Vital Signals Monitoring Wheelchair

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© 2005, it - instituto de telecomunicações. Todos os direitos reservados.

PhD Candidate Eduardo Pinheiro

Supervision:Prof. Octavian PostolacheProf. Pedro Girão

Vital Signals Monitoring Wheelchair

PDEEC Doctoral SeminarJuly 21st, 2010, IST, Lisboa

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Doctoral Seminar

IST, Lisboa, July 21st, 2010

Table of Contents

Introduction: Who? What? Motivation: Why? Options and Solution

Objectives and Work PlanState of the art

Start-up: Courses, Knowledge reviewDevelopments: Sensing, Processing, Networking

Summary and Future Works


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Who?

Eduardo Pinheiro, 2nd year PhD studentDegree in Automation, Control and Instrumentation 18.4 GPAFinal project ECG+Resp+Temp monitor platform4 Merit Scholarships and Santander Prize Best 2007 Graduate2½ years responsible by I&M and A&Rob labs5 conference papers

@IST {+1patent + 2chapter + 3journal +12conf + Doct Cons +DARPA Talk}

Octavian Postolache, IT Principal Researcher, Adj. IPS Professor, SM IEEE

Pedro Girão, IT Senior Researcher, IST Full Professor, SM IEEE

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Thesis focus

Cardiovascular and PulmonaryUnobtrusive and Continuous Monitoring

of Wheelchair Users

Aiming at the development ofinconspicuous instrumentationinteractive and ubiquitous system personalized data analysis and diffusion


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Why?

Millions of wheelchair users Many are elders, diabetics, or stroke victimsOften 24/7 monitoring (HR, BP, RespR, HRV, BPV, CO…)

Requirements for optimal monitoring systemAccurateContactlessContinuousInconspicuousReal-time Unrestrictive Wireless

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Cardiac informationElectrocardiogram

represents the myocardium electrical stimulusPhotoplethysmogram

registers tissues’ light transmittance swings induced by blood flowImpedance plethysmogram

indicates changes in the tissues’ conductivity due to blood passageBallistocardiogram

assesses pressure oscillations due to heart activity


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Cardiac information

Blood Pressurearterial pressure in the systemic circulation

Stroke Volumeamount of blood expelled by a ventricle in one beat

Cardiac OutputSV*HR, volume of blood expelled by a ventricle in one minute

Heart Rate, Blood Pressure and their variabilities

markers of the autonomic cardiovascular system regulation

HRV is a diagnostic and outcome estimator of pathologies

HRV and BPV are capable of foretelling cardiovascular risksPAT

allows estimation of Systolic BP, and BPV from PATV

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OptionsEmbedded BCG, IPG and ECG may help

HR and RespRbasic information, comes from every cardiac signal

BP, SV and COinstantaneous utmost importance, only from invasive measurements

HRV, BPVlong term utmost importance, only from obtrusive measurements

This set of signals and parameters allows profound cardiopulmonary status continuous description and forecast


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Table of Contents





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Objectives

1. Posture, stability and motion Cardiorespiratory and motor activity monitoring

Inverse problem on sensor noise

2. Real-time knowledgeHR, RespR, PEP, PAT, PTT, BP

3. Data classification & Critical events predictionHRV, BPV, fusion of other data → alarms, “Are you OK?”

4. User’s WhellchairAN into HealthCareNSafe data sharing, database remote access


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Work Plan

0 Meaningful data

1. Stable and robust hardware

2. RT algorithms for baseline wandering removal

3. Accurate physiological parameter estimation

4. Posture ↔ ∆IPG ↔ ∆BCG ↔ ∆acc

5. User identification (wheelchair+environment)

6. Continuous & Personalized by meta-data

7. Interact & Integrate in HealthCareNetwork

Healthy & Controlled → Uncontrolled → Hospital

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State of the Art

Few devices developed (single signal)Wheelchairs are underexploredRadar BCG

All the signals are very unstable (motion)Weak results on ↑SNR in unstable BCG and IPGICA, PCA, DWT, NN(SF-ART, TFM-SVD) → poor BCG classificationUbiquitous ECG

Diverse portable systems without interactionNumerous PANs and automated ECG classifiersSmartphone apps


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Table of Contents





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PhD Disciplines

Applied for 6 PhD-level courses (37.5 ECTS), 5 are completed

Wireless Sensor Networks (16)

Reconfigurable Computing (16)

Autonomous Systems (18)

Computers and Sensors Interfaces (19)

Inverse Problems in Signal and Image Processing (1st term 08/09)

Statistic Learning (2nd term 09/10)


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Basis revision

Review of all BCG knowledge [1] (163 refs)contractility mechanism status indicatormorphology directly related to heart disease riskabandoned due to ECG, but betterdistortions are not disease-specific

State of the art was reviewed [2,3] (51,88 refs)embedded ECG is comparable to direct contact (Quasar)

eECG, eBCG, IPG, and radar BCG need motion immunity

[1] “Theory and Developments in an Unobtrusive Cardiovascular System Representation : Ballistocardiography”, The Open Biomedical Engineering Journal, 2010.[2] “Recent Advances on Unobtrusive Measurements of the Cardiovascular Function”, Proc. 4th International Conf. on Sensing Technology, Lecce, Italy, June 2010.[3] “Unobtrusive and non-invasive sensing solutions for on-line physiological parameters monitoring" Chapter in Wearable and autonomous biomedical devices and systems: New issues and characterization, Springer-Verlag, 2010.

