Home People Cenedese Research Time series analysis

Timeseries Analysis, Gesture Recognition and Classification



Angelo Cenedese – Assistant Professor at the Department of Information Engineering - University of Padova


Gian Antonio Susto – Research Fellow at the Department of Information Engineering - University of Padova



Nowadays, pervasive networking of sensors and actuators has definitely changed our way of interacting with the environment, thanks to the advances in technology and novel paradigms in distributed system theory as well as in information and coding theory: indeed these devices can offer access to an unprecedented quality and quantity of information that can revolutionize our ability in controlling the human space.

Timeseries analysis for the Smart City (SC)

The analysis of data to monitor human-related activities plays a crucial role in the development of smart policies to improve well being and sustainability of our cities. For several applications in this context anomalies in time series can be associated to smaller timeframes such as days or weeks.
We developed a consensus-based anomaly detection approach that exploits the power of the Symbolic Aggregate approXimation (SAX) and the specificity of such time series. More in details, a model for the expected behavior, composed by one or more significant patterns, is built from the data stream. The model can be saved and used for subsequent analysis. Frames far enough from the model are reported as anomalies. An optional hierarchical classification of the retrieved anomalies can help to uncover common underlining causes.


Gesture recognition (GR)

Within the context of Home Automation, the design of man-machine interfaces have assumed a central role for the development of smart environments. In this respect, the interaction based on gestures measured through inertial devices represents a fascinating and interesting solution thanks to a new generation of ubiquitous technologies that allow to pervasively and seamlessly control the human space.

This research line regards a Machine Learning (ML) approach to gesture recognition (GR), in its main aspects of (i) event identification, (ii) feature extraction and (iii) classification: in detail, an informative and compact representation of the gesture input signals is defined, using both feature extraction and the analysis in the time domain through signal warping, a pre-processing phase based on Principal Component Analysis is proposed to increase the performance in real-world scenario conditions, and, finally, parsimonious classification techniques based on Sparse Bayesian Learning are designed and compared with more classical ML algorithms.

These contributions yield the definition of a system that is user independent, device independent, device orientation independent, and provides a high classification accuracy.