• The students will have knowledge concerning Applied Motions Analysis and AI based analysis approaches in the field of AAL. Further they will have knowledge concerning fundamentals of motion analysis, kinematic and kinetic motion analysis and selected approaches in the field of Mobile Health.
• The students will be familiar with selected approaches of applied Machine Learning (pre-processing, feature selection and extraction, classification) in the field of Motion Analysis. They will have knowledge concerning practical implementation and deployment chain on mobile solutions.

None

ML basic methods in the field of AAL:
• Detailed Introduction to applied Machine Learning in the field of ADL recognition (classification chain, data gathering and annotation strategies, pre-processing (PCA), supervised learning strategies (tree, Bayesian MAP, KNN), (Descriptive) Evaluation of results)
• Practical implementation and deployment chain on Smart Home solutions
Application of ML methods in the field of AAL:
• Fundamentals of Motion analysis and Mobile Health in the field of AAL. Concepts and technological approaches and relevant enabling technologies (sensor technologies, pre-processing, signal analysis, classification, networks and transmission, etc.).
• Fundamentals of AAL relevant Biomechanics (static and dynamic)
• Detailed Introduction to applied Machine Learning in the field of Motion Analysis (classification chain, data gathering and annotation strategies, pre-processing (PCA), supervised learning strategies (MAP, SVM), (Descriptive) Evaluation of results)
• Practical implementation and deployment chain on mobile solutions

• Lecture script as provided in the course (required)
• C.M. Bishop: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer, 2007
• M. Paluszek: MATLAB Machine Learning. Springer ,2017
• D. Knudson: Fundamentals of Biomechanics. Springer, 2nd ed. 2017
• C.-M. Kyung et al.Smart Sensors and Systems - Innovations for Medical, Environmental, and IoT Applications. Springer, 2017

Integrated course - teaching & discussion, guest lectures by specialists, demonstration, exercises and practical examples in the lab, home work

Immanent examination character: presentation, assignment reports, written/oral exam