University of Technology Sydney

68203 Biomedical Physics Methodology

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2020 is available in the Archives.

UTS: Science: Mathematical and Physical Sciences
Credit points: 6 cp
Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Recommended studies:

68201 Physics in Action AND (33290 Statistics and Mathematics for Science OR 33230 Mathematical Modelling 2) AND 91400 Human Anatomy and Physiology AND 65111 Chemistry 1

Description

This is predominantly an experimentally based subject. It builds upon the approach to experimentation introduced in the first year and provides a firm foundation for later experimental work. In particular, a focus is brought to methods of measurement and principles of data analysis of relevance to laboratory-based experimentation. The subject reinforces basic principles with practical applications drawn from various areas of applied physics. Open-ended, project-based experiments are a major feature of this subject, where experimental design, data analysis and faithful and accurate reporting are emphasised.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. apply a range of measurement and analysis techniques found in contemporary physics-related industry and research
2. understand and apply important physical processes, such as conduction, energy conversion and transfer, and the manipulation of data
3. harness data analysis methods in order to verify or refute models of physical processes as well as understand the principles that underpin methods of analysis
4. communicate the findings of their project work in a manner consistent with that expected of a professional scientist in research and industry. In addition students should able to maintain a faithful record of work carried out in the laboratory
5. plan and implement practical investigations to answer a research question
6. access information from a variety of sources including the Internet and the library and analyse, evaluate and discriminate this experimental data
7. demonstrate the capacity to work independently against deadlines

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of following course intended learning outcomes:

  • Analyse: Demonstrate knowledge of physics with relevant discipline areas including biological and human physiological functions, advanced mathematical techniques, chemical principles and techniques and technical areas. (1.2)
  • Analyse: Investigate, gather, and critically evaluate data and academic sources. (2.2)
  • Synthesise: Design experiments to real-world problems by identifying the underlying physics and biology applicable to the research question. (2.3)
  • Apply: Exhibit competence in using scientific tools to display, process and analyse data. (3.1)
  • Analyse: Model and predict analytically and numerically the behaviour of a physical/biological system and understand the beneficial and deleterious consequences of scientific work. (3.2)
  • Analyse: Demonstrate creative thinking within a structured discipline by extending the principles of biomedical physics to a broader context using individual and independent learning strategies enabled by peer review and self-reflection. (4.2)
  • Apply: Illustrate scientific principles and data based on accurate scientific record keeping, using graphic and tabular means. (5.1)
  • Analyse: Organise data to construct a scientific argument demonstrating critical analysis of sources, selection of appropriate data and scientifically rigorous interpretation of results. (5.2)
  • Synthesise: Prepare and deliver an appropriate professional presentation to different audiences using a variety of media. (5.3)

Contribution to the development of graduate attributes

This subject contributes to the following aspects of the graduate attributes:

1.Disciplinary knowledge

Throughout this subject you will apply a range of measurement and analysis techniques used in contemporary physics-related industry and research. Important physical processes, such as conduction mechanisms and energy conversion and transfer will be considered as well as the manipulation and analysis of data from environmental systems. This subject will provide a firm foundation for future laboratory- and field-based experimentation. Upon completion, you will be able to harness data analysis methods in order to verify or refute models of physical processes as well as understand the principles that underpin methods of analysis.

2. Research, Inquiry and Critical Thinking

Project-based practicals form one of the key activities undertaken in this subject and will provide you with an opportunity to devise and carry out practical investigations to answer a research question.

3. Professional, Ethical and Social Responsibility

During this subject you will have numerous opportunities to apply methods of analysis of experimental data to analyse, evaluate and discriminate data obtained through the laboratory investigations. In addition, you will acquire skills in accessing information from a variety of sources including the Internet and the library and also demonstrate the capacity to work independently against deadlines.

4. Reflection, Innovation, Creativity

This graduate attribute will be developed during the major research project. You will use the methodologies studied in this subject to create and devise research studies of your own choosing. You will give and receive feedback on several drafts of your research reports.

