Comprehending low-dimensional manifolds of temporal data from the home

Title Comprehending low-dimensional manifolds of temporal data from the home
Summary Study and development of tools and methods for the visualization of (temporal) human activity patterns.
Keywords Visualization, Dimensionality Reduction, Manifold learning
References Maaten, L. V. D., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579-2605.

Lundström, J., Järpe, E., & Verikas, A. (2016). Detecting and exploring deviating behaviour of smart home residents. Expert Systems with Applications, 55, 429-440.

Rauber, P. E., Falcão, A. X., & Telea, A. C. (2016). Visualizing time-dependent data using dynamic t-SNE. Proc. EuroVis Short Papers, 2(5).

Cheng, J., Liu, H., Wang, F., Li, H., & Zhu, C. (2015). Silhouette analysis for human action recognition based on supervised temporal t-sne and incremental learning. IEEE Transactions on Image Processing, 24(10), 3203-3217.

Prerequisites Completed courses in basic machine learning is required.
Supervisor Jens Lundström, Eric Järpe, Rebeen Hamad
Level Master
Status Open

Data generated from sensors deployed in an in-home setting can be processed to characterize human activity patterns. These patterns can further be used for smart home services such as fall detection, task reminders and messages for the motivation of exercise. Despite the direct use of activity patterns there are few tools and methods available for the visual interpretation, especially the temporal variations in high-dimensional human activity patterns lack proper visualization.

Researchers in the area of Smart Homes (at the ISDD department) study and develop methods for modeling human activity patterns and now calls for student(s) to perform master thesis work in the area of model comprehensibility by studying methods for visualization. As a student in this project you are expected to be working by four suggested work packages:

1. Background study on visualization of human activity patterns and related research. 2. Practical tests on visualization methods on medium size datasets. 3. Investigation (test, development and validation) on how spatio-temporal components can be (better) visualized on real-life datasets. The result is expected to include investigation results and conclusions on how high-dimensional human activity patterns could be visualized for better interpretation.