Proficiency in signals and systems is essential to a biomedical engineer’s (BME) education, as many key technologies in healthcare—such as medical imaging, diagnostic instrumentation, wearable health monitors, and electronic health record systems—depend on digital signal processing. BME students typically find signals and systems courses difficult because they require an intuitive understanding of calculus, differential equations, circuit analysis, and principles of human physiology. In addition, signals and systems courses require application of mathematical formulas to model and analyze signals as well as cognitive flexibility in switching between time and frequency domains.
In traditional electrical engineering-oriented signals and systems courses, concepts are presented from the perspective of mathematical modeling of systems, where the signals being investigated are primarily periodic and predictable. Such math-focused approaches can deprive students of the critical connections they could be making between theoretical concepts and human physiology. Our course emphasizes the development of fundamental skills that enable students to observe and identify key features of physiological signals, supporting visualization, modeling, and analysis without requiring extensive mathematical derivations. Students apply core principles of digital signal processing to analyze and interpret their own physiological data—such as heart rate, blood pressure, respiration, and muscle activation—which are inherently less predictable and not strictly periodic. This practical, pattern-seeking approach is what we refer to as the “Signal Detective Mindset.”
This paper has two primary objectives: (1) to describe the Signal Detective approach as a pedagogical tool and (2) to evaluate the effectiveness of the Signal Detective approach in enhancing students’ understanding and application of core signal processing concepts.
While the signal detective approach has been previously implemented in the course, it had not undergone formal evaluation until now. Quantitative and qualitative analysis of the data collected shows that the signal detective approach was effective. Students not only demonstrated measurable skill in signal identification but also articulated how the signal detective method improved their understanding and confidence level in tackling other signals and systems.
Students also thought the method helped clarify concepts they had learned in prior coursework as well as signals and data they encountered in their jobs (co-op positions). While the approach prioritizes applied analysis over theoretical mathematical rigor, students appear to appreciate this tradeoff - recognizing that developing intuitive, structured ways of engaging with signals is a critical step in mastering the more abstract dimensions of signal processing. This signal detective mindset offers a unique educational opportunity because it enables students to make connections between the underlying concepts and how their own bodies function.
The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025