BMET2922: Semester 2, 2021

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Units BMET2922 Semester 2 2021 [Normal day]

Unit of study_

BMET2922: Computational Analysis for Biomedical Signals

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

Overview

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	BMET2922
Academic unit	Biomedical Engineering
Credit points	6
Prohibitions ?	BMET9922
Prerequisites ?	None
Corequisites ?	None
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	Greg Watkins, greg.watkins@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam (Open book)		45%	Formal exam period	2 hours
Outcomes assessed:
Small test	Laboratory - knowledge assessment	0%	Multiple weeks	No weight in 2021
Outcomes assessed:
Tutorial quiz	Quiz 0 - Expectation alignment	0%	Week 01	15 minutes
Outcomes assessed:
Assignment	Major project - System Design Report	10%	Week 05	n/a
Outcomes assessed:
Tutorial quiz	Mid-semester quiz	10%	Week 08	1 hour
Outcomes assessed:
Presentation	Major project - system demonstration	10%	Week 12	20 minutes
Outcomes assessed:
Assignment	Major project - Project Report	25%	Week 13	n/a
Outcomes assessed:
= hurdle task ? = group assignment with individually assessed component ? = 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	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

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 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.

Learning support

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Unit Overview and Python introduction	Lecture and tutorial (2 hr)
Week 02	Project requirements and planning, Python control flow and lists	Lecture and tutorial (2 hr)
Week 02	Python introduction	Practical (3 hr)
Week 03	Micro-controller fundamentals, Python control flow, lists	Lecture and tutorial (2 hr)
Week 03	Microcontroller introduction	Practical (3 hr)
Week 04	Biomedical sensors, Python modules	Lecture and tutorial (2 hr)
Week 04	Interfacing biomedical sensors	Practical (3 hr)
Week 05	Acquiring, storing and displaying signal data from biomedical sensors	Lecture and tutorial (2 hr)
Week 05	Data Visualisation	Practical (3 hr)
Week 06	Designing the host system for biomedical signal data	Lecture and tutorial (2 hr)
Week 06	Bio signal data acquisition and display	Practical (3 hr)
Week 07	Data communication	Lecture and tutorial (2 hr)
Week 07	System design and test	Practical (3 hr)
Week 08	Mid-semester quiz	Lecture and tutorial (1 hr)
Week 08	System design and test	Practical (3 hr)
Week 09	Putting it all together: System integration	Lecture and tutorial (2 hr)
Week 09	System design and test	Practical (3 hr)
Week 10	Putting it all together: System validation	Lecture and tutorial (2 hr)
Week 10	System design and test	Practical (3 hr)
Week 11	Putting it all together: System validation	Lecture and tutorial (2 hr)
Week 11	System design and test	Practical (3 hr)
Week 12	Case Study	Lecture and tutorial (2 hr)
Week 12	System demonstration	Practical (3 hr)
Week 13	Course review	Lecture and tutorial (2 hr)

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: 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

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.

Outcomes
Graduate qualities

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
Learning outcomes

Responding to student feedback

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

This is the initial presentation of this course.

Additional information

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

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