Data captured by body worn sensors provides an excellent opportunity for assessing the physical activity of patients and hence creating behavioral profiles over time. Particularly patients with chronic disease can receive tailored advice on how to increase their activity and hence improve their overall life quality.
The focus and challenge for this project and master thesis is the selection, implementation and improvement of pattern recognition and data mining techniques to identify different types of activities such as sitting, standing, walking, running, biking, etc within sensor data. The data will be provided by NTNUs medical faculty (DMF), while this thesis should focus on the data analysis. The captured data sets will be streaming data from six acceleration sensors recorded at 100 Hz. The sensor system also include recording of heart rate.
During this work, the state of the art for algorithms should be described, the most fitting algorithms selected and implemented. The implementation will be evaluated in collaboration with DMF, who is also providing background information on the data.
Once the experimental set up is created, different existing algorithms should be evaluated and their strength and weaknesses pointed out. Based on this analysis, a follow-up master thesis can be defined focusing on improving existing algorithms and validated in a real world setting.
Sketch for the project thesis
Sketch for a follow-up master thesis
For more information on this thesis, feel free to contact Kerstin Bach (email@example.com).