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ADHD-ML – Machine Learning for Detecting Hyperactivity in Children

ADHD-ML – Machine Learning for Detecting Hyperactivity in Children

In the project ADHS ML machine learning processes are used to predict hyperactivity of children with the help of accelerometers.

Project Description

Up to now it is common to investigate hyperactivity of children via magnetic resonance imaging (MRI). The recorded images are used for gaining a classifier to make a unique diagnosis. Although the classification rates are very promising the method implies disadvantages concerning temporal and financial costs. In addition, the survey of hyperactivity cannot take place in every-day surroundings. To capture this, there are many common and low cost alternatives such as accelerometers, in contrary to the expensive and resource intense MRI experiments. Especially, accelerometers play a major role in the classification of physical activities of people, because they can largely automatise the observation of patients in the context of health research.

Objectives

  1. Identification of hyperactivity regarding children
  2. Survey of hyperactivity in everyday-surroundings
  3. Using intelligent methods of machine learning

Methods

The project aims at investigating the identification of hyperactivity regarding children. The measurements of children with accelerometers in different scenarios serve as data sets (e.g., playing cards). We are using intelligent methods of machine learning, especially support-vector- machines. In contrary to Johnson et al. (2012), our approach offers a low cost and easy possibility to detect hyperactivity that does not limit the child’s degree of freedom.

Funding

LOEWE-Logo

Selected Publications

Brefeld, U., & Scheffer, T. (2206). Semi-supervised Learning for Structured Output Variables. Proceedings of the International Conference on Machine Learning.

Brefeld, U., Büscher, C., & Scheffer, T. (2005). Multi-view Discriminative Sequential Learning. Proceedings of the European Conference on Machine Learning.

Fernandes, E. R., & Brefeld, U. (2011). Learning from Partially Annotated Sequences. Proceedings of the European Conference on Machine Learning.

Further Information

Website: IDeA Center

Project management

Project details

State:
Completed projects
Project type: Interdepartmental projects
Duration:
2013 - 2014
Funding:
External funding
Departments:
last modified Jun 29, 2016