5. Communication skills

This subject will involve preparing a report in the form of a journal article, detailing the outcomes of the research project you have undertaken. This is also presented to the class in the form of group oral presentation. The development of these key communication skills is consistent with that expected of a professional scientist in research and industry. In addition you will maintain a faithful record of work carried out in the laboratory and receive regular feedback on this activity.

Teaching and learning strategies

The first eight weeks will consist of weekly 2-hour combined lecture/tutorial sessions, followed by a 2-hour practical session. It is expected that you will have reviewed lecture notes and tutorial problems (available on UTSOnline) before attending, so face-to-face time is used for active discussion and verbal feedback on specific problem areas. Completing these questions allows you to assess your progress in applying concepts and receive informal feedback at an early stage, before developed understanding is assessed in the class test and final exam.

For the 2-hour practical sessions you will work collaboratively in small groups of 2-3 students. The labs are designed to actively develop essential practical skills and data analysis, and you are encouraged to seek informal feedback from demonstrators on skills, knowledge, and important aspects of records keeping in the form of a log book which is an assessment task for the subject.

The remainder of the semester consists of project-based practicals (4 hours per week) in which you will work in small groups to solve an authentic problem of your choosing, applying the analysis techniques covered in the lectures. You will have access to essential software outside of class so that additional development and reporting can be conducted individually as necessary. The projects are an important (and mandatory) assessment so you are strongly encouraged to seek regular informal feedback during class. The projects culminate in a group presentation of the findings, as well as a paper in the form of a journal article, utilising feedback from a peer review of a paper draft.

Content (topics)

Following the lectures, assignments, laboratory work, projects and private study, a student should be able to:

Measurement, error and uncertainty
Understand the importance of the ‘Guide to Uncertainty in Measurement’ (GUM) as the international convention for calculating and expressing uncertainties
Distinguish between Type A and Type B evaluations of uncertainty
Calculate standard uncertainty, combined standard uncertainty and expanded uncertainty
Understand coverage factor and how it is calculated

Method of Least Squares
Understand and apply technique of least squares to linear and non-linear equations
Appreciate the assumptions normally prevailing when applying least squares
Carry out weighted fitting of equations to data
Understand that not all equations can be fitted using linear least squares
Understand the principle of fitting using non-linear least squares
Carry out non-linear least squares using a computer package

Model identification
Appreciate that statistical methods are able to compare the goodness of fit of several models to data, but that ‘physical significance’ is usually the overriding consideration when choosing models
Understand and be able to apply Design of Experiments to explain the response of a physical system
Understand and be able to apply the Adjusted Multiple Coefficient of Determination
Use the Akaikes information criterion to compare models
Apply analysis of residuals to check for goodness of fit.
Determine the correlation coefficient and appreciate the limitation in the correlation coefficient as a measure of ‘goodness of fit’
Use ANOVA to determine if there is statistically significant difference between models

Measurement methods
Understand the working principles of biomedical sensors for the detection of biosignals
Understand and use equivalent circuit modelling
Understand the clinical importance of biopotentials and use different electrodes to measure and create biopotentials.

Signal Processing
Understand the mathematical differences between digital and analog signals.
Use convolution to smooth and identify peaks in signals
Carry out correlation, cross-correlation and auto-correlation analyses on signals
Understand and apply Fourier analysis to filter and analyse signals

Projects
Design a experiments to address a particular scientific or technical question. For example, how does how does hydration affect the bio-impedance measurement of body fat?
Apply appropriate of measurement methods in a number of novel situations, (for example) to study the relative accuracy of different methods of measuring ions in biofluids. Details of possible projects will be discussed in class and provided on UTSOnline.

