# LLM-Guided Exemplar Selection for Few-Shot Wearable-Sensor Human Activity Recognition

arXiv: https://arxiv.org/abs/2512.22385 · HTML: https://mlsanity.com/paper/2512.22385/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 26 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [Negative Margin Matters: Understanding Margin in Few-shot Classification](https://mlsanity.com/paper/2003.12060/) (2020)
- [A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks](https://mlsanity.com/paper/2011.01844/) (2020)
- [Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision](https://mlsanity.com/paper/1901.01151/) (2019)
- [Human Activity Recognition using Inertial, Physiological and Environmental Sensors: a Comprehensive Survey](https://mlsanity.com/paper/2004.08821/) (2020)
- [Are Large Language Models the New Interface for Data Pipelines?](https://mlsanity.com/paper/2406.06596/) (2024)
- [Large Language Models for Wearable Sensor-Based Human Activity Recognition, Health Monitoring, and Behavioral Modeling: A Survey of Early Trends, Datasets, and Challenges](https://mlsanity.com/paper/2407.07196/) (2024)
- [Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition](https://mlsanity.com/paper/2310.14390/) (2023)
- [A Weighted K-Center Algorithm for Data Subset Selection](https://mlsanity.com/paper/2312.10602/) (2023)
- [Automatic Combination of Sample Selection Strategies for Few-Shot Learning](https://mlsanity.com/paper/2402.03038/) (2024)
- [Few-Shot Optimization for Sensor Data Using Large Language Models: A Case Study on Fatigue Detection](https://mlsanity.com/paper/2505.18754/) (2025)

## Built on by (most cited first, 1 of 1)

- [TRACE: Temporal Reasoning over Context and Evidence for Activity Recognition in Smart Homes](https://mlsanity.com/paper/2605.02841/) (2026)

## Abstract (arXiv)

In this paper, we propose an LLM-Guided Exemplar Selection framework to address a key limitation in state-of-the-art Human Activity Recognition (HAR) methods: their reliance on large labeled datasets and purely geometric exemplar selection, which often fail to distinguish similar wearable sensor activities such as walking, walking upstairs, and walking downstairs. Our method incorporates semantic reasoning via an LLM-generated knowledge prior that captures feature importance, inter-class confusability, and exemplar budget multipliers, and uses it to guide exemplar scoring and selection. These priors are combined with margin-based validation cues, PageRank centrality, hubness penalization, and facility-location optimization to obtain a compact and informative set of exemplars. Evaluated on the UCI-HAR dataset under strict few-shot conditions, the framework achieves a macro F1-score of 88.78%, outperforming classical approaches such as random sampling, herding, and k-center. The results show that LLM-derived semantic priors, when integrated with structural and geometric cues, provide a stronger foundation for selecting representative sensor exemplars in few-shot wearable-sensor HAR.