ObeSense Monitoring the Consequences of Obesity
Motivations Obesity is associated with multiple health problems
Cardiovascular diseases Atrial fibrillation Hypertension Obstructive sleep apnea Diabetes Certain types of cancer
Has been proven to reduce life expectancy
10% of premature adult deaths
Is reaching epidemic proportions
i. e. Switzerland: 48.7% overweight, 8.3% obese 7.3% of the total healthcare expenses
Guidelines about identification, evaluation and treatment exist
Those guidelines require long-term monitoring Such monitoring systems do not exist
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Objectives
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Answer a clear medical need by joining research in physiological markers sensors with clinical end-users Develop innovative and non-invasive sensors. Integrate them into single long-term monitoring systems adapted
to obese patients. • Multi-parametric, low-power, allergy-free, comfortable, with online feedback.
Sophisticated software and algorithms.
Central involvement of end-users. • Through 3 clinical scenarios
Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomograohy
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomograohy
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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Monitoring system  WP1: Monitoring of respiratory rate and volume EMPA - CSEM
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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… Monitoring system
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WP2: Cardiac output CSEM – EPFL/LTS5 – EPFL/LHTC feasibility of measuring cardiac output non-invasively via electrical impedance tomography (EIT)
EIT
CO = 5 l/min
1. Simulations
‌ Monitoring system
2. Measurements
4D Bio-Impedance Model
EIT In planning‌
Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
Oxygen consumption by NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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… Monitoring system
WP3: Estimation of energy expenditure Detection of anaerobic threshold (AT)
IRR, CSEM, EPFL-ASPG Respiratory variables recorded from 12 healthy subjects while exercising incrementally. BR and VT by ergospirometer, HR by instrumented t-shirt (CSEM SEW model).
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… Monitoring system
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…Estimation of energy expenditure Platform with 3D accelerometer and ECG front-end almost complete
Front view
Front view with electronic components Back view
‌ Monitoring system
Energy expenditure estimation based on acceleration and ECG compared to indirect calorimetry.
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‌ Monitoring system
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 ‌Estimation of energy expenditure  Fick-based method
USZ
VO2: CO: cHb: CO SaO2 SvO2
VO2 =
đ?‘?đ??ťđ?‘?Ă—coĂ—
SaO2 – SvO2 đ?‘˜1
Oxygen consumption (mL/100g/min), Cardiac output (mL/100g/min), Haemoglobin concentration (g/dL). stroke volume Ă— heart beat, SV = EDV – ESV ≈ 70 đ?‘šđ??ż, pulse oximetry, novel NIRS system.
measured as part of other WPs
… Monitoring system
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…Estimation of energy expenditure Fick-based method
USZ Energy expenditure
Heart beat
Respiration rate
Energy expenditure
Heart beat (beats/min) Respiration rate
Stroke volume
… Monitoring system
…Estimation of energy expenditure Sensor design and cell-phone/laptop interface
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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… Monitoring system
WP4: Blood pressure (BP) CSEM Estimation of BP based on Pulse Transit Time (PTT). Non-invasive, continuous measurement based on ICG, ECG, PPG.
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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… Monitoring system
WP5: Smart ECG T-shirts EMPA - CSEM Textile based ECG electrodes with humidication pad, Integration into T-shirt and short validation.
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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‌ Monitoring system
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 Wireless body sensor network CSEM Embedded architecture for processing of multiple bio-signals and the integration of signal processing algorithms on the embedded hardware.
… Monitoring system
Multi-parameter sensing EPFL - ESL Touch based/wearable: 1-lead ECG Respiration Skin conductance Motion Body fat and hydration level Emotions: mood (valence/arousal), stress Real time BT 4.0 communication, open APIs.
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Monitoring system
Monitoring system WP1: respiratory rate and volume
Flexible optical fibers
WP2: cardiac output
Electrical Impedance Tomography
WP3: energy expenditure
NIRS
WP4: blood pressure
Anaerobic threshold
WP5: ECG T-shirt
ICG, ECG, PPG
WP6: wireless body sensor network
Textile based ECG electrodes
WP7: ECG analysis
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… Monitoring system
ECG analysis EPFL - ASPG QRS complexes and fiducial points detection in the ECG by
means of mathematical morphology operators in an adaptive manner.
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Clinical scenarios Scenario 1: physical activity & lifestyle interventions – Supervised by Dr O. Dériaz (IRR) and Dr U. Mäder
(SFISM) on patients following activity regimen in lab settings and at home
Scenario 2: hospitalization monitoring – Obesity and atrial fibrillation, hypertension and type-
2 diabetes – Supervised by Dr E. Pruvot (CHUV)
Scenario 3: ambulatory monitoring – Obesity and outpatient cardiovascular complications – Supervised by Dr E. Pruvot (CHUV)
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