Green multimedia: informing people of their carbon footprint through two simple sensors

In this work we discuss a new, but highly relevant, topic to the multimedia community; systems to inform individuals of their carbon footprint, which could ultimately effect change in community carbon footprint-related activities. The reduction of carbon emissions is now an important policy driver of many governments, and one of the major areas of focus is in reducing the energy demand from the consumers i.e. all of us individually. In terms of CO2 generated from energy consumption, there are three predominant factors, namely electricity usage, thermal related costs, and transport usage. Standard home electricity and heating sensors can be used to measure the former two aspects, and in this paper we evaluate a novel technique to estimate an individual's transport-related carbon emissions through the use of a simple wearable accelerometer. We investigate how providing this novel estimation of transport-related carbon emissions through an interactive web site and mobile phone app engages a set of users in becoming more aware of their carbon emissions. Our evaluations involve a group of 6 users collecting 25 million accelerometer readings and 12.5 million power readings vs. a control group of 16 users collecting 29.7 million power readings.

[1]  Alex Pentland,et al.  InSense: Interest-Based Life Logging , 2006, IEEE MultiMedia.

[2]  D. Bassett,et al.  Comparison of four ActiGraph accelerometers during walking and running. , 2010, Medicine and science in sports and exercise.

[3]  Alan F. Smeaton,et al.  Interaction platform-orientated perspective in designing novel applications , 2009 .

[4]  Horace Herring,et al.  Energy efficiency—a critical view , 2006 .

[5]  Dermot Diamond,et al.  Smart nanotextiles: materials and their application , 2010 .

[6]  Angelo M. Sabatini,et al.  Machine Learning Methods for Classifying Human Physical Activity from On-Body Accelerometers , 2010, Sensors.

[7]  Cassidee Shinn,et al.  Greenhouse gas inventory report , 2009 .

[8]  Alan F. Smeaton,et al.  Everyday concept detection in visual lifelogs: validation, relationships and trends , 2010, Multimedia Tools and Applications.

[9]  Paola Petroni,et al.  The new edge for the Enel telegestore: An integrated solution for the remote management of electricity and gas distribution allowing a total management of the energy consumptions , 2009 .

[10]  Jun Yang,et al.  Toward physical activity diary: motion recognition using simple acceleration features with mobile phones , 2009, IMCE '09.

[11]  Michael L. Littman,et al.  Activity Recognition from Accelerometer Data , 2005, AAAI.

[12]  Noah J. Goldstein,et al.  The Constructive, Destructive, and Reconstructive Power of Social Norms , 2007, Psychological science.

[13]  N. Lior,et al.  Advances in energy studies , 2009 .

[14]  Anita L. Allen,et al.  Dredging Up the Past: Lifelogging, Memory and Surveillance , 2007 .

[15]  Shahram Izadi,et al.  SenseCam: A Retrospective Memory Aid , 2006, UbiComp.

[16]  Alan F. Smeaton,et al.  Smart tablecloths - ambient feedback of domestic electricity consumption , 2010 .

[17]  P. E. Morris,et al.  Practical aspects of memory : current research and issues , 1988 .