Proposing Faculty Performance Monitoring Scale (FPMS) as an Application of Ambient Intelligence

Ambient Intelligence in the modern era of research and innovations is an active field that uses various embedded computing devices for providing different types of interaction with environment. This paper describes a new methodology “Faculty Performance Monitoring Scale” abbreviated as “FPMS” aiming to improve the overall quality of existing education system utilizing an application of ambient intelligence. As designed, the methodology elaborates the mutual interaction among humans and service robotics having master-slave relationship in which humans act as a master and service robotics act as a slave. Human utilizes interface embedded in service robotics for giving instruction and service robotics will respond according to the instruction given by the master. The primary duty of service robotics is to store and monitor the faculty members at the time of lecture delivery and calculate their performance on the basis of certain parameters as considered in FPMS. The benefit to utilize this designed methodology is to reduce conflicts among faculty based upon biases’ and appreciation will be given on the basis of feedback from FPMS. An appropriate collaboration between human and service robotics leads to achieve a joint action and provides a mechanism of active team participation utilizing a more natural form of interactions. The proposed architecture design has been compared with the current education system and may be followed by the educational institutes for maintaining efficiency as the case thereof.

[1]  Jesús Fontecha,et al.  Ambient Intelligence: technological solutions for wellness and supporting to daily activities , 2012 .

[2]  Kristrun Gunnarsdottir,et al.  Ambient intelligence : a narrative in search of users (discussion paper) , 2011 .

[3]  Kusum Gupta,et al.  Ambient Intelligence in Ubiquitous Robotics , 2011 .

[4]  Rimmy Chuchra,et al.  Synergetic Interaction among Humans and Robotics by Proposing Communication Flow Methodology , 2015 .

[5]  Ben Kröse,et al.  A User-Interface Robot for Ambient Intelligent Environments , 2003 .

[6]  Werner Weber Ambient intelligence: industrial research on a visionary concept , 2003, ISLPED '03.

[7]  Ben J. A. Kröse,et al.  Lino, the User-Interface Robot , 2003, EUSAI.

[8]  Rimmy Chuchra,et al.  Human Robotics Interaction ( HRI ) based Analysis – using DMT , 2014 .

[9]  William J. Rapaport,et al.  Recent and Current Artificial Intelligence Research in the Department of Computer Science SUNY at Buffalo , 1986, AI Mag..

[10]  Matt Duckham,et al.  Ambient spatial intelligence for sustainable cities , 2009 .

[11]  Vic Grout,et al.  User Modelling in Ambient Intelligence for Elderly and Disabled People , 2008, ICCHP.

[12]  Tom Gross,et al.  Towards a new human-centred computing methodology for cooperative ambient intelligence , 2010, J. Ambient Intell. Humaniz. Comput..

[13]  William J. Rapaport,et al.  Recent and current artificial intelligence research in the Department of Computer Science, State University of New York at Buffalo , 1986 .

[14]  Alessandro Saffiotti,et al.  PEIS ecologies: ambient intelligence meets autonomous robotics , 2005, sOc-EUSAI '05.

[15]  Matthias Rauterberg,et al.  Responsive environments: User experiences for ambient intelligence , 2010, J. Ambient Intell. Smart Environ..

[16]  Fillia Makedon,et al.  Multimodal interaction in ambient intelligence environments using speech, localization and robotics , 2013 .

[17]  Gerhard Goos,et al.  Ambient Intelligence , 2015, Lecture Notes in Computer Science.

[18]  A. Florea,et al.  SOLVING EXPERIMENT REPRODUCIBILITY IN AMBIENT INTELLIGENCE , 2014 .