Name of Sponsoring Organization: Dr. Wilfrido Moreno
Sponsor Point of Contact (name):
Email Address:
Name of Technical Mentor (i.e. Subject Matter Expert): Kishore Kumar Kadari and Wilfrido Moreno
TM Email Address:
Project Title: Development of an Integrated Internet of Things (IoT) Healthcare System (Hardware & Data) via Model Based Systems Engineering Framework (MBSE) and Tools
Required EE Area(s) of Expertise: Basic Math, Programming skills, Embedded Systems, Engineering Design, Communication skills, Problem Solving Skills, Machine Learning, Interest to learn Internet of Things.
Brief Project Description: According to the World Health Organization, Chronic diseases or Noncommunicable diseases (NCDs) kill 41 million people each year, equivalent to 71% of all deaths globally [WHO 2021]. The world’s population is aging, and over 60 years humans are projected to accelerate in the coming decades [WHO 2019]. There is a necessity for alternative smart and conscious healthcare system through reliable and affordable care solutions. On the other hand, according to Gartner (technological research and consulting firm), the Digital Twin (DT) technology was in the top 10 technological trends for 2020. Also, MarketsandMarkets explain that the Digital Twin market is expected to grow. The proposal consists of carrying out the design, implantation, training and testing process of a Digital Twin to characterize the stages of lung diseases through spectroscopic analysis of the patient's breathing, audio and video signals correlated to the lung disease diagnostic, with the use of machine learning and AI
Major system products expected from this project: 1. Build a Digital Twin for data collection and monitoring 2. Create an integration to MBSE tools and Hardware in real time. 3. Create an integration to IoT services and Hardware in real time. 4. Testing on indicating the health description, prediction, and prescription in real-time on an embedded system
Parts of the project that “Engineering Design” is expected (e.g., mathematical calculations and/or verification simulations): 1. Digital Twin Edge Device • Real-time monitoring loop • Output/Actuation: Display (annotate the images), Sound. • Cloud Integration • Communication between the above items. 2. Cloud • Data Capture - Receiving and storing measurements from the device • Data Collection for the initial Model creation • Clean and transform data into a standard pipeline. • Intelligence - Prediction / Prescription / Integration o Model Creation on Cloud ML Services • IoT deployment (i.e., deployment of new ML models at scale)
The level of support (Time: technical guidance, materials, computing, financial, etc.) for the project. If no financial support will be provided, please offer an estimate of the Materials Cost: Time: Technical guidance by the mentors (1 hour/week) Materials: GPU based Computer and Cloud Platform (Azure or AWS, GCP) access, Sensors, MBSE Tools will be supplied to the students.