Bringing Wearable Sensors into the Classroom: A Participatory Approach

By bringing research into the curriculum, this initiative 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.


Legend: MATLAB code, PDF files, Supplements and data.


The original paper can be found here:




The necessary instructions, code, data and supplementary files can be found in the following links provided.

  1. Assignment - Example questions from the coursework related to processing real world ECG signals.
  2. Recording Guidlines - The instructions required to use the iAMP device to record ECG signals for the assignment.
  3. MATLAB Scripts
    1. iAmp_import_v40.m - code necessary to convert raw binary data obtained from iAMP into decimals:
    2. ECG_to_RRI.m - an example of how ECG signals can be converted to RRI data:
  4. ECG Data - two data files containing both an example of raw ECG signal and RRI data that can be used in the assignment.
  5. Additional Material
    1. iAmp in the Classroom
    2. Recording ECG from Limbs