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Unit outline_

BMET2922: Computational Analysis for Biomedical Signals

Semester 2, 2024 [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

Academic unit Biomedical Engineering
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
BMET9922
Assumed knowledge
? 

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

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Ann Na Cho, ann.cho@sydney.edu.au
Lecturer(s) Christina Maher, christina.maher@sydney.edu.au
Tutor(s) Luis Fernando Herbozo Contreras, luis.herbozocontreras@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Small continuous assessment Lab activities
Completion of prescribed lab activities during scheduled lab sessions
10% Multiple weeks During lab sessions
Outcomes assessed: LO1 LO7 LO5 LO4 LO3 LO2
Tutorial quiz Fortnightly mini-quizzes
Fortnightly mini-quizzes to reinforce lecture content
10% Multiple weeks ~20 minutes
Outcomes assessed: LO1 LO8 LO6
Assignment Major project - System Design Report Part 1
Written system design report in the lead up to major project
15% Week 04
Due date: 23 Aug 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
Assignment Major project - System Design Report Part 2
Written system design report in the lead up to major project
15% Week 07
Due date: 13 Sep 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
Presentation Live demonstration
Live demonstration of developed software during lab sessions
10% Week 12 ~3 minutes
Outcomes assessed: LO1 LO7 LO5 LO4 LO3
Assignment group assignment Major project - Project Report
Major project report
40% Week 13
Due date: 03 Nov 2024 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO7
group assignment = group assignment ?

Assessment summary

“Keeping you on your toes” assessments

  • Lab activities: Students will be graded based on their level of completion of the prescribed weekly lab activities
  • Fortnightly mini-quizzes: Students will demonstrate understanding of the lecture content via fortnightly mini-quizzes. These can be completed anytime during the fortnight; a time limit (~20 mins) will be applied upon quiz start

 

Major Project

Students will develop a basic hardware/software setup using optical electrocardiography. Based on this major project, three deliverables are expected as outcomes:

  • Design report: Split into two parts for checkpointing purposes, each part worth 15%; total 30%
  • Live demonstration + brief commentary: Students will be asked to demonstrate their project and provide commentary during Week 12 lab sessions. These are anticipated to be ~3 mins each, worth 10%.
  • Major project report: A final report to outline and discuss the development process of the final product. 40% of coursework mark

 

 

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (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

Description

High distinction

85 - 100

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard as defined by grade descriptors or exemplars established by the faculty.

Distinction

75 - 84

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard as defined by grade descriptors or exemplars established by the faculty.

Credit

65 - 74

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard as defined by grade descriptors or exemplars established by the faculty.

Pass

50 - 64

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard as defined by grade descriptors or exemplars established by the faculty.

Fail

0 - 49

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard established by the faculty. This grade, with corresponding mark, should also be used in cases where a student fails to achieve a mandated standard in a compulsory assessment, thereby failing to demonstrate the learning outcomes to a satisfactory standard. In such cases the student will receive the mark awarded by the faculty up to a maximum of 49.

For more information see sydney.edu.au/students/guide-to-grades.

 

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 Assessment Procedures 2011 provide that any written work submitted after 11:59pm on the due date will be penalised by 5% of the maximum awardable mark for each calendar day after the due date. If the assessment is submitted more than ten calendar days late, a mark of zero will be awarded.

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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.

Support for students

The Support for Students Policy 2023 reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy 2023. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

WK Topic Learning activity Learning outcomes
Week 01 Unit overview + Python revisited Lecture and tutorial (2 hr) LO1 LO2
Python introduction Practical (3 hr) LO1 LO3
Week 02 Microcontroller fundamentals Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4
Microcontroller Familiarisation - ESP32 Practical (3 hr) LO1 LO4
Week 03 Python control flow + Project briefing Lecture and tutorial (2 hr) LO1 LO2 LO3
Microcontroller Sensor Interfacing Practical (3 hr) LO1 LO4
Week 04 Biomedical Sensors and Signal Processing Lecture and tutorial (2 hr) LO1 LO3 LO4
Microcontroller Signal Processing and Host Data Visualisation Practical (3 hr) LO1 LO3
Week 05 Python Classes and Data Communication Lecture and tutorial (2 hr) LO1 LO3 LO4 LO5
Microcontroller Communications Practical (3 hr) LO1 LO3 LO4
Week 06 Python Dictionaries and pysimpleGUI Lecture and tutorial (2 hr) LO1 LO3 LO5
Graphic User Interfaces Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 07 Data acquisition and storage Lecture and tutorial (2 hr) LO3 LO4
Catchup Lab Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 08 C Functions, multiple files and static variables Lecture and tutorial (2 hr) LO1 LO3 LO4 LO5
Signal Power and Energy; System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 09 Software development methodologies Lecture and tutorial (2 hr) LO1 LO6
Fast Fourier transform of datasets; System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 10 System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 11 Biomedical Software Standards Lecture and tutorial (2 hr) LO1 LO6
System design and test Practical (3 hr) LO1 LO2 LO3 LO4 LO5
Week 12 Case Study Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
System demonstration Practical (3 hr) LO7
Week 13 Course review Lecture and tutorial (2 hr) LO1 LO2 LO3 LO4

Attendance and class requirements

Opportunities for discussion of course concepts are provided in lectures and labs. Students are expected to attend a minimum of 80% of all timetabled activities.

See the Faculty resolutions for more information: https://www.sydney.edu.au/handbooks/engineering/rules/faculty_resolutions.shtml

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. Understand the need for appropriate documentation and reference to standards in the development of biomedical software tools
  • LO7. Work individually or in small groups to carry out a prescribed engineering design task and present the outcomes in an oral, written or video format
  • LO8. Apply engineering principles to answer questions relating to biomedical software systems in a quiz 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Course has been adapted based on previous years' comments.

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.

Disclaimer

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.