Spreadsheet
Use Excel for exploratory and advanced data analysis including use of

  1. advanced statistical function such as NORMDIST() and LINEST()
  2. Analysis ToolPak with particular reference to the Regression Tool, ANOVA etc
  3. Matrix function such as MINVERSE() and MMULT() for solving advanced least squares problems.
  4. Solver for fitting non-linear equation to data

Assessment

Assessment task 1: Log book (continuous)

Intent:

This assessment task contributes to the following graduate attributes:
3. Professional skills and their application
5. Communication skills

Objective(s):

This assessment task addresses subject learning objective(s):

4 and 5

This assessment task contributes to the development of course intended learning outcome(s):

3.1 and 5.1

Type: Laboratory/practical
Groupwork: Individual
Weight: 15%
Criteria:

A rubric for the logbook is available on UTSOnline.

Assessment task 2: Class test

Intent:

This assessment task contributes to the following graduate attributes:
1. Disciplinary knowledge
3. Professional, Ethical and Social Responsibility

Objective(s):

This assessment task addresses subject learning objective(s):

1 and 3

This assessment task contributes to the development of course intended learning outcome(s):

1.2 and 3.1

Type: Quiz/test
Groupwork: Individual
Weight: 10%
Length:

2 hours

Criteria:

Correct application of analysis techniques. Appropriate presentation of results.

Assessment task 3: Major Project

Intent:

This assessment task contributes to the following graduate attributes:

  1. Disciplinary knowledge
  2. Research, Inquiry and Critical Thinking
  3. Professional , Ethical and Social Responsibility
  4. Reflection, Innovation, Creativity
  5. Communication
Objective(s):

This assessment task addresses subject learning objective(s):

1, 2, 3, 4, 5 and 6

This assessment task contributes to the development of course intended learning outcome(s):

1.2, 2.2, 2.3, 3.2, 4.2, 5.2 and 5.3

Type: Project
Groupwork: Group, group and individually assessed
Weight: 45%
Length:

Stage 1 Report Length: between 500 and 1000 words

Stage 2 Report Length: between 1500 and 3000 words

Workshop presentation: 10-15 minutes

Criteria:

Rubrics and further details for this assessment is available on UTSOnline.

Individual components are weighted as follows: Report Stage 1 (10%), Report Stage 2 (20%), Peer review (5%), workshop (10%).

Assessment task 4: Final Exam

Intent:

This assessment task contributes to the following graduate attributes:
1. Disciplinary knowledge
3. Professional, Ethical and Social Responsibility

Objective(s):

This assessment task addresses subject learning objective(s):

1 and 3

This assessment task contributes to the development of course intended learning outcome(s):

1.2 and 3.2

Type: Examination
Groupwork: Individual
Weight: 30%
Criteria:

Correct answers, Correct application of analysis techniques. Appropriate presentation of results.

Minimum requirements

Assessment task 3 is worth 45%, so students are required to gain at least 40% of the mark for that task. If 40% is not reached, an X grade fail may be awarded for the subject, irrespective of an overall mark greater than 50.

Recommended texts

Kirkup L Data Analysis for Physical Scientists (2012) Cambridge University Press, Cambridge

Other resources

The relevance of these sources will be indicated in the lectures.

  • Kirkup, L. & Frenkel, B. (2006) An Introduction to Uncertainty in Measurement. Cambridge University Press.
  • McPherson, G. (1990) Statistics in Scientific Investigation: Its basis, application, and interpretation. Springer-Verlag.
  • Bentley, J. P. (1988) Principles of Measurement Systems. 2nd Edition Longmans.
  • Dietrich, C. R. (1991) Uncertainty, Calibration and Probability: Statistics of Scientific and Industrial Measurement. 2nd edition Adam Hilger.
  • Arora, P. N. & Malhan, P. K. (2009) Biostatistics. Global Media.
  • Doebelin, E. O. (1995) Engineering Experimentation: Planning, Execution, Reporting. McGraw-Hill.
  • Wilks, D., (2005) Statistical Methods in the Atmospheric Sciences. Academic Press, 648pp.