Examination arrangement

Course content

The course will provide an introduction to signal analysis in Matlab. The course will include selected topics within signal processing like digital data sampling, signal-to-noise ratio, data filtering, selection of time periods, and calculation of relevant signal characteristics. The course will include introduction to basic operations in Matlab such as importing and exporting data, data visualization, programming of simple logical structures, use of functions, indexing of data vectors and matrices, and batch processing of larger data sets. The course will use exercises and examples from GPS, accelerometer, force, 3D motion, NIRS and electrophysiological data (EMG, EEG and / or EKG) to show the use and interpretation of signal analysis in movement science

Learning outcome

After completing the course, the student is able to…
• Explain important aspects of signal processing like data sampling, signal-to-noise ratio, data filtering and selection of time periods, and how these factors affect calculation of relevant signal characteristics.
• Explain important aspects of data management in Matlab such as file names and folder structure
• Apply basic operations in Matlab such as importing and exporting data, data visualization, programming simple logical structures, indexing data vectors and matrices, and batch processing large data sets.
• Create a Matlab script that can read the data, improve data quality, visualize results and compute relevant signal characteristics of various signals relevant in movement science
• Interpret different signals (GPS, accelerometer, force, 3D motion, NIRS and electrophysiological data) on the basis of relevant theories
• Perform signal analysis using basic operations in Matlab.
• Explain central concepts of signal processing
• Understand the importance of various aspects in signal analysis for data quality and further statistical analysis

Learning methods and activities

The course contains eight meetings of up to 5 hours which includes some lectures, but most practical exercises in Matlab. Students are expected to work on assigned tasks between the meetings and participate in all activities. The course is taught in English when required and all communications must be in English if international students are enrolled.

Compulsory assignments

• 70% attendance meetings

Specific conditions

Recommended previous knowledge

BEV3102, BEV3103, BEV3023, BEV3024, BEV3004

Required previous knowledge

As for admission to the master programmes in medicine, health and social sciences. The course is reserved for students enrolled at master programmes in medicine, health and social sciences.

Credit reductions