Medical Devices Lab
Units: 1.0 CU
Term: Fall 2022
When: Lecture: MW 5:15-6:15pm, Lab: T 5:15-8:15pm (all times are EST)
Where: Lecture: Moore 212, Lab: Detkin Lab
Instructor: Tania Khanna (Levine 262, seas: taniak)
Instructor Office Hours: W 1-2:30pm (in person), or by appointment
TA: Sydney Sofronici (seas: ssofroni) (office hours: F 10-11am (TBD))
Prerequisites: None, but ESE 2150 and/or ESE 2240 are recommended
Catalog Level Description:
With the demand for personalized medicine and health care, the need for consumer medical devices has risen. Traditionally devices have been designed from the ground up, but with more standardized components and software tools devices can be built to fulfill this need. This course will introduce design of medical devices. Students will learn the basics of sensors, signal conditioning, data acquisition and analysis, biopotential, biopotential electrodes, biomedical instrumentation, examples of biological signal measurement and electronics safety. This will be a lab based inquiry into medical device design.
Role and Objectives
- Utilize sensors to monitor biometric signals
- Design differential analog circuitry to acquire and condition biometric signal
- Employ circuitry to digitize and transmit data wirelessly via Bluetooth
- Build and populate a PCB given circuit schematic
- Extract biometrics (eg. Heart rate, respiration, etc.) from discrete-time signal
- Relate time-domain behaviour to frequency domain content of discrete time signals
- Implement simple digital filters
Rough Syllabus (by weeks)
- Introduction and review
- Biometric Signals
- Signal Conditioning (amplification and filtering)
- Analog-to-digital conversion
- Wireless communication
- PCB design project
- Disrete-time signal analysis
- Digital filter design
- Final demos and presentations
2022 course calendar for day-by-day calendar with assignments.
Main Reference Text
- G. Baura, Medical Device Technologies: A Systems Based Overview Using Engineering Standards, Academic Press, 2020
- A. V. Oppenheim and R. W. Schafer (with J. R. Buck), Discrete-Time Signal Processing. 3rd. Edition, Prentice-Hall, 2010
- S. Sonkusale, M. S. Baghini, S. Aeron, Flexible Bioelectronics with Power Autonomous Sensing and Data Analytics, Springer, 2022
Grading is based on homework assignments, midterm, and projects.
- Quizzes [30%]
- Lab demos [30%]
- Final demo/presentation [40%]
Lecture/Lab Mask Policy
Lecture will be in person only. Masks are required to attend lecture and lab in person, and all students are responsible for bringing their own masks. N95/KN95 and Surgical masks are recommended. Any student coming to class without a mask will be asked to leave and only return with a mask.
Use the Penn Course Absence Report (CAR) in Penn-in-Touch to report
Regrade requests must be submitted to the TA no later than 1 week after the assignment grades are released to the students. A cover sheet detailing the disrepency should be submitted with a copy of the original homework, and the TA reserves the right to regrade the entire assignment. Students are responsible for checking posted grades in a timely manner.
There are no regrades on final exams and final projects.
You may help each other understand how to use the CETS computers and course
Each student is expected to do his/her own work -- including developing the
details, drawing circuits, performing simulations, and writing the
solutions. For the homeworks and projects, you are free to
discuss basic strategies and approaches with your fellow classmates or
others, but detail designs, implementations, analysis, and writeups should
always be the work of the individual. If you get advice or insights from
others that influenced your work in any way, please acknowledge this in
In general, you are expected to abide by Penn's
Code of Academic Integrity. If there is any uncertainty, please ask.