Americans With Disabilities Act – Compliance Assessment Toolkit on Smartphone Mohammad Tanviruzzaman ∗ , Rizwana Rizia ∗ , Sheikh Iqbal Ahamed ∗ , and Roger Smith † ∗ Department of Mathematics, Statistics, and Computer Science Marquette University, Milwaukee, Wisconsin 53233 Emails: {mtanviru, rrizia, iq}@mscs.mu.edu † Department of Occupational Science & Technology University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53201–0413 Email: smithro@uwm.edu Abstract—The Americans With Disabilities Act (ADA) and other accessibility standards specify the requirements for assessing the architectural barriers for the built environment. And, the ADA – Compliance Assessment Toolkit (ADACAT) provides simple pass-fail assessments of those requirements. Measurement Kit is the principal component of the ADACAT, which consists of various measuring instruments like, measur- ing tape, sound-level meter, light-level meter, magic slope block, etc. In this paper, we show that most of the instruments in the Measurement Kit can be effectively implemented as a software application on a smartphone equipped with sensors like, accelerometer, gyroscope, and microphone. Detailed algorithms have been presented and the experimental results show that an iPhone or iPod Touch 4.0 application can serve as measuring tape, sound-level meter, as well as slope meter, making the ADACAT both cheaper and more portable. Keywords-allan variance; americans with disabilities act; a-weighting; dead reckoning; distance; inertial navigation; kalman filter; measurement; slope; smartphone; sound-level I. I NTRODUCTION Over 50 million Americans have some kind of physical, sensory, cognitive, or mental disability [1]. The Americans With Disabilities Act (ADA) of 1990 sets the minimum requirements (minimum door width, staircase slope, etc.) for state and local government facilities, public accommo- dations, and commercial facilities to be readily accessible to and usable by individuals with disabilities with a view to eliminate architectural barriers for these people [1, 22]. The Americans With Disabilities Act – Compliance Assessment Toolkit (ADACAT) is a screening tool-kit, which allows individuals without advanced training to assess the archi- tectural barriers of the built environment. It consists of two parts: Audit and Measurement Kit. Each audit is essentially a web-based questionnaire concentrating on the legal require- ments of the ADA. The answers to the questions in an audit come from the actual measurements performed using the Measurement Kit. After answering the questions in an audit, the person, who is performing the accessibility-assessment can click the “Score Audit” button to receive a numerical score of the degree of accessibility and usability compliance of say, a room [23]. The Measurement Kit consists of a measuring tape, a sound-level meter, a light-level meter, a door force tool, the story stick, the magic slope block, the font guide, the multi-tool, and the key force tool. In this paper, our focus is limited to implementing the measuring tape, the magic slope block, and the sound-level meter of the Measurement Kit as a smartphone application. The measuring tape is used to measure width of hallways and doors, height of signages, barriers, and clearances under desks and tables. A digital sound-level meter enables pass-fail assessments of the environmental sound-level with sufficient precision. The magic slope block enables one to measure slopes of ramps and paths, and cross-slopes of both [24]. In this paper we show that a smartphone equipped with accelerometer, gyroscope, and microphone can adequately (having acceptable accuracy) serve as a measuring tape, sound-level meter, and slope meter. In order to measure length or height (measuring tape) the accelerometer of the smartphone is used to perform dead reckoning based inertial navigation [2]. The basic idea is to integrate ac- celeration twice to get the position of the phone and the distance (length or height) is the difference between the final and the initial positions of the smartphone. The Microelectromechanicalsystem (MEMS) type accelerometer used in a smartphone is susceptible to various deterministic and stochastic errors. Two consecutive integrations at each time instant cause any small error to grow large very quickly [6]. The deterministic errors include, bias, scale-factor, nonorthogonality, etc., whereas the stochastic errors include, random walk, Gauss-Markov process, etc. [3]. We apply the 6-position static test to determine the principal deterministic errors (bias, scale-factor, and non-orthogonality), and then we use this information to calibrate the sensor-data, before applying stochastic error removal techniques [4]. In order to reduce the stochastic errors, we use Allan Variance technique for determining velocity random walk error coefficients of the accelerometer (data) and then we feed those coefficients into a Kalman filter along with the calibrated data [5, 6]. The smartphone’s microphone is used to record audio data, and then the environmental sound-level is determined from the Fast Fourier Transform (FFT) coeffcients cal-