Industry Track: Biometric, Medical, and Wearable Systems

Diagnosing birth defects in children is challenging even though there are several modalities like ultrasound and MRI. One in every 33 fetuses has a birth defect. The South-East Asia Region had the second highest prevalence of BD in the world, with a case-load of 1.13 million annually. Early detection of birth defects – in fetal life and its appropriate management, will help in reducing not only the disease burden but also the economic burden to the society. Birth defect screening has a low detection rate with the current technologies. There is a need to develop  smart sensors to detect birth defects early in fetal life. The talk is focused on the early detection of birth defects.  

There is ugent need to to move the Healthcare systems from current  “illness”-driven models (where all the stakeholders are incentivised for people being “ill”) to a “wellness”-driven model (where the business incentive can be to keep people healthy and away from hospitals). However, such systems need affordable, easily-usable and mass-deployable solutions powered by IoT and Sensing. In both developed and developing nations, a vast majority of the population is affected by non-commucable diseases in cardiovascular, respiratory and neurological space. Early detection and screening for such diseases can be really useful to improve patient’s quality of life, introduce necesaary lifestyle changes as preventie measures and reduce expenses for medical treatments.

In this talk, we present how to detect early onset of Coronary Artery Disease and Atrial Fibrillation using mobile phones and low-cost attachments to mobile phones followed by Artificial Intelligence (AI) driven analytics. We also present how wearbale sensor driven analytics can be useful for Parkinsson’s. We present various means of unobtrusive sensing follwed by necessary signal processing methods to extract clean signals from noisy sensor data and classification using AI techniques.

There is ugent need to to move the Healthcare systems from current “illness”-driven models (where all the stakeholders are incentivised for people being “ill”) to a “wellness”-driven model (where the business incentive can be to keep people healthy and away from hospitals).

However, such systems need affordable, easily-usable and mass-deployable solutions powered by IoT and Sensing. In both developed and developing nations, a vast majority of the population is affected by non-commucable diseases in cardiovascular, respiratory and neurological space. Early detection and screening for such diseases can be really useful to improve patient’s quality of life, introduce necesaary lifestyle changes as preventie measures and reduce expenses for medical treatments.

In this talk, we present various means of unobtrusive sensing follwed by necessary signal processing methods to extract clean signals from noisy sensor data and classification using AI techniques. We also present how wearbale sensor driven analytics can be useful for detecting early onset of diseases.