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Sensing developments

Basic prototypes of physiological sensing devices were developedBCG, PPG, ECG, then IPG and eECG

Validated the signals obtained, observed the physiological relations expected between PAT, PTT, PEP, and their variabilities [4,5]

small error between the HR estimates HRV estimates from these signals are very similar HRV from ECG is best, then PPG, and BCGBCG-PPG delay is synchronized with PAT

frequency → εHF<εVLF<<εLF and ρVLF>>ρHF>ρLF

price to pay → 9 to 16% estimation error

[4] “Blood Pressure and Heart Rate Variabilities Estimation Using Ballistocardiography“, Proc. 7th Conf. on Telecommunications, Santa Maria da Feira, Portugal, pp. 125-128, May 2009.[5] “Pulse Arrival Time and Ballistocardiogram Application to Blood Pressure Variability Estimation”, Proc 4th IEEE Int. Workshop on Medical Measurements and Applications, Cetraro, Italy, pp. 132-136, May 2009.


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Sensing developments

IPG [6,7]extremely informativeeven more unstable than BCGdoes not need physical contactworks up to 20 cm separation

embedded ECG [7,8]interference noise is a severe problemvery careful design mandatoryimproved by PCA

[6] “Automatic Wavelet Detrending Benefits to the Analysis of Cardiac Signals Acquired in a Moving Wheelchair”, Proc. 32nd IEEE EMBS Annual Conf., Buenos Aires, Argentina, September 2010.[7] “Inconspicuous Measurements of Cardiac Function: Shielding (ECG) and Radiating (ICG) Approaches”, Proc. Portuguese Physics for Health Summer School, Covilhã, Portugal, July 2010.[8] “Stationary Wavelet Transform and Principal Component Analysis Application on Capacitive Electrocardiography”, Proc. 2010 Int. Conf. on Signals and Electronic Systems, Gliwice, Poland, September 2010.

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Signal Processing developments

Simulation results on digital notch filters for reconf platforms [9-11]problematic in fixed-point: two regions of instabilityno rule to choose optimal number of bitsFPGA latency does not suffer

[9] “Digital Notch Filters Implementation with Fixed-point Arithmetic”, Proc. XIX IMEKO World Congress, Lisboa, Portugal, pp. 491-496, September 2009.[10] “Implementação de Filtros Notch em Aritmética de Ponto Fixo”, Proc. VI Jornadas sobre Sistemas Reconfiguráveis, Aveiro, Portugal, pp. 91-96, February 2010.[11] “Fixed-point implementation of infinite impulse response notch filters”, Metrology and Measurement Systems, Vol. XVII, no. 2, 2010.


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Adaptive peak detectors and classifiers [4,5,12]wavelet-based good in stable signalsdemodulation+FFT surpasses artifactspartial recomposition of EMD diminishes artifactspower-window is faster

[12] “Online Heart Rate Estimation in Unstable Ballistocardiographic Records”, Proc. 32nd IEEE EMBS Annual Conf., Buenos Aires, Sep 2010.

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Study on detrending the signals sensors’ hardware is always limitedwavelet baseline estimation improves signals [6]stationary wavelet+PCA good in eECG [8]partial recomposition of EMD positive for all [13,14]ICA with 2BCG and 2accelerometers not much improvement [15]

[13] “Merging Multi-Level Decompositions and Feature Extraction to Optimize Biological Data Analysis”, Proc. Portuguese Physics for Health Summer School, Covilhã, Portugal, July 2010.[14] “Assessment of Empirical Mode Decomposition Implementation in Cardiovascular Signals”, Proc. 17th IMEKO TC4, Kosice, Sl, Sept 2010.[15] “Physiological Parameters Measurement based on Embedded Sensors in a Wheelchair and Advanced Signal Processing”, IEEE Transactions on Instrumentation and Measurement, 2010.


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Detrending methodsStill limitations in baseline removalMore adaptability needed

Wavelets and PCA are fastEMD is slower and not controlled

EMD is more flexibleEMD is easier to implement in low SP-power platforms

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Networking developments

Implemented prototypes with WiFi compatibility

Compressed SensingSampling below Nyquist is possible

IA

Wi-Fi

Laptop PC

BCG and ACC

Signal conditioning

BCG-F

IPG electrodes

IPG-F WiFi IA

BCG sensor and Acc


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Doctoral Seminar


Networking developments

Compressed Sensingcardiovascular signals are compressible and recoverable [16]wireless sensor networks benefit from CS [17]packet loss is forbidden, even so CS is profitable (power, duty-cycle)

[16] “Compressed Sensing Implementation in Cardiac Signals", Proc. 5th Int. Workshop on Intelligent Data Acquisition and Advanced Computing Systems, Rende, Italy, pp. 96-101, September 2009.[17] “Implementation of compressed sensing in telecardiology sensor networks”, International Journal of Telemedicine and Applications, 2010.

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Table of Contents






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Summary

Basisextensive review of SOA and BCG knowledgevalid sensing instrumentation

Controlled and Healthyvalidated the physiological relations PAT, PTT, PEPprototype embedding 2BCG + IPG + 2acc

UncontrolledHHT improves SNR (baseline)Simple adaptive classifiersICA with acc not that great

Networkinginitial wireless set-upvalidated Compressed Sensing data

reduction & encription

IA

Wi-Fi

Laptop PC

BCG and ACC

Signal conditioning

BCG-F

IPG electrodes

IPG-F WiFi IA

BCG sensor and Acc

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Future Works

1) Uncontrolled environment [3 to 6 months]database of subjectsnew processing techniques for artifact removaltrain statistic classifiers (NN, SVM, PDA, kernel-based)

2) Real care-needing users [12 to 15 months]database and coordination systemDSP-based system with wireless integrationcreate interaction

3) Infere models [3-6 months]sensors’ noise relation to motion/postureestimation of personal characteristics


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Vital Signals Monitoring Wheelchair