Students will learn various techniques for the analysis of time series. A brief sketch of the mathematical background will enable students to select and apply proper methods for the study of signals typically found in the movement sciences. As examples range from kinematic and (neuro-) physiological signals students will get well-equipped to analyze and interpret their own experimental recordings.
Course Description
Recent advances in recording techniques and increasing data storage capacity render time series analysis a challenge. In this course various uni-, bi-, and multivariate methods for the study of experimental data will be outlined and critically discussed. Statistical time-domain approaches go hand in hand with Fourier analysis, Hilbert and Gabor transforms, wavelet decomposition, et cetera. For the multivariate extension primary focus will be on principal and independent component analysis and on investigating recordings of whole-body kinematics and electromyographic signals. All techniques will be discussed based on current research articles and implemented by means of numerical exercises (Matlab).
Study Characteristics
- Discipline: Human Movement Sciences: Sport, Exercise and Health (Research)
- Type of education: Seminar, Lecture, Computer lab
- Academic skill: N/A
- Graduate School: N/A
- Start date: TBD
- End date: TBD
- Minimum number of students: N/A
- Maximum number of students: N/A
- Admission criteria: Contact the course coordinator for information on admission criteria: Prof. Dr. A. Daffertshofer
- Concluding assessment: N/A
- Assessment type: N/A
- With Certificate: N/A
- Schedule info: A mixture of lectures, seminars, and computer practicals. At the computer students will analyze typical examples of movement-related, temporal data like kinematic or electromyographic signals. During the seminars, research articles on the analysis of movement dynamics will be discussed on the basis of brief summaries written by the students (writing assignment).
- Number of lessons: 14 seminars, 12 practicals, 10 lectures
- Total course duration in hrs.: 36 contact hours (14 seminars, 12 practicals, 10 lectures); 124 hours self-study
- Sign up period: N/A
- Anticipated hrs of study: 124 hours
- Available to: PhD students VU (and VU RMA students)
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Course Description & Study Characteristics
Course Description
Recent advances in recording techniques and increasing data storage capacity render time series analysis a challenge. In this course various uni-, bi-, and multivariate methods for the study of experimental data will be outlined and critically discussed. Statistical time-domain approaches go hand in hand with Fourier analysis, Hilbert and Gabor transforms, wavelet decomposition, et cetera. For the multivariate extension primary focus will be on principal and independent component analysis and on investigating recordings of whole-body kinematics and electromyographic signals. All techniques will be discussed based on current research articles and implemented by means of numerical exercises (Matlab).
Study Characteristics
- Discipline: Human Movement Sciences: Sport, Exercise and Health (Research)
- Type of education: Seminar, Lecture, Computer lab
- Academic skill: N/A
- Graduate School: N/A
- Start date: TBD
- End date: TBD
- Minimum number of students: N/A
- Maximum number of students: N/A
- Admission criteria: Contact the course coordinator for information on admission criteria: Prof. Dr. A. Daffertshofer
- Concluding assessment: N/A
- Assessment type: N/A
- With Certificate: N/A
- Schedule info: A mixture of lectures, seminars, and computer practicals. At the computer students will analyze typical examples of movement-related, temporal data like kinematic or electromyographic signals. During the seminars, research articles on the analysis of movement dynamics will be discussed on the basis of brief summaries written by the students (writing assignment).
- Number of lessons: 14 seminars, 12 practicals, 10 lectures
- Total course duration in hrs.: 36 contact hours (14 seminars, 12 practicals, 10 lectures); 124 hours self-study
- Sign up period: N/A
- Anticipated hrs of study: 124 hours
- Available to: PhD students VU (and VU RMA students)
Would you like to register or want to know more?
Please contact the course coordinator prof. dr. A. Daffertshofer: