NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness

Sensors
Harsh V P Singh, Qusay H Mahmoud

Abstract

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for n - a h e a d time-step window given k - l a g g e d past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence (seq2seq) deep-learning machine translation algorithms is presented. In addition, a custom Seq2Seq convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher ( ≈ 26 % ) forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher ( ≈ 53 % ) validation accuracy indicativ...Continue Reading

References

Mar 9, 2006·Journal of Radiological Protection : Official Journal of the Society for Radiological Protection·G A M WebbM J S Gaffney
Jan 18, 2018·JMIR Research Protocols·Floor SieverinkLisette van Gemert-Pijnen
Apr 26, 2020·Accident; Analysis and Prevention·Guofa LiYuezhi Li

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Methods Mentioned

BETA
seq2seq

Software Mentioned

Python
Manoeuvring Characteristics Augmentation System ( MCAS )
word2vec
GloVe
Trident
OuS
SNAIL ( Neural Attentive MetaLearner )
EncDec
LSTM
Keras

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