Interpretable Machine Learning Models for Human Action and Emotion Deciphering

Abstract

Interpretable machine learning models have gained significant attention in recent years due to their ability to provide insights into complex decision-making processes. This chapter explores the application of interpretable machine-learning models for deciphering human actions and emotions. The authors propose a novel framework that combines state-of-the-art machine learning techniques with interpretable model architectures to enhance our understanding of human behaviour. A comprehensive literature review highlights the significance of interpretable models in various domains, such as healthcare, psychology, and human-computer interaction. They present a detailed methodology for data collection, preprocessing, feature extraction, and model training. The results demonstrate the effectiveness of interpretable models in accurately classifying and explaining human actions and emotions. They provide insightful discussions on the implications of the findings, identify limitations, and propose avenues for future research. Overall, this research contributes to developing interpretable machine-learning models that can enhance our understanding of human behaviour and emotions.