En Plein Air: A Mobile Learning Approach for Sustainability Education in the Wild

En Plein Air: A Mobile Learning Approach for Sustainability Education in the Wild

Leonardo Giusti (MIT, USA), Alessandro Pollini (International Telematic University UNINETTUNO, Italy), Liselott Brunnberg (MIT, USA) and Federico Casalegno (MIT, USA)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/jmhci.2012040104
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This paper discusses educational challenges and design opportunities concerning the use of mobile technologies in the context of education on sustainable development. The discussion will be supported by the presentation of a pedagogical model and a technological platform consisting of Web and mobile technologies designed to support a mix of formal and informal, indoor and outdoor learning experiences. In particular, the platform is a reconfigurable system that can be adapted to support different kinds of learning formats. The paper presents two use cases and the authors discuss the implications of mobile technologies in the field of education for sustainable development, taking into consideration both pedagogical and technological issues.
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The impact of physical activity on health is well documented. Physical inactivity, for example, may contribute to the onset of chronic diseases, such as heart disease and diabetes, as well as conditions such as overweight and obesity that may exacerbate a host of health problems. Because of the important relationship between physical activity and health, the medical and exercise science research communities are in need of better tools to study how and when people engage in physical activity and/or sedentary behaviors.

Early physical activity measurement studies used self-administered or interviewer-administered questionnaires to collect detailed information about both occupational and leisure-time activities. Self-report methods have been conducted using a variety of diaries, logs and more recently mobile devices such as smart phones where participants record their activities on an hourly, daily or weekly basis. This approach suffers from a number of limitations, including burden and inconvenience in entering data, poor compliance, and inaccuracy due to bias or poor memory.

Objective tools that use accelerometers for measuring activities have been developed in research labs as well as in commercial products. Typically, participants in a study are asked to wear a motion monitor at the hip. The Actigraph GT1M and GT3X (http://www.theactigraph.com) activity monitors are widely used by medical researchers and clinicians. These and similar devices measure two or three-axis acceleration in a band-limited frequency. An analog to digital converter samples the data, typically at 10Hz or above, and these values are then filtered and integrated using proprietary algorithms over a specific time period (epoch), usually 1 s or 1 min. This “activity count” is then mapped to energy expenditure (e.g., VO2 in ml/kg/min or MET).

One limitation of this approach is that raw acceleration data is often not saved due to memory limitations on the device. Raw data contains additional information that might be used to discriminate between activity types or to more accurately estimate energy expenditure (Rothney, Neumann, Beziat, & Chen, 2007). Another limitation is that acceleration at the hip may not reflect overall body motion for some activities such as bicycling, and therefore monitors tend to systematically under-estimate or over-estimate energy expenditure for specific types of activities where hip motion does correlate well with overall energy expenditure (Albinali, Intille, Haskell, & Rosenberger, 2010).

Researchers in exercise science are increasingly interested in systems that can collect raw accelerometer data (Zhang, Werner, Sun, Pi-Sunyer, & Boozer, 2003). Within ubiquitous and wearable computing, much work has been proposed to detect activity type from wearable accelerometers using features computed from the raw accelerometer signal (versus summarized activity counts output by most motion monitors) (e.g., Bao & Intille, 2004; Lukowicz et al., 2003). Work has also shown that the raw signal may be useful for computing energy expenditure (Albinali et al., 2010; Rothney et al., 2007). New commercial monitors, such as the GT3X+ (Actigraph) (Figure 1) can collect multiple days of raw data for such analyses. Moreover, some research suggests that activity type and activity energy expenditure estimate may be improved by using more than a single sensor located at the hip (Albinali et al., 2010).

Figure 1.

An Actigraph GT3X+ (right) in comparison to a Wocket


In this position paper, we introduce the Wockets accelerometer system, which has been designed to allow for continuous, 24/7 collection of raw accelerometer data from multiple body locations simultaneously, where the hardware and software technology can enable population-scale, longitudinal measurement of physical activity and sedentary behavior using common mobile phones. The fast processor and wireless capabilities of common mobile phones make it possible to use these devices with Wockets placed on the upper and lower parts of the body for accurate measurement of physical activity type, intensity and bout duration.

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