Seminar: Sensor Data Compression and Power Management for Activity Recognition
The aim of the project is to design an energy-efficient context-aware strategy for activity recognition using sparse sampling and sensor data compression. Prototyping will be done in Python. Evaluation will be performed on free-living dataset and benchmarked in terms of energy/memory savings, time constraints and quality of information retrieved. Machine learning techniques will be applied for pattern recognition.
Learning Objectives
- Gain overview on the state-of-the-art of software-based sparse sampling and signal compression for wearable devices.
- Learn concepts of data compression.
- Learn concepts of sparse sampling.
Course data
| ECTS | 2.5, 5, 7.5, default: 5 |
| Project type | Seminar, Extension to BSc.MSc.-Thesis can be discussed |
| Language | English |
| Presence time | 4 SWS |
| Work distribution | 25% Theory, 75% Programming |
| Useful knowledge | Python programming, machine learning basics |
| Period | Summer semester 2019 |
| First meeting | Seminar introduction/Vorbesprechung on 24. Apr 2019, 17:00-18:30 at Henkestr. 91, Haus 7, 1. OG, R 373 |
| Med. Eng. Seminar Title | Advanced Context Recognition (ACR) |
Contact
Dr. Giovanni Schiboni
- Job title: Researcher
- Phone number: +49 9131 85-23604
- Email: giovanni.schiboni@fau.de