Chakaveh Ahmadizadeh: Katalogdaten im Herbstsemester 2024 |
| Name | Frau Dr. Chakaveh Ahmadizadeh |
| Adresse | Biomedical and Mobile Health Tech ETH Zürich, BAA C 307.1 Lengghalde 5 8008 Zürich SWITZERLAND |
| chakaveh.ahmadizadeh@hest.ethz.ch | |
| Departement | Gesundheitswissenschaften und Technologie |
| Beziehung | Dozentin |
| Nummer | Titel | ECTS | Umfang | Dozierende | |||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 376-1176-00L | Wearable and Mobile Technologies of the Future - Focus on Sports and Health  | 4 KP | 3G | C. Menon, C. Ahmadizadeh, C. Otesteanu | |||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The course introduces concepts about wearable and unobtrusive technologies for sport and health data collection. Tools for analysing this data and evaluating the performance of these systems are introduced. Topics such as monitoring movement and muscle activation, mental focus, stress, and heart health are covered. | ||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Objective 1: Acquire knowledge about wearable and unobtrusive technologies and skills to process data acquired using wearable technologies. Objective 2: Provide students with hands-on experience using various wearable technologies, including textile-based stretchable sensors, electromyography (EMG), electroencephalogram (EEG) and electrocardiogram (ECG). | ||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | The course consists of the following modules: Module 0: Fundamentals This module briefly reviews time-frequency domains, transforms and filtering. Module 1: Movement. This module provides the scientific background needed to understand the principles that current technologies investigating movement rely on. It provides an overview of wearable technologies for monitoring overall body movement as well as for tracking motion parameters (e.g. gait, running, posture). Technologies such as inertial measurement units, electromyography, optical motion capture systems, and others are covered. The latest updates on wearable-related (e.g., textile-based) technologies are also introduced. Students are involved in labs/assignments to put into practice the knowledge and skills acquired during this module. Module 2: Brain. This module briefly introduces brain functions and provides an overview of related wearable technologies. Topics covered may include electroencephalograms, transcranial magnetic stimulation, and galvanic vestibular stimulation. Data processing techniques such as common spatial patterns, principal component analysis, and linear discriminant analysis are introduced. Students are given a practical assignment to put into practice the knowledge and skills acquired during this module. Module 3: Cardiac. This module focuses on wearable technologies for monitoring cardiac health. The module starts by providing information on cardiac physiology and introduces wearable technologies for the heart. Topics such as electrocardiography, photoplethysmography, light sensing, and contactless physiology monitoring are introduced. Data analysis techniques for cardiac data including heart-rate variability, Poincare plots, and wavelet transforms are discussed. Students will be given an assignment to put into practice the knowledge and skills acquired during this module. | ||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | - Students should be proficient in programming (any language). - Note: assignments will be in Python. Students are expected to learn Python on their own if not experienced with this programming language. - Course prerequisites: For Biomedical Engineering Master’s: none For ITET Master’s: none For D-MAVT Master’s: none For D-HEST Master’s and PhD students: • If BSc in electrical/mechanical engineering or computer science: none • If any other BSc program: 376-0022-00L (Imaging and Computing in Medicine) or 376-1983-00L (Foundations of Data Science) | ||||||||||||||||||||||||||||||||||||||||||||
Kompetenzen |
| ||||||||||||||||||||||||||||||||||||||||||||