Integrating wearable sensor technology into signal processing courses allowed students to record and analyze their own ECG data, enhancing engagement and practical experience with recording hardware.
Incorporating wearable ECG sensors into signal processing curricula enhances student engagement and practical learning.
By bringing research into the curriculum, this article explores new opportunities to refresh some classic signal processing courses. Since 2015, we in the Electrical and Electronic Engineering (EEE) Department of Imperial College London, United Kingdom, have explored the extent to which the level of student engagement and learning can be enhanced by inviting the students to perform signal processing exercises on their own physiological data. More specifically, using new wearable sensor technology and video instructions as an experiment guide, the students are asked to record their electrocardiograms (ECGs) and perform both time- and spectral-domain estimation tasks on their own real-world data. In this way, the students not only gain experience with recording hardware and sources of signal contamination (baseline wanders and artifacts), but they also are highly motivated by being kept in the loop and through their part ownership of their course.
Kanna et al. (Thu,) conducted a other in Education. Wearable sensor technology and video instructions for ECG recording was evaluated. Integrating wearable sensor technology into signal processing courses allowed students to record and analyze their own ECG data, enhancing engagement and practical experience with recording hardware.
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