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
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25113324

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 depends on the quality of selected examples. HED-LM addresses this challenge through a hybrid selection pipeline that filters candidate examples based on Euclidean distance and re-ranks them using contextual relevance scored by large language models (LLMs). To validate its effectiveness, we apply HED-LM to a fatigue detection task using accelerometer data characterized by overlapping patterns and high inter-subject variability. Unlike simpler tasks such as activity recognition, fatigue detection demands more nuanced example selection due to subtle differences in physiological signals. Our experiments show that HED-LM achieves a mean macro F1-score of 69.13 ± 10.71%, outperforming both random selection (59.30 ± 10.13%) and distance-only filtering (67.61 ± 11.39%). These represent relative improvements of 16.6% and 2.3%, respectively. The results confirm that combining numerical similarity with contextual relevance improves the robustness of few-shot prompting. Overall, HED-LM offers a practical solution to improve performance in real-world sensor-based learning tasks and shows potential for broader applications in healthcare monitoring, human activity recognition, and industrial safety scenarios.