Modular Middleware for Gestural Data and Devices Management

In the last few years, the use of gestural data has become a key enabler for human-computer interaction (HCI) applications. The growing diffusion of low-cost acquisition devices has thus led to the development of a class of middleware aimed at ensuring a fast and easy integration of such devices within the actual HCI applications. The purpose of this paper is to present a modular middleware for gestural data and devices management. First, we describe a brief review of the state of the art of similar middleware. Then, we discuss the proposed architecture and the motivation behind its design choices. Finally, we present a use case aimed at demonstrating the potential uses as well as the limitations of our middleware.

[1]  Brad A. Myers,et al.  The importance of percent-done progress indicators for computer-human interfaces , 1985, CHI '85.

[2]  Jan Zibuschka,et al.  MT4j - A Cross-platform Multi-touch Development Framework , 2010, ArXiv.

[3]  Johan Bas,et al.  A 3D Gesture Recognition Extension for , 2011 .

[4]  Jay William Roltgen,et al.  AQUA-G: A universal gesture recognition framework , 2010 .

[5]  Elena Mugellini,et al.  Gesture recognition corpora and tools: A scripted ground truthing method , 2015, Comput. Vis. Image Underst..

[6]  Douglas Crockford,et al.  The application/json Media Type for JavaScript Object Notation (JSON) , 2006, RFC.

[7]  Antonio Gentile,et al.  Continuous Hand Openness Detection Using a Kinect-Like Device , 2014, 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems.

[8]  Ralph Johnson,et al.  design patterns elements of reusable object oriented software , 2019 .

[9]  Alessio Malizia,et al.  Gesture recognition using low-cost devices: Techniques, applications, perspectives , 2016 .

[10]  Alessio Malizia,et al.  Touchless Interfaces For Public Displays: Can We Deliver Interface Designers From Introducing Artificial Push Button Gestures? , 2016, AVI.

[11]  Chung-Lin Huang,et al.  Hand gesture recognition using a real-time tracking method and hidden Markov models , 2003, Image Vis. Comput..

[12]  Rhadamés Carmona,et al.  An open source framework to manage kinect on the web , 2015, 2015 Latin American Computing Conference (CLEI).

[13]  Tae-Seong Kim,et al.  Hand Gesture Recognition and Interface via a Depth Imaging Sensor for Smart Home Appliances , 2014 .

[14]  Fabrizio Milazzo,et al.  Short-Term Sensory Data Prediction in Ambient Intelligence Scenarios , 2014, Advances onto the Internet of Things.

[15]  Beat Signer,et al.  iGesture: A General Gesture Recognition Framework , 2007 .

[16]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[17]  Liang Hu,et al.  A Data Processing Middleware Based on SOA for the Internet of Things , 2015, J. Sensors.

[18]  Radovan Stojanovic,et al.  Wheelchair maneuvering using leap motion controller and cloud based speech control: Prototype realization , 2015, 2015 4th Mediterranean Conference on Embedded Computing (MECO).

[19]  Zenon Chaczko,et al.  Haptic Middleware Based Software Architecture for Smart Learning , 2015, 2015 Asia-Pacific Conference on Computer Aided System Engineering.

[20]  Stephen B. Gilbert,et al.  Sparsh UI: A Multi-Touch Framework for Collaboration and Modular Gesture Recognition , 2009 .

[21]  Mark Lycett,et al.  Service-oriented architecture , 2003, 2003 Symposium on Applications and the Internet Workshops, 2003. Proceedings..

[22]  Fariba Sadri,et al.  Ambient intelligence: A survey , 2011, CSUR.

[23]  Meinard Müller,et al.  Dynamic Time Warping , 2008 .

[24]  Simon Fong,et al.  Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm , 2015, J. Sensors.

[25]  Elena Mugellini,et al.  ARAMIS: Toward a Hybrid Approach for Human- Environment Interaction , 2011, HCI.

[26]  Alessio Malizia,et al.  Designing Touchless Gestural Interactions for Public Displays In-the-Wild , 2015, HCI.

[27]  Giuseppe Lo Re,et al.  Adaptable data models for scalable Ambient Intelligence scenarios , 2011, The International Conference on Information Networking 2011 (ICOIN2011).