resolution Frequency Domain

Image Filtering” Journal of Circuits, Systems and Computers, Vol. 14, No. 5, pp 895-921 (2005).

I.S. Uzun, A. Amira, A. Bouridane “FPGA Implementations of Fast Fourier Transforms for Real- Time

Signal and Image Processing” IEE Proceedings on Vision, Image and Signal Processing, Volume 152,

Number 03, pp 283-296 - June 2005.

- Input Image

- Filter Coeffs.- Output Image

Point-to-Point

Multiplication

Virtex-2000E

FPGAParallel

2-D Forward FFT - Radix-2

- Radix-4

- Split-Radix

- FHT

Parallel

2-D Inverse FFT - Radix-2

- Radix-4

- Split-Radix

- FHT

RC1000 Dev. Board

SRAM

Bank 0

SRAM

Bank 1

Input/Output

Image

SRAM

Bank 2

SRAM

Bank 3

Filter

Coeffs.

Design Paremeters

- FFT Type

- Number of PEs

- Filter Type

*Filter params.

• Radix-2 is area efficient

• Radix-4 is best in terms ofcomputation time

• FHT has real kernel. It requires lessmemory

• Split-Radix is suitable for completepipelined architecture which is notsuitable for processing large FFTs

• Number of processors hassignificant effect on performance ofparallel 2-D FFT implementation

• Real-time 2-D FFT performance formatrix size: 128,256,512

• Near real-time 2-D FFT performancefor matrix size N =1024

© 2015 Abbes Amira

P. Meher, S. Chandrasekaran and A. Amira “FPGA Realization of FIR Filters by Efficient and Flexible

Systolization Using Distributed Arithmetic” IEEE Transactions on Signal Processing, VOL. 56, no. 7, pp

3009-3012, July 2008.

B. K. Mohanty, P. K. Meher, S. Al-Maadeed, and A. Amira “Memory Footprint Reduction for Power-

Efficient Realization of 2-D Finite Impulse Response Filters”, IEEE Transactions on Circuits and

Systems—I: REGULAR PAPERS, VOL. 61, No. 1, January 2014.

Image filtering

© 2015 Abbes Amira

A.AhmedSaid and A.Amira “Accelerating SVD on Reconfigurable Hardware For Image Denoising”

Proceedings of the IEEE International Conference on Image Processing (ICIP 2004), Singapore, October

24-27, 2004.

Image filtering NXN

SVD Reconstruction

Threshold

Noisy image Denoised image

FPGA

Partitioning of the

ImageComputation of SVD

output

blocks

D

.

.

.bank1

output

blocks

V

.

.

.bank2

output

blocks

U

.

.

.bank3

input

image

blocks

.

.

.bank0

Computation time of the SVD of one block

is 0.123ms (fmax=84.44 MHz) which

means 8130 blocks per second can be

processed.

X2 speedup

The use of a faster and a larger FPGA

(which can allow more parallelism), can

lead to higher performances

© 2015 Abbes Amira

A. Ahmad, A. Amira, H. Rabah and Y. Berviller, “Medical Image Denoising on FPGA using Finite Radon

Transform”, IET Image Processing – Volume 6, Issue 9, pp. 862 – 870, December 2012.

Image filtering

3-D

Transform

Quantisation/

selection

Entropy

coding

Buffers Buffers Buffers

Image

de-noising

z

Output: Bitstream

(Compressed

medical images)

Input: 3-D

medical images

Finite Radon

transformThresholding

Inverse finite

Radon transform

Buffers

Pre-processing Compression system

Sub-images [1]

Sub-images [n-1]

x

y

(a) (b)

Image noisy with σ2 = 0.16

29.91 dB

Image de-noising by FRAT, p = 7

(a)

34.48 dB

(b) (c)

Type Platform ReferencesMax. frequency

(MHz)Throughput

(MSPS)Area

(Slices)

Sequential

Virtex-II

Rahman & Wadawy (2004) 100.13 9.87 159Uzun & Amira (2005): Arch. 1 112.87 11.13 198Uzun & Amira (2005): Arch. 2 67.30 6.64 131Chandrasekaran et al. (2008) 79.97 37.32 215

Virtex-EChandrasekaran & Amira (2005) 69.00 6.90 345

Chandrasekaran et al. (2008) 94.46 45.01 245

Virtex-5Proposed - Loops rolled 174.30 0.12 669

Proposed - Loops unrolled 127.80 8.52 2,704

Pipelined Virtex-5Proposed - Loops rolled 161.40 13.31 2,044

Proposed - Loops unrolled 103.50 48.30 5,286BRAM-based Virtex-5 Proposed 188.90 6.30 637

© 2015 Abbes Amira

A. Amira, S. Chandrasekaran and D. Skinner “A Fast Hybrid LSI Approach for Intelligent Information Retrieval”

Proceedings of the 4th IET Visual Information Engineering 2007 Conference (VIE2007), London 25-27 July 2007.

Information retrieval using LSI

Document

Preprocessing

Term Document Matrix

(TDM) Generator

TDMQuery

Vector

Decomposition: SVD/

Haar

Visualisation

Engine

Document

Cosines

Results

Processor

Search Results

User Query

Update

Engine

Input

Database

Hardware

Acceleration

Parametrisable

Handel-C Code

Matrix

Param-

eters

DK4 EDIF Generation

Xilinx ISE P&R

Generate FPGA

Bitstream

Host Program

A. Alzu'bi, A. Amira and N. Ramzan “Semantic Content-based Image Retrieval: A Comprehensive Study”. Journal

of Visual Communication and Image Representation (Elsevier), 2015.

M. Al-Qahtani, A. Amira, N. Ramzan “An Efficient Information Retrieval Technique for

e-Health Systems” The 22nd International Conference on Systems, Signals and Image Processing, IWSSIP 2015,

London, 10-12 September 2015

© 2015 Abbes Amira

M. Neggazi, M. Bengherabi, Z.Boulkenafet and A.Amira “An Efficient FPGA Implementation of Gaussian

Mixture Models Based Classifier: Face Recognition Application”, the 9th IEEE International Workshop on

Systems, Signal and their Applications (WOSSPA2013), 12-15 May 2013, Algiers, Algeria.

GMM based Classifier

M. Shi, A. Bermak, S. Chandrasekaran, A. Amira and S. Brahim-Belhouari, “A Committee Machine Gas

Identification System Based on Dynamically Reconfigurable FPGA” the IEEE Sensors Journal, VOL. 8,

NO. 4, April 2008.

© 2015 Abbes Amira

A. Al-Harami, A. Al-Mansoori, R. AlMesallam, U. Qidwai, A. Amira, “An Intelligent Sensing System for

Healthcare Applications using Real-time EMG and Gaze Fusion" The IEEE Technically Co-Sponsored SAI

Intelligent Systems Conference, London, 10-11 November 2015.

Robotics for Connected Health

© 2015 Abbes Amira

2011 Gold Crest Award for Sentinus project on “No More Home Alone: A robotic application for connected

health applications”

Robotics for Connected Health

© 2015 Abbes Amira

AI for Connected Health

L. Müller, S. Zagaria, A. Bernin, A. Amira, N. Ramzan, C. Grecos and F. Vogt “EmotionBike: A Study of

Provoking Emotions in Cycling Exergames”, 14th International Conference on Entertainment Computing

(ICEC), 2015, Trondheim, Norway

© 2015 Abbes Amira

Conclusions

• Connected health concepts and challenges

• Some multiresolution and imaging algorithms have been

implemented and accelerated on reconfigurable

hardware

• Some applications have been addressed including fall

detection, ECG monitoring and medical imaging

• Design approaches and methodologies from Algorithm

to Power Modelling have been presented

© 2015 Abbes Amira

• Compressive sensing and Classification methods

• Co-design using the new FPGA platforms: partitioning the

design and implementation on the FPGA and ARM

processor

• Fall detection, gait analysis, ECG monitoring

• Medical devices with embedded internet services

• Pattern recognition for vital signs analysis and fusion

Ongoing Research

© 2015 Abbes Amira

Data Fusion Processing Analysis

Decision

Making

Intelligent and Pervasive

Fusion and

Optimisation

Embedded and Wireless

Data

Retrieval

Distributed

And Wireless

Ongoing Research

© 2015 Abbes Amira

• Connected Health: How mobile phones, cloud, and big

data will reinvent healthcare?

• Social networking and internet services

• Towards prevention and management

• Training: transition from traditional to connected?

Ongoing Research

© 2015 Abbes Amira

References and Acknowledgment

www.xilinx.com

Applications notes: DPR, ISE, Zynq, Family 7, Spartan 3

Xilinx university program

www.xilinx.com/university

PPT, Notes, Documentations, Code

www.mentor.com

Handel-C application notes

RC devices

Thanks to all my research team members and collaborators

University of the West Scotland (Visual Communication Cluster)

Qatar University (Embedded Computing Systems)

© 2015 Abbes Amira

Thank You

Better Life quality at low cost

Connecting people and information for better life

Toward Embedded, Wireless, Intelligent, Continuous, Low-

Cost Solutions