Smartphone-Based Light and Sound Intensity Calculation Application For Accessibility Measurement Nadiyah Johnson 1, Piyush Saxena 1, Drew Williams 1, Ola Claire Bangole 1, Md. Kamrul Hasan 1, Sheikh Iqbal Ahamed 1, and Roger O. Smith 2, Dennis Tomashek 2 Marquette University 1, University of Wisconsin – Milwaukee 2 Smartphone-Based Light and Sound Intensity Calculation Application For Accessibility Measurement Nadiyah Johnson 1, Piyush Saxena 1, Drew Williams 1, Ola Claire Bangole 1, Md. Kamrul Hasan 1, Sheikh Iqbal Ahamed 1, and Roger O. Smith 2, Dennis Tomashek 2 Marquette University 1, University of Wisconsin – Milwaukee 2 Introduction Methods References Contact InformationAcknowledgements The AR-B Project is supported in part by the U.S. Department of Education, National Institute on Disability and Rehabilitation Research (NIDRR), grant number H133G The opinions contained in this poster do not necessarily represent the policy of the U.S. Department of Education, and you should not assume endorsement by the Federal Government. Marquette University Nadiyah Johnson: Sheikh I. Ahamed: Claire Bangole : University of Wisconsin-Milwaukee Roger O. Smith: In 2010, the United States Census determined that over 56 million people in the United States have a disability. This amounts to a little over 18% of the population [1]. Visual and Auditory impairments Affected by the quality of lighting and sound Leads to decreased quality of life Existing Tools Other smartphone applications Luxi[2] and Lumu[3] Sound Meter – Smart Tools co and Mobile Essentials Industry sound and light meters Bulky, expensive, and require industry training[4] We present usable and inexpensive solutions for determining sound and light in a room. Our Approach Results Conclusion We developed smartphone applications (AccessSound & AccessLight) which emulate the functionality of industry sound and light meters using iOS built-in hardware. Results provided to the user are derived using a novel algorithm. These mobile applications require no industry training and are accessible from anywhere. [1] M. Brault, “Americans with Disabilities: 2010,” U.S. Census Bureau, Report P70-131, July [2] "Extrasensory Devices," [Online]. Available: [Accessed January 2015]. [3] "Lumu," [Online]. Available: [Accessed January 2015]. [4] Anson, Denis. (2008). ADA-CAT: Making Accessibility Accessible. ADA-CAT: Making Accessibility Accessible. [Web]. Retrieved 13 Jan. 2014, from User selects the type of environment App uses the front microphone to collect the sound data from the environment. Based on sound level selected by the user(quiet, moderate, loud) the decibel value is multiplied by a percentage value. This project has the potential to greatly improve the quality of life of individuals with disabilities. AccessSound and AccessLight are mobile applications that inform the user of the accessibility of their current location by providing useful sound and light measurements. The novel algorithms used to produce the results have been proven to be accurate compared to industry light and sound tools. Both applications have an average percent error within 5% of industry standard meters. These tools can be used as stand alone measurement instruments. They are intended to be integrated into other apps, such as AccessTools for rating public building accessibility and the HESTIA home assessment app. Future development of the algorithm is in progress to improve accuracy over the entire range of sound and light measurement. AccessSound AccessLight User selects the type of environment The algorithm uses the method continuous auto exposure which is provided by iOS API to conjunctly use speed, aperture, ISO Multiple images are sampled and Lux Value is calculated (112*pow(1.92,BrightnessValue)/(0.0929/ExposureTime)); AccessSoundAccessLight Figure 5: Graph comparing accuracy of AccessLight app to industry standard Light Meter Figure 4: Graph comparing accuracy of AccessSound app to industry standard Sound Meter Figure 2: AccessSound screens Figure 3: AccessLight screens Figure 1: AccessSound and AccessLight workflow chart