Abstract

Gesture recognition is becoming increasingly popular as an input mechanism for human-computer interfaces. The availability of MEMS (Micro-Electromechanical System) 3-axis linear accelerometers allows for the design of an inexpensive mobile gesture recognition system. Wearable inertial sensors are a low-cost, low-power solution to recognize gestures and, more generally, track the movements of a person. Gesture recognition algorithms have traditionally only been implemented in cases where ample system resources are available, i.e. on desktop computers with fast processors and large amounts of memory. In the cases where a gesture recognition algorithm has been implemented on a resource-constrained device, only the simplest algorithms were imple-mented to recognize only a small set of gestures. Current gesture recognition technology can be improved by making algorithms faster, i more robust, and more accurate. The most dramatic results in optimization are obtained by completely changing an algorithm to decrease the number of computations. Algorithms can also be optimized by profiling or timing the different sections of the algorithm to iden-