# Toward Pioneering Sensors and Features Using Large Language Models in Human Activity Recognition

arXiv: https://arxiv.org/abs/2306.16017 · HTML: https://mlsanity.com/paper/2306.16017/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2023.

## Built on by (most cited first, 3 of 3)

- [A Survey on Generative AI and LLM for Video Generation, Understanding, and Streaming](https://mlsanity.com/paper/2404.16038/) (2024)
- [Using Large Language Models to Compare Explainable Models for Smart Home Human Activity Recognition](https://mlsanity.com/paper/2408.06352/) (2024)
- [ContextGPT: Infusing LLMs Knowledge into Neuro-Symbolic Activity Recognition Models](https://mlsanity.com/paper/2403.06586/) (2024)

## Abstract (arXiv)

In this paper, we propose a feature pioneering method using Large Language Models (LLMs). In the proposed method, we use Chat-GPT 1 to find new sensor locations and new features. Then we evaluate the machine learning model which uses the found features using Opportunity Dataset [ 4 , 9]. In current machine learning, humans make features, for this engineers visit real sites and have discussions with experts and veteran workers. However, this method has the problem that the quality of the features depends on the engineer. In order to solve this problem, we propose a way to make new features using LLMs. As a result, we obtain almost the same level of accuracy as the proposed model which used fewer sensors and the model uses all sensors in the dataset. This indicates that the proposed method is able to extract important features efficiently.