This paper examines a novel technique for measuring the light illuminance and compares it to existing apps and light meters. While there is some information on statistical modeling and estimation techniques as well as filtering techniques, little information is available on measuring illuminance as needed accessibility evaluations (Das et all., 2011 Jones et al., 1941). Building accessibility evaluations, however, use illuminance measured in foot candles or lux (Bakker et al., 2004). Most light meters apps have been developed for the purposes of photography and therefore measure luminance (Komaee, A., 2010 Das et all., 2011 Jones et al., 1941). Whereas, illuminance considers all possible reflections available in a specific place and calculates the brightness value accordingly. Luminance measures the brightness value of the reflected lights from an object. Light intensity can be measured in terms of illuminance and luminance. While there are sensors that measure light intensity, apple smartphone products do not have an application programming interface for the light sensor, meaning that more creative methods that utilize other sensors to measure light are needed (Ctein, 1997).Īdditionally, measuring light for accessibility purposes also poses some unique challenges. Unfortunately evaluating lighting can be very complex. Using the same principals we sought to develop a smartphone based light meter. Using these sensors, the team has successfully measured other building features such as font size & distance (Jahangir, Majumder, Ahamed, & and Smith, 2013). Modern smartphone come with many sensors. As part of the Access Ratings for Buildings (AR-B) project, the R&D team has worked to make environmental accessibility evaluation tools more available by duplicating common tools using a smartphone, thereby enabling more people to participate in the building evaluation process (Schwartz, 2013) Light meters are a bulky and expensive piece of equipment ranging from about 30 to well over 300 dollars, making it more difficult for a typical practitioner to have a light meter on hand. Because lighting levels have such a profound impact on persons with disabilities, a lighting level assessment is essential during a environmental accessibility evaluation.Ĭurrently, to conduct a lighting assessment, the evaluator must use a physical light meter (Young, 2012). Specifically, poor lighting is associated with falls, depression, poor visual performance, decreased performance in daily activities, and a lower quality of life (Bakker et al., 2004 Brunnstrom et al., 2004). Persons with disabilities, particularly those with vision loss, are significantly affected by the lighting levels of the environments around them. When implemented into an app, this algorithm improved the accuracy of our app based light meter compared other apps in the iTunes store, but was less accurate than current light meters. T he camera features, exposure time, ISO value and camera brightness attribute value to be significant predictors of observed lighting levels. The purpose of this paper is to describe the development of the app based light meter, Access Light, and the initial reliability testing.Īs direct measurement of light using an iPhone is not possible, we developed and implemented a novel method of measuring light using proxy measures. Therefore, we sought to create a smart phone based light meter to fill this disparity. Unfortunately, many evaluators do not have access to a light meter due to the bulk and cost. Adequate environmental lighting is essential for healthy productive living for people with disabilities, making lighting assessment a fundamental component of environmental accessibility evaluations.
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