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Unit outline_

EXSS2031: Movement Analysis

Semester 1, 2021 [Normal day] - Camperdown/Darlington, Sydney

This unit of study builds on the platform established in EXSS1038 Principles of Biomechanics to consolidate prior knowledge and competency with a focus now on interpretation, application and integration of knowledge with skills. Practical classes are a key feature of this unit of study providing the learning experience in which the mathematical and problem-solving skills introduced in earlier units are specifically challenged in the conduct of kinematic and kinetic analysis of movement. A major project will be conducted to quantitatively analyse a movement task, developing problem solving, analysis and presentation skills.

Unit details and rules

Academic unit Movement Sciences
Credit points 6
Prerequisites
? 
EXSS1038
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Rene Ferdinands, edouard.ferdinands@sydney.edu.au
Demonstrator(s) Matthew Hollings, matthew.hollings@sydney.edu.au
Clorinda Hogan, clorinda.hogan@sydney.edu.au
Aaron Beach, aaron.beach@sydney.edu.au
Lecturer(s) Peter Sinclair, peter.sinclair@sydney.edu.au
Rene Ferdinands, edouard.ferdinands@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final Exam
Final written exam that assesses all material covered in the semester.
50% Formal exam period 2 hours
Outcomes assessed: LO2 LO3 LO4 LO5
Online task Quiz 1
Online quiz to promote and assess learning.
2% Week 07 20 minutes
Outcomes assessed: LO1 LO4 LO2
Online task Quiz 2
Quiz to prompt and assess state of learning
1% Week 09 30 mins
Outcomes assessed: LO3 LO4
Assignment group assignment Biomechanics Data Analysis
Group will analyse sets of kinematic data collected from Vicon.
25% Week 13 Due at End of Week 13
Outcomes assessed: LO1 LO6
Online task Quiz 3
Quiz to prompt and assess state of learning.
2% Week 13 30 mins
Outcomes assessed: LO1 LO5 LO4 LO3 LO2
Assignment Biomechanics Data Interpretation
Individual students will interpret their data and write a report.
20% Week 13 Due at End of Week 13
Outcomes assessed: LO2 LO4 LO6
group assignment = group assignment ?
Type B final exam = Type B final exam ?

Assessment summary

Type Description Weight Week Due Length
Data Analysis Assignment Group component of the assignement. Various tasks related to the analysis of motion analysis data 25% 13 TBA
Data Interpretation Assignment Individual component of the assignment. Intepretation of the motion analysis data. 20% 13 TBA
Quiz 1 Progressive learning assessment 2%  7 30 mins
Quiz 2 Progressive learning assessment 2%  9 30 mins
Quiz 3 Progressive learning assessment 1% 13 30 mins

Practicals &

Tutorials

Progressive learning: The practicals are an essential core component of EXSS2031. Students are strongly encouraged to attend all the practical and tutorial sessions. Please note that students are required to attend a minimum of 5 of the 9 practical/tutorial sessions in order to be eligible for a pass grade. An inability to meet this minimum crtierion will result in the awarding of a fail grade.
0%

Weeks

2-13

120 mins
Final Exam Written closed book exam on all work covered during semester 50% Formal Exam Period 120 mins

 

Assessment criteria

The University awards common result grades, set out in the
Coursework Policy 2014
(Schedule1).


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

Highdistinction 85 -100
Distinction 75 - 84
Credit 65 - 74
Pass 50 - 64

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:

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.

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.

WK Topic Learning activity Learning outcomes
Week 01 Week 1 A. Introduction; B. Principles of filming Lecture (2 hr) LO1
Week 02 Week 2. 2D Kinematic data collection procedures Lecture (2 hr) LO1
Week 2 Practical. Introduction to Photography Practical (1 hr) LO1
Week 03 Week 3. Qualitative Analysis of Movement Lecture (2 hr) LO2
Week 04 Week 4. Mechanics of musculoskeletal injuries Lecture (2 hr) LO2 LO4
Week 4 Tutorial. Qualitative Analysis tutorial Tutorial (2 hr) LO4
Week 05 Week 5. Signals & filtering Lecture (2 hr) LO1
Week 5 Practical. Assignment Filming Practical (1 hr) LO1
Week 07 Week 7. Biomechanics of exercise training Lecture (2 hr) LO2 LO4
Week 7 Practical. Data Analysis for Assignment Practical (2 hr) LO6
Week 08 Week 8. EMG Collection, processing and interpretation Lecture (2 hr) LO1 LO3 LO4
Week 8 Practical: Exercise Machines Practical (2 hr) LO2 LO4
Week 09 Week 9. Inverse Dynamic Analysis Lecture (2 hr) LO4
Week 9 Tutorial. Inverse Dynamics Tutorial (2 hr) LO6
Week 10 Week 10. Kinematics and Kinetics of Gait Lecture (2 hr) LO2 LO5
Week 10 Practical EMG Practical A (First Group) Practical (2 hr) LO3 LO4
Week 11 Week 11 Practical EMG Practical B (Second Group) Practical (2 hr) LO3 LO4
Week 11. Clinical Gait Biomechanics Lecture (2 hr) LO2 LO4 LO5
Week 12 Week 12. Dynamics of Sporting Movements Lecture (2 hr) LO2 LO4
Week 12 Tutorial: Data Interpretation and Communication for Assignment Tutorial (1 hr) LO6
Week 13 Week 13. Review of semester Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 13 Tutorial Review Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6

Attendance and class requirements

The practicals are an essential core component of EXSS2031. Students are strongly encouraged to attend all the practical and tutorial sessions. Please note that students are required to attend a minimum of 5 of the 9 practical/tutorial sessions in order to be eligible for a pass grade. An inability to meet this minimum crtierion will result in the awarding of a fail grade.

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 principles of data collection for biomechanics, especially with regard to the principles of filming movements for quantitative analysis
  • LO2. Understand how the mechanical properties of biological tissues influence the response of the body to these loads, potentially causing acute and chronic injuries
  • LO3. Be able to collect and interpret EMG signals to understand the relative load and fatigue level experienced by muscles
  • LO4. Be able to assess the demands placed on the body by exercise loads and use this to recommend changes to enhance performance and reduce injury risk
  • LO5. Be familiar with the mechanical principles associated with normal and pathological gait, and how these change across the lifespan
  • LO6. Be able to conduct a biomechanical assessment of movement technique and communicate the findings to a lay audience

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.

Assignment has been continuously developed in response to student feedback.

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.