Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 11; p. 3324
• Main Authors: Ronando, Elsen, Inoue, Sozo
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.06.2025; MDPI
• Subjects: accelerometer; Accelerometers; Analysis; Case studies; example selection; Fatigue; fatigue detection; few-shot prompting; Graph representations; Language; Large language models; large language models (LLMs); Machine learning; Methods; Optimization; Physiological aspects; Rankings; Semantics; sensor data; Sensors; United States; California

In this paper, we propose a novel few-shot optimization with Hybrid Euclidean Distance with Large Language Models (HED-LM) to improve example selection for sensor-based classification tasks. While few-shot prompting enables efficient inference with limited labeled data, its performance largely depen...