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SSJ is an extensible android framework for social signal processing in an out of lab envirnoment. It packages common signal processing tools in a flexible, mobile friendly Java library which can be easily integrated into Android Apps.

• Realtime signal processing using independent components as processing steps in a pipeline
• Synchronized data streams
• Support for most standard android sensors e.g. Camera, Microphone, Acceleration, GPS
• Support for external sensors via bluetooth e.g. Microsoft Band 2, Myo, Angel Sensor, Empatica
• Advanced signal processing functionality, including machine learning approaches (NaiveBayes, SVM)
• I/O functionality: local storage, sockets, bluetooth
• Energy efficient processing thanks to advanced sleep state management and support for discrete data propagation
• Basic GUI elements: graphs (using GraphView library), camera painter
• SSJ Creator: Android App for building, editing and running SSJ pipelines without writing a single line of code

• To use libssj in your own application, simply add the gradle dependency:

compile 'com.github.hcmlab:libssj:0.5'

• You can also download the latest binaries from the releases section
• SSJ Creator can be downloaded from the play store

• White-paper: dl.acm.org | uni-augsburg.de
• Api (Javadoc): http://hcmlab.github.io/ssj/api

The Social Signal Processing for Java/Android (SSJ) framework is being developed at the Lab for Human Centered Multimedia of the University of Augsburg. The authors of the framework are: Ionut Damian, Michael Dietz, Frank Gaibler, Daniel Langerenken, Simon Flutura.

SSJ has been inspired by the SSI (http://openssi.net) framework. SSJ is not a one-to-one port of SSI to Java, it is an approximation. Nevertheless, it borrows a lot of programming patterns from SSI and preserves the same vision for signal processing which makes SSI great. It than packages everything in a flexible, mobile friendly Java library which can be easily integrated into Android Apps.

If you use SSJ for a research project, please reference the following paper:

• Ionut Damian, Tobias Baur, Elisabeth André, Measuring the Impact of Multimodal Behavioural Feedback Loops on Social Interactions, In Proceedings of International Conference on Multimodal Interaction (ICMI), ACM, 2016
  paper | BibTex | dl.acm.org

This library is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or any later version.

This library is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this library; if not, write to the Free Software Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA