Christine Martindale

Christine Martindale, M. Sc.

Alumnus

Department Artificial Intelligence in Biomedical Engineering (AIBE)
Lehrstuhl für Maschinelles Lernen und Datenanalytik

Room: Room 01.012
Carl-Thiersch-Straße 2b
91052 Erlangen

Research Focus

Objective health data about subjects outside of the laboratory is important in order to analyse symptoms that cannot be reproduced in the laboratory environment. This health data needs to also be clinically applicable and accurate in order to use it for ‘in the wild’ athlete analysis and medical analysis. In order to achieve this large and diverse data sets of human motion recorded using unobtrusive, wearable sensors need to be collected and annotated, as well as generic or easily adaptable algorithms for processing the data need to be developed. Therefore my research focuses on collecting and analyzing diverse and realistic data sets of human motion where ground truth data on a stride level is available, developing smart annotation methods in order to reduce the labeling cost of large data sets, developing generic recognition algorithms which are capable of clinically relevant annotation and testing the applicability of common machine learning algorithms on heterogeneous gait data, specifically gait data from spastic patients.

 

  • Machine learning in mobile gait analysis, especially of patients with spastic gait
  • Semi-supervised methods for annotation of daily activity data for accurate gait analysis, or repetition analysis