BOARD # 18: WIP: A Methodology for Developing a “Signal Detective” Mindset in Biomedical Engineering Students

Uri Feldman; George D. Ricco

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Conference: 2025 ASEE Annual Conference & Exposition
Location: Montreal, Quebec, Canada
Publication Date: June 22, 2025
Start Date: June 22, 2025
End Date: August 15, 2025
Conference Session: Biomedical Engineering Division (BED) Poster Session
Tagged Division: Biomedical Engineering Division (BED)
Page Count: 6
Permanent URL: https://peer.asee.org/55532

Paper Authors

biography

Uri Feldman Wentworth Institute of Technology orcid.org/0000-0002-2986-748X

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Uri Feldman is an Associate Professor of Biomedical Engineering in the School of Engineering at Wentworth Institute of Technology in Boston. He received a Ph.D. from the Massachusetts Institute of Technology’s Media Lab, a B.S. in Electrical Engineering from Case Western Reserve University in Cleveland, and an M.S. in Electrical Engineering from University of Illinois at Urbana Champaign. As a Postdoctoral Fellow at Harvard Medical School at Brigham and Women’s Hospital in Boston, Dr. Feldman developed informatics metrics to quantify performance of clinicians when using digital diagnostic tools. He has published in Radiology, Academic Radiology, IS&T, SPIE, and RESNA. As a Latino and native Spanish speaker, born in Peru, Dr. Feldman has created markets and commercialized innovative telemedicine products in Latin America for medical device companies, including Orex Computed Radiography, Kodak Health Group, and ICRco. Dr. Feldman also served as Chief Information Officer (CIO) of Boston Healthcare for the Homeless Program where he led the strategic planning and migration to EPIC Electronic Health Records system and novel meaningful use implementations through the Massachusetts Health Information Exchange. At Wentworth, Dr. Feldman is focused on project-based instruction, hands-on simulations, experiential learning approaches, and first year curriculum. Dr. Feldman is one of the lead instructors for Introduction to Engineering courses, with enrollments in the hundreds each fall. His research and teaching interests, in addition to first year engineering, include telemedicine, health informatics, rehabilitation engineering, and medical robotics. Dr. Feldman has collaborated with researchers and engineers from organizations including Tufts School of Veterinary Medicine, Boston Children’s Hospital, Vecnacares, and Restoreskills.

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George D. Ricco Miami University orcid.org/0000-0002-4993-2971

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George D. Ricco is an associate professor in the Department of Electrical and Computer Engineering at Miami University. He is an engineering education educator who focuses on advanced analytical models applied to student progression. He teaches first-year engineering, energy systems, experiential learning methods, design principles, and project management.

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Abstract

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.

Citation
Format

Feldman, U., & Ricco, G. D. (2025, June), BOARD # 18: WIP: A Methodology for Developing a “Signal Detective” Mindset in Biomedical Engineering Students Paper presented at 2025 ASEE Annual Conference & Exposition , Montreal, Quebec, Canada . https://peer.asee.org/55532

TY  - CPAPER
AB  - 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. 


AU  - Uri Feldman
AU  - George D. Ricco
CY  - Montreal, Quebec, Canada 
DA  - 2025/06/22
PB  - ASEE Conferences
TI  - BOARD # 18: WIP: A Methodology for Developing a “Signal Detective” Mindset in Biomedical Engineering Students
UR  - https://peer.asee.org/55532
ER  -