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Grant: Discovery Grants Program - Individual

York University

$55,000.00

Remotely Powered Flexible Wearable Sensors for Managing Chronic Diseases

FY 2020-21

Summary
Cardiovascular diseases (CVDs) are the number ONE cause of death globally according to World Health Organization. As pointed out in the 2019 Report from Heart economy $3.6 billion a year in physician services, hospital costs, lost wages and decreased productivity. Dementia (the most severe form of vascular cognitive impairment) costs $33 billion a year in direct and indirect costs and this number is expected climb to $293 billion a year by 2040. With the advancement of information technologies, it is possible to prevent and detect CVDs at early stage with the help of wearable wireless biomedical sensors. Many types of wearable sensors were developed in the last decade for this purpose. However, none of them provides the information that are comparable to data recorded by medical grade equipment at hospital. This is mainly due to signal distortions caused by motion artifacts when people carry out daily activities with wearable devices. The motion artifacts are caused by several factors: (1) the weight of sensor that pulls the electrodes attached to skin causing signal distortion, and (2) the change of skin-electrode contact resistance due to motion and muscle activities. This project aims to create an innovative solution that solves the motion artifact problems faced by existing wearable sensors. We propose to conduct research in following areas: (1) thin flexible electrodes that conform to body shape with improved contact impedance; (2) ultra-high input impedance analog front-end interface circuit that is suitable for dry electrodes used in flexible electrodes. This will help to maximize input signal strength while minimizes the ambient noises; (3) large dynamic range instrumentation amplifier that is tolerant to large motion artifacts, i.e. to avoid saturation of amplifier when a large motion artifact is presented at input; (4) removal of battery in the sensor to reduce the weight by powering the sensor remotely; (5) ultra-low power embedded machine learning algorithm at the sensor for compensating motion artifacts and for CVD early warning sign detection. The goal is to design a system-on-chip solution for a remotely powered flexible wearable sensors that is thin, skin-friendly, bendable, light weight, and low cost. The success of proposed project will create a solution for preventing, predicting, and managing chronic diseases. It will help to improve the quality of CVD patient's life while helping reduce the CVD related health care cost.
Program
Discovery Grants Program - Individual
Location
Ontario, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data

As published

Application id
RGPIN-2020-06116
Name nom
Lian, Peter
Department département
Electrical Engineering and Computer Science
Organization id
38
Institution établissement
York University
Province en
Ontario
Province fr
Ontario
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2020
Competition year année de concours
2020
Award amount
55000
Program id
RGPIN
Program name en
Discovery Grants Program - Individual
Program name fr
Programme de subventions à la découverte - individuelles
Group en
Discovery grants programs
Group fr
Programmes de subventions à la découverte
Committee code
1510
Committee name en
Electrical and Computer Engineering
Committee name fr
Génie électrique et informatique
Area of application code
802
Area of application group en
Information and communication services
Area of application group fr
Services d'information et de télécommunications
Area of application en
Information systems and technology
Area of application fr
Systèmes d'information et technologie de l'information
Research subject code
2506
Research subject group en
Electrical and electronic engineering
Research subject group fr
Génie électrique et électronique
Research subject en
Electron devices
Research subject fr
Dispositifs électroniques
Installment
inst:1-5
Partie
5
Nb partie
5
Application title
Remotely Powered Flexible Wearable Sensors for Managing Chronic Diseases
Keyword
NSERC
Application summary
Cardiovascular diseases (CVDs) are the number ONE cause of death globally according to World Health Organization. As pointed out in the 2019 Report from Heart economy $3.6 billion a year in physician services, hospital costs, lost wages and decreased productivity. Dementia (the most severe form of vascular cognitive impairment) costs $33 billion a year in direct and indirect costs and this number is expected climb to $293 billion a year by 2040. With the advancement of information technologies, it is possible to prevent and detect CVDs at early stage with the help of wearable wireless biomedical sensors. Many types of wearable sensors were developed in the last decade for this purpose. However, none of them provides the information that are comparable to data recorded by medical grade equipment at hospital. This is mainly due to signal distortions caused by motion artifacts when people carry out daily activities with wearable devices. The motion artifacts are caused by several factors: (1) the weight of sensor that pulls the electrodes attached to skin causing signal distortion, and (2) the change of skin-electrode contact resistance due to motion and muscle activities. This project aims to create an innovative solution that solves the motion artifact problems faced by existing wearable sensors. We propose to conduct research in following areas: (1) thin flexible electrodes that conform to body shape with improved contact impedance; (2) ultra-high input impedance analog front-end interface circuit that is suitable for dry electrodes used in flexible electrodes. This will help to maximize input signal strength while minimizes the ambient noises; (3) large dynamic range instrumentation amplifier that is tolerant to large motion artifacts, i.e. to avoid saturation of amplifier when a large motion artifact is presented at input; (4) removal of battery in the sensor to reduce the weight by powering the sensor remotely; (5) ultra-low power embedded machine learning algorithm at the sensor for compensating motion artifacts and for CVD early warning sign detection. The goal is to design a system-on-chip solution for a remotely powered flexible wearable sensors that is thin, skin-friendly, bendable, light weight, and low cost. The success of proposed project will create a solution for preventing, predicting, and managing chronic diseases. It will help to improve the quality of CVD patient's life while helping reduce the CVD related health care cost.