Leveraging Industry 4.0 in Education for Remote Implementation in a Team-Based Computer Engineering Capstone Project

This Innovative Practice Category Full Paper presents the remote implementation of an embedded systems capstone project for computer engineering students. A capstone project is a feature of most undergraduate programs in computer engineering. Such a project is usually meant to expose students to the development of a large system from conceptualization to its final implementation, involving substantial design and development of hardware and software components. In our university, students were given the opportunity to work on a cutting-edge problem focused on healthcare - “Designing a wearable device that automatically detects human activities”. It is an area where a large amount of research is ongoing, and hundreds of scientific papers are published every year. In addition, students had to evaluate and adopt techniques from existing literature and adapt them to meet the problem requirements. Students equipped themselves with state-of-the-art hardware, Bio-signal processing, machine learning, power optimization and secure communications to design the wearable. Thus, the project reinforced their knowledge of fundamentals, while exposing them to a problem with no obvious solution. Through the capstone project, students are able to better appreciate the relevance of the various components in the computer engineering curriculum to large-scale computer engineering projects. Students are organized into teams of six to execute the project. The COVID-19 pandemic resulted in the university migrating the teaching online for all courses. This was particularly challenging to implement for the capstone project as one of the key requirements is for the members to work together and subsystems to interact with each other. A course refresh resulted in a credits update and this provided a unique opportunity for the teaching team to re-design the project with core Industry 4.0 technologies such as hardware acceleration, remote processing, networking, remote analytics, and secure protocols. This work presents a framework for its implementation, reviews the challenges encountered and the processes put in place to ensure data security and smooth running of the project in the event of future disruptions. Quantitative and qualitative results from the course feedback surveys are analyzed to gauge student response to the implementation and compared to the previous version.