TY - GEN
T1 - Domain-Informed Label Fusion Surpasses LLMs in Free-Living Activity Classification
AU - Soumma, Shovito Barua
AU - Mamun, Abdullah
AU - Ghasemzadeh, Hassan
N1 - Publisher Copyright: Copyright © 2025, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2025/4/11
Y1 - 2025/4/11
N2 - 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 GPT-4, by balancing semantic meaning and physical intensity.
AB - 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 GPT-4, by balancing semantic meaning and physical intensity.
UR - https://www.scopus.com/pages/publications/105003907860
UR - https://www.scopus.com/pages/publications/105003907860#tab=citedBy
U2 - 10.1609/aaai.v39i28.35301
DO - 10.1609/aaai.v39i28.35301
M3 - Conference contribution
T3 - Proceedings of the AAAI Conference on Artificial Intelligence
SP - 29495
EP - 29497
BT - Special Track on AI Alignment
A2 - Walsh, Toby
A2 - Shah, Julie
A2 - Kolter, Zico
PB - Association for the Advancement of Artificial Intelligence
T2 - 39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Y2 - 25 February 2025 through 4 March 2025
ER -