Using Cultural Algorithms to Improve Wearable Device Gesture Recognition Performance
Faisal Waris, Robert G. Reynolds · 2015
Wearable computing devices are now mainstream. Many such devices have capable MEMS sensors that can be exploited for recognizing dynamic, in-the-air gestures. Noting the somewhat limited compute and battery life of today's devices, we present a computationally efficient approach to gesture recognition that can be effectively used inside an app running on standard, off-the-shelf hardware, such as an Android Smart watch. Our approach has two phases. The first phase is to construct finite state machines (FSM) for gesture recognition. We present a novel approach - which leverages techniques from functional programming languages - to define rich yet compact FSM. Such FSM can be further tuned for higher accuracy with help of some training data and a suitable optimization method - this is the second phase. To demonstrate effectiveness, we created a gesture recognition system for an automotive scenario as an Android Smart watch app and then tuned the gesture recognition engine using Cultural Algorithms optimization and training data. We achieved 77% gesture recognition accuracy which is on par which more computationally intensive techniques such as Hidden Markov Models.