  Wearable computing is one of the fastest growing technologies today. Smart
watches are poised to take over at least of half the wearable devices market in
the near future. Smart watch screen size, however, is a limiting factor for
growth, as it restricts practical text input. On the other hand, wearable
devices have some features, such as consistent user interaction and hands-free,
heads-up operations, which pave the way for gesture recognition methods of text
entry. This paper proposes a new text input method for smart watches, which
utilizes motion sensor data and machine learning approaches to detect letters
written in the air by a user. This method is less computationally intensive and
less expensive when compared to computer vision approaches. It is also not
affected by lighting factors, which limit computer vision solutions. The
AirDraw system prototype developed to test this approach is presented.
Additionally, experimental results close to 71% accuracy are presented.
