32,640 research outputs found

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

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    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

    Mobile information access in the real world: A story of three wireless devices

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    This is the post-print version of the Article. The official published version can be accessed from the link below - Copyright @ 2008 ElsevierThe importance of the user perspective to the wireless information access experience cannot be understated: simply put, users will not indulge in devices that are perceived to be difficult to use and in technologies that do not offer quality infotainment – combined information and entertainment – content. In this paper, we investigate the impact that mobile devices have on the user wireless infotainment access experience in practice. To this end, we have undertaken an empirical study placed in a ‘real-world’ setting, in which participants undertook typical infotainment access tasks on three different wireless-enabled mobile devices: a laptop, a personal digital assistant and a head mounted display device. Results show that, with the exception of participants’ level of self-consciousness when using such devices in public environments, the user wireless information access experience is generally unaffected by device type. Location was shown, though, to be a significant factor when users engage in tasks such as listening to online music or navigation. Whilst the interaction between device and environment was found to influence entertainment-related tasks in our experiments, the informational ones were not affected. However, the interaction effects between device and user type was found to affect both types of tasks. Lastly, a user’s particular computing experience was shown to influence the perceived ease of wireless information access only in the case of online searching, irrespective of whether this is done for primarily informational purposes or entertainment ones

    Towards a Practical Pedestrian Distraction Detection Framework using Wearables

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    Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices
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