Data Science for Wearables (Sommersemester 2024)

Lecturer: Prof. Dr. Bert Arnrich (Digital Health - Connected Healthcare) , Orhan Konak (Digital Health - Connected Healthcare)

General Information

• Weekly Hours: 4
• Credits: 6
• Graded: yes
• Enrolment Deadline: 01.04.2024 - 30.04.2024
• Examination time §9 (4) BAMA-O: 08.07.2024
• Teaching Form: Lecture / Exercise
• Enrolment Type: Compulsory Elective Module
• Course Language: English

Programs, Module Groups & Modules

Description

Course Content

1. Introduction to Data Science for Wearables: Covering essential data science principles and their application in analyzing time-series data from wearables. This includes an overview of wearable technology's role in health and fitness, alongside statistical foundations for robust data analysis.
2. Statistical Data Analysis and Experimentation: Focusing on designing statistically valid empirical data collection methods with wearables, including conducting experiments and achieving accurate statistical test results.
3. Handling Time-Series Data: Techniques for managing time-series data challenges, such as imputation for missing data and dimensionality reduction, to simplify analysis without losing critical information.
4. Feature Engineering and Machine Learning Basics: Introducing feature extraction methods from raw data and transitioning to machine learning, specifically for tasks like classification and pattern recognition in wearable sensor data.
5. Practical Application with Wearables: Empirical experimentation with wearable devices to apply covered theories in real-world scenarios, enhancing learning through hands-on experience. No prior knowledge required; the course caters to all levels, providing necessary background knowledge.

Please find more detailed information here

 

Examination

The final grade is composed of three equal parts:

• Experimental data collection and data analysis: 1/3
• Technical report: 1/3
• Presentation: 1/3

Dates

Mondays 13:30pm &
Tuesdays 9:15-10:45am
in G2.U10/14

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