Smart Annotation using semi-supervised techniques
Project leader: Björn Eskofier
Project members: Christine Martindale
Start date: 1. February 2015
End date: 30. January 2019
Abstract
Objective health data about subjects outside of the laboratory is important in order to analyse symptoms that cannot be reproduced in the laboratory. A simple daily life example would be how stride length changes with tiredness or stress. In order to investigate this we must be able to accurately segment a stride from daily living data in order to have an accurate measure of duration and distance. State-of-the-art methods use separate segmentation and classification approaches. This is inaccurate for segmentation of an isolated activity, especially one that is not repeated. This could be solved using a model that is based on the sequence of phases within activities. Such a model is a graphical model. Currently we are working with Conditional Random Fields and Hierarchical Hidden Markov Models on daily living data. The applications will include sports as well as daily living activities.
Publications
- Martindale C., Hönig FT., Strohrmann C., Eskofier B.:
Smart Annotation of Cyclic Data Using Hierarchical Hidden Markov Models
In: Sensors 17 (2017)
ISSN: 1424-8220
DOI: 10.3390/s17102328
URL: http://www.mdpi.com/1424-8220/17/10/2328
BibTeX: Download - Martindale C., Wirth M., Schneegas S., Zrenner M., Groh B., Blank P., Schuldhaus D., Kautz T., Eskofier B.:
Workshop on wearables for sports
2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Heidelberg, 12. September 2016 - 16. September 2016)
In: 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing 2016
DOI: 10.1145/2968219.2968583
BibTeX: Download - Martindale C., Roth N., Hannink J., Sprager S., Eskofier B.:
Smart Annotation Tool for Multi-sensor gait based daily activity data
In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) 2018
DOI: 10.1109/PERCOMW.2018.8480193
URL: https://www.mad.tf.fau.de/files/2018/09/percom2018_martindale.pdf
BibTeX: Download