Remotely Powered Flexible Wearable Sensors for Managing Chronic Diseases

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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 & Stroke Foundation of Canada, one person dies in Canada every five minutes from heart conditions, stroke or vascular cognitive impairment. This death rate outpaces other diseases, i.e. 13% more people die of heart condition, stroke and vascular cognitive impairment than die from all cancers combined. 2.6 million CVD related hospitalizations in Canada between 2007 and 2017. CVDs alone cost totaling $21.2 billion a year in direct (medical) and indirect (lost earnings) costs. Stroke costs the Canadian 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.