The feasibility of using gesture mapping was checked first. Sign languages use a lot of different parameters while communicating, which would create a lot of parameters and sensor data to be processed. The amount of data to be processed was becoming so much that low cost microprocessors wouldn’t be able to process that much seamlessly, and was significantly increasing the cost of the device. ( 30+ locations were to be mapped at once ). I wanted to use flex sensors initially, because they are easier to integrate into wearable electronics, however, since accurate character reproduction is required, plotting and tracking movements on the X-Y-Z axes require powerful processors that further increase the cost of the project. A system with flex sensors and powerful enough processors would cost over a 5000 Rupees, well above what the target demographic would have access to, or would be willing to spend. Flexsensors also create a lot of sensor noise, that would have reduced the accuracy rate of the device. Tactile momentary switches are much better because they are much cheaper, easily replaceable and extremely easy to work with producing predictable results. ( Debounce time of 20ms ) While coding, the usage of the serial functions would seem like the easier option however it would result in the display of the results on a separate terminal window, which is highly inconvenient for any text to speech engine to read. The keyboard library functions were used instead because it allows HID device functionality, solving a lot of problems at once. Arduinos can also be attached with certain shields that make them capable of text to speech capabilities, however it isn’t feasible because it requires preprogramed text strings to be sent, and it would be impractical too as the shield cost almost the double the price of most lower end smartphones. The device is able to log 60- 120 keystrokes per minute. Although this is fast for a AAC, it still isn’t as fast as normal speech. If required, the user can use third party text prediction services like SwiftKey to engage auto correction, hence vastly increasing the speed, including being able to type words by just a single keystroke. The code for the Arduino is written in such a way that the software detects it’s a physical keyboard, and hence, is compatible with most auto correct engines. The system was also checked with a third party keyboard software that detected it as a hardware keyboard (SwiftKey) and it was found that the system used 67.82% of the total time without it, which is a substantial saving in terms of time if the person is using it for daily usage. A bluetooth or Wi-Fi based data transfer system wasn’t used because it requires an additional power source whereas using an USB OTG cable network allows the charge to be drawn directly from the phone battery. This also removes problems like restrictions caused due to range of the connections.