Recognizing patterns in noisy data using trainable 'Functional' State Machines

Abstract

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) that are computationally as efficient as FSM but can handle probabilistic patterns almost as well as more elaborate techniques such as Hidden Markov Models.

A real-world application of FnSM is described; an Android smartwatch app that recognizes gestures performed using wrist and forearm movements. Also described is the use of an evolutionary computing algorithm to optimize the performance of the FnSM by selecting better parameter values for the various state-transition decisions.

We are increasingly living in a word where data processing needs to happen in real-time to enable quicker decision making. FSM are an essential technique for recognizing patterns over sequential data. This talk describes techniques that leverage FP to succinctly define complex FSM. Additionally, such constructed FSM can be trained with the help of a suitable evolutionary computing algorithm to also handle noisy data.

Faisal Waris

Faisal Waris has worked in Detroit's automotive industry for the past 15 years, in a variety of IT roles. He is currently pursuing a PhD in Computer Science at Wayne State University. He primarily uses F# as his everyday functional programming language but also delves into Spark/Scala for data science work.