Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification

Abstract

FUSE-MET addresses critical challenges in deploying human activity recognition (HAR) systems in uncontrolled environments by effectively managing noisy labels, sparse data, and undefined activity vocabularies. By integrating BERT-based word embeddings with domain-specific knowledge (i.e., MET values), FUSE-MET optimizes label merging, reducing label complexity and improving classification accuracy. Our approach outperforms the state-of-the-art techniques, including ChatGPT-4, by balancing semantic meaning and physical intensity.

Shovito Barua Soumma

Graduate Research Associate

I am a Ph.D. candidate at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Abdullah Mamun

Graduate Research Associate

I am a Ph.D. student at Arizona State University. I work as a Graduate Research Associate at Embedded Machine Intelligence Lab (EMIL) under the supervision of Dr. Hassan Ghasemzadeh.

Hassan Ghasemzadeh

Director

Hassan Ghasemzadeh is a Professor of Biomedical Informatics at Arizona State University (ASU).