Strategies and Challenges of Efficient White-Box Training for Human Activity Recognition

TL;DR

This paper proposes a white-box, human-in-the-loop framework for Human Activity Recognition that enhances interpretability, trust, and efficiency by integrating user interaction and language model assistance.

Contribution

It introduces a novel human-in-the-loop approach combined with LLM assistance to improve transparency and performance in wearable sensor-based activity recognition.

Findings

Enhanced interpretability through white-box models

Improved model performance via iterative user feedback

Effective guidance using Large Language Models

Abstract

Human Activity Recognition using time-series data from wearable sensors poses unique challenges due to complex temporal dependencies, sensor noise, placement variability, and diverse human behaviors. These factors, combined with the nontransparent nature of black-box Machine Learning models impede interpretability and hinder human comprehension of model behavior. This paper addresses these challenges by exploring strategies to enhance interpretability through white-box approaches, which provide actionable insights into latent space dynamics and model behavior during training. By leveraging human intuition and expertise, the proposed framework improves explainability, fosters trust, and promotes transparent Human Activity Recognition systems. A key contribution is the proposal of a Human-in-the-Loop framework that enables dynamic user interaction with models, facilitating iterative refinements to enhance performance and efficiency. Additionally, we investigate the usefulness of Large Language Model as an assistance to provide users with guidance for interpreting visualizations, diagnosing issues, and optimizing workflows. Together, these contributions present a scalable and efficient framework for developing interpretable and accessible Human Activity Recognition systems.