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Finite state machines (FSM) are a common technique for recognizing patterns over streams of data. However basic FSM cannot easily handle noisy data such as sensor data from a wearable device that is generated using human limb movement. This session presents a novel variation of FSM using techniques from functional programming to construct 'functional' state machines (FnSM).
In this talk, you'll see real-world application of - an Android smartwatch app that recognizes gestures performed using wrist and forearm movements. I will also describe how we used evolutionary computing algorithms to optimize the performance of the FnSM by selecting better parameter values for the various state-transition decisions.
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