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Unit of study_

BMET9922: Computational Analysis for Biomedical Signals

Semester 2, 2021 [Normal day] - Camperdown/Darlington, Sydney

Biomedical engineering is being deeply reshaped by the advancements in computational tools and the utilisation of rich data. This unit will explore the processes involved in designing and building systems to perform computational analysis on biological signals, using microcontrollers and desktop or server computing. The main teaching activities will focus on the theory and practical skills for data capture, cleaning, communication, storage, and analytics. The purpose is to ensure that students develop the skills necessary to design systems that can be used for monitoring of patients, where the data can be used for analytics, e.g. prediction of an adverse event. This is relevant to a number of applications in modern healthcare such as continuous and remote monitoring devices. The unit will develop core skills in programming, solution design, sensor interfacing, and data analysis.

Unit details and rules

Unit code BMET9922
Academic unit Biomedical Engineering
Credit points 6
Assumed knowledge

Knowledge of basic biomedical engineering principles (BMET1960) and basic programming (ENGG1801 or ENGG1810 or ENGG9810 or INFO1110).

Available to study abroad and exchange students


Teaching staff

Coordinator Greg Watkins,
Type Description Weight Due Length
Final exam (Open book) Type C final exam hurdle task
45% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO8
Tutorial quiz Laboratory - knowledge assessment
0% Multiple weeks No weight in 2021
Outcomes assessed: LO1 LO7 LO5 LO4 LO3 LO2
Tutorial quiz hurdle task Quiz 0 - Expectation alignment
0% Week 01 15 minutes
Outcomes assessed: LO1
Assignment group assignment Major project - System Design Report
10% Week 05 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Tutorial quiz Mid-semester quiz
10% Week 08 1 hour
Outcomes assessed: LO1 LO7 LO5 LO4 LO3 LO2
Presentation group assignment Major project - system demonstration
10% Week 12 20 minutes
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
Assignment group assignment Major project - Project Report
25% Week 13 n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6
hurdle task = hurdle task ?
Group assignment with individually assessed component = group assignment with individually assessed component ?
Type C final exam = Type C final exam ?

Assessment summary

  • Quizzes: The first quiz must be completed in order to pass the unit of study. The mid-semester quiz may contain questions relating to any aspect of the unit up to and including the week prior to the quiz.
  • Assignments: Detailed information for each assessment can be found on Canvas.

Assessment criteria


The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see


For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

The standard university policy regarding late submission will apply, unless noted otherwise on Canvas.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Unit Overview and Python introduction Lecture and tutorial (2 hr) LO1 LO2
Week 02 Project requirements and planning, Python control flow and lists Lecture and tutorial (2 hr) LO1 LO2 LO3
Python introduction Practical (3 hr) LO1 LO3
Week 03 Micro-controller fundamentals, Python control flow, lists Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4
Microcontroller introduction Practical (3 hr) LO1 LO4
Week 04 Biomedical sensors, Python modules Lecture and tutorial (2 hr) LO1 LO3 LO4
Interfacing biomedical sensors Practical (3 hr) LO1 LO4
Week 05 Acquiring, storing and displaying signal data from biomedical sensors Lecture and tutorial (2 hr) LO3 LO4
Data Visualisation Practical (3 hr) LO1 LO3
Week 06 Designing the host system for biomedical signal data Lecture and tutorial (2 hr) LO1 LO3 LO5
Bio signal data acquisition and display Practical (3 hr) LO1 LO3 LO4
Week 07 Data communication Lecture and tutorial (2 hr) LO1 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 Mid-semester quiz Lecture and tutorial (1 hr) LO1 LO2 LO3 LO4 LO5 LO7
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Putting it all together: System integration Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 Putting it all together: System validation Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Putting it all together: System validation Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Case Study Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5
Course review Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 System demonstration Practical (3 hr) LO6

Attendance and class requirements

Opportunities for discussion of course concepts are provided in lectures and labs. Students are expected to attend a minimum of 90% of all timetabled activities.

See the Faculty resolutions for more information:

Attendance in labs (practical sessions) is required and will be assessed.


Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. Understand and communicate the principles of operation of computational systems in a biomedical device context.
  • LO2. Produce a design plan for capturing and analysing biomedical signals
  • LO3. Program solutions for biomedical signal processing tasks using existing software packages and libraries.
  • LO4. Integrate bio-electronic sensors with micro-controllers to capture biological signals
  • LO5. Apply computational tools to capture, store, transmit, analyse, and display biomedical signal data
  • LO6. Work together in small groups to carry out a prescribed engineering design task and present the outcomes in an oral, written or video format
  • LO7. Apply engineering principles to answer questions relating to biomedical software systems in a quiz format.
  • LO8. Apply engineering principles to answer questions relating to biomedical software systems in an exam format.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

This is the initial presentation of this course.

Work, health and safety

The course assumes knowledge of “c” and python. In 2021 additional lecture and lab content has been added to provide python knowledge. From 2022 python knowledge will be assumed from ENGG1810. “c” and some micrcontroller knowledge is assumed from BMET1960.



The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.