16: Developing NLP AND Supervised Machine Learning Techniques to Classify Mars Tasks

Music City Center 

As NASA's Human Research Program (HRP) prepares for long-duration Mars missions, understanding astronaut tasks is crucial. This study, conducted at NASA Glenn Research Center (GRC), applied Natural Language Processing (NLP) and machine learning to classify 1,058 Mars tasks into 18 Human System Task Categories (HSTCs) [1]. We developed an NLP model using Google's BERT [2] to capture semantic and syntactic nuances. Supervised training on a subset of tasks improved classification accuracy for 9 of 18 HSTCs, especially when incorporating HSTC descriptions. To address severe class imbalance, we introduced weighting and sampling techniques for data augmentation [3]. Additionally, we fine-tuned BERT to implement a pairwise relatedness scoring method, enabling task clustering and progressing toward unsupervised labeling. This presentation covers data preprocessing, key syntax extraction using BERT, and supervised classification, highlighting NLP's potential for analyzing Mars mission tasks in crew health and performance studies.

Natural Language Processing

Machine Learning

BERT (Bidirectional Encoder Representations from Transformers)


NASA Human Research Program (HRP)

Mars Task Classification

Supervised Learning